Merge remote-tracking branch 'something/feature/merge-build-main' into test-branch-some-work

This reverts commit 3e1469a55a.

cli.py

@@ -0,0 +1,7 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+from cli import app
+
+if __name__ == "__main__":
+    app()

cli/__init__.py

@@ -0,0 +1,22 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import typer
+
+from cli.commands.train import train
+from cli.commands.inference import inference
+from cli.commands.export import export, list_checkpoints
+from cli.commands.ui import ui
+from cli.commands.studio import studio_app
+
+app = typer.Typer(
+    help = "Command-line interface for Unsloth training, inference, and export.",
+    context_settings = {"help_option_names": ["-h", "--help"]},
+)
+
+app.command()(train)
+app.command()(inference)
+app.command()(export)
+app.command("list-checkpoints")(list_checkpoints)
+app.command()(ui)
+app.add_typer(studio_app, name = "studio", help = "Unsloth Studio commands.")

cli/commands/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

cli/commands/export.py

@@ -0,0 +1,132 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+
+EXPORT_FORMATS = ["merged-16bit", "merged-4bit", "gguf", "lora"]
+GGUF_QUANTS = ["q4_k_m", "q5_k_m", "q8_0", "f16"]
+
+
+def list_checkpoints(
+    outputs_dir: Path = typer.Option(
+        Path("./outputs"), "--outputs-dir", help = "Directory that holds training runs."
+    ),
+):
+    """List checkpoints detected in the outputs directory."""
+    from studio.backend.core.export import ExportBackend
+
+    backend = ExportBackend()
+    checkpoints = backend.scan_checkpoints(outputs_dir = str(outputs_dir))
+    if not checkpoints:
+        typer.echo("No checkpoints found.")
+        raise typer.Exit()
+
+    for model_name, ckpt_list, metadata in checkpoints:
+        typer.echo(f"\n{model_name}:")
+        for display, path, loss in ckpt_list:
+            loss_str = f" (loss: {loss:.4f})" if loss is not None else ""
+            typer.echo(f"  {display}{loss_str}: {path}")
+
+
+def export(
+    checkpoint: Path = typer.Argument(..., help = "Path to checkpoint directory."),
+    output_dir: Path = typer.Argument(..., help = "Directory to save exported model."),
+    format: str = typer.Option(
+        "merged-16bit",
+        "--format",
+        "-f",
+        help = f"Export format: {', '.join(EXPORT_FORMATS)}",
+    ),
+    quantization: str = typer.Option(
+        "q4_k_m",
+        "--quantization",
+        "-q",
+        help = f"GGUF quantization method: {', '.join(GGUF_QUANTS)}",
+    ),
+    push_to_hub: bool = typer.Option(
+        False, "--push-to-hub", help = "Push exported model to HuggingFace Hub."
+    ),
+    repo_id: Optional[str] = typer.Option(
+        None, "--repo-id", help = "HuggingFace repo ID (username/model-name)."
+    ),
+    hf_token: Optional[str] = typer.Option(
+        None, "--hf-token", envvar = "HF_TOKEN", help = "HuggingFace token."
+    ),
+    private: bool = typer.Option(
+        False, "--private", help = "Make the HuggingFace repo private."
+    ),
+    max_seq_length: int = typer.Option(2048, "--max-seq-length"),
+    load_in_4bit: bool = typer.Option(True, "--load-in-4bit/--no-load-in-4bit"),
+):
+    """Export a checkpoint to various formats (merged, GGUF, LoRA adapter)."""
+    if format not in EXPORT_FORMATS:
+        typer.echo(
+            f"Error: Invalid format '{format}'. Choose from: {', '.join(EXPORT_FORMATS)}",
+            err = True,
+        )
+        raise typer.Exit(code = 2)
+
+    if push_to_hub and not repo_id:
+        typer.echo("Error: --repo-id required when using --push-to-hub", err = True)
+        raise typer.Exit(code = 2)
+
+    from studio.backend.core.export import ExportBackend
+
+    backend = ExportBackend()
+
+    typer.echo(f"Loading checkpoint: {checkpoint}")
+    success, message = backend.load_checkpoint(
+        checkpoint_path = str(checkpoint),
+        max_seq_length = max_seq_length,
+        load_in_4bit = load_in_4bit,
+    )
+    if not success:
+        typer.echo(f"Error: {message}", err = True)
+        raise typer.Exit(code = 1)
+    typer.echo(message)
+
+    typer.echo(f"Exporting as {format}...")
+    if format == "merged-16bit":
+        success, message = backend.export_merged_model(
+            save_directory = str(output_dir),
+            format_type = "16-bit (FP16)",
+            push_to_hub = push_to_hub,
+            repo_id = repo_id,
+            hf_token = hf_token,
+            private = private,
+        )
+    elif format == "merged-4bit":
+        success, message = backend.export_merged_model(
+            save_directory = str(output_dir),
+            format_type = "4-bit (FP4)",
+            push_to_hub = push_to_hub,
+            repo_id = repo_id,
+            hf_token = hf_token,
+            private = private,
+        )
+    elif format == "gguf":
+        success, message = backend.export_gguf(
+            save_directory = str(output_dir),
+            quantization_method = quantization.upper(),
+            push_to_hub = push_to_hub,
+            repo_id = repo_id,
+            hf_token = hf_token,
+        )
+    elif format == "lora":
+        success, message = backend.export_lora_adapter(
+            save_directory = str(output_dir),
+            push_to_hub = push_to_hub,
+            repo_id = repo_id,
+            hf_token = hf_token,
+            private = private,
+        )
+
+    if not success:
+        typer.echo(f"Error: {message}", err = True)
+        raise typer.Exit(code = 1)
+
+    typer.echo(message)

cli/commands/inference.py

@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import sys
+from typing import Optional
+
+import typer
+
+
+def inference(
+    model: str = typer.Argument(..., help = "HF model id or local path."),
+    prompt: str = typer.Argument(..., help = "Prompt to send to the model."),
+    hf_token: Optional[str] = typer.Option(
+        None, "--hf-token", envvar = "HF_TOKEN", help = "Hugging Face token if needed."
+    ),
+    temperature: float = typer.Option(0.7, "--temperature"),
+    top_p: float = typer.Option(0.9, "--top-p"),
+    top_k: int = typer.Option(40, "--top-k"),
+    max_new_tokens: int = typer.Option(256, "--max-new-tokens"),
+    repetition_penalty: float = typer.Option(1.1, "--repetition-penalty"),
+    system_prompt: str = typer.Option(
+        "",
+        "--system-prompt",
+        help = "Optional system prompt to prepend.",
+    ),
+    max_seq_length: int = typer.Option(2048, "--max-seq-length"),
+    load_in_4bit: bool = typer.Option(True, "--load-in-4bit/--no-load-in-4bit"),
+):
+    """Run a single inference using the specified model."""
+    from studio.backend.core import ModelConfig, get_inference_backend
+
+    inference_backend = get_inference_backend()
+    model_config = ModelConfig.from_ui_selection(
+        dropdown_value = model, search_value = None, hf_token = hf_token, is_lora = False
+    )
+    if not model_config:
+        typer.echo("Could not resolve model config", err = True)
+        raise typer.Exit(code = 1)
+
+    if not inference_backend.load_model(
+        config = model_config,
+        max_seq_length = max_seq_length,
+        load_in_4bit = load_in_4bit,
+        hf_token = hf_token,
+    ):
+        typer.echo("Model load failed", err = True)
+        raise typer.Exit(code = 1)
+
+    messages = [{"role": "user", "content": prompt}]
+    stream = inference_backend.generate_chat_response(
+        messages = messages,
+        system_prompt = system_prompt,
+        temperature = temperature,
+        top_p = top_p,
+        top_k = top_k,
+        max_new_tokens = max_new_tokens,
+        repetition_penalty = repetition_penalty,
+    )
+
+    typer.echo("Assistant:", nl = True)
+    previous = ""
+    for chunk in stream:
+        delta = chunk[len(previous) :]
+        if delta:
+            sys.stdout.write(delta)
+            sys.stdout.flush()
+        previous = chunk
+    sys.stdout.write("\n")
+    sys.stdout.flush()

cli/commands/studio.py

@@ -0,0 +1,383 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import os
+import platform
+import subprocess
+import sys
+import time
+from pathlib import Path
+from typing import Optional
+import typer
+
+studio_app = typer.Typer(help = "Unsloth Studio commands.")
+
+STUDIO_HOME = Path.home() / ".unsloth" / "studio"
+
+# __file__ is cli/commands/studio.py — two parents up is the package root
+# (either site-packages or the repo root for editable installs).
+_PACKAGE_ROOT = Path(__file__).resolve().parent.parent.parent
+
+
+def _is_repo_root(path: Path) -> bool:
+    """Check if a directory looks like the repo root (actual git clone, not site-packages)."""
+    return (
+        (path / ".git").exists()
+        and (path / "pyproject.toml").is_file()
+        and (
+            (path / "studio" / "setup.sh").is_file()
+            or (path / "studio" / "setup.ps1").is_file()
+        )
+    )
+
+
+def _get_repo_root() -> Optional[Path]:
+    """Find the git clone repo root.
+
+    Used only by setup() — checks __file__ first (editable install),
+    then walks CWD parents (wheel install, user is inside the clone).
+    """
+    # Check 1: __file__ is in the repo (editable install)
+    if _is_repo_root(_PACKAGE_ROOT):
+        return _PACKAGE_ROOT
+    # Check 2: CWD or any parent is the repo
+    cwd = Path.cwd().resolve()
+    for parent in (cwd, *cwd.parents):
+        if _is_repo_root(parent):
+            return parent
+    return None
+
+
+def _studio_venv_python() -> Optional[Path]:
+    """Return the studio venv Python binary, or None if not set up."""
+    if platform.system() == "Windows":
+        p = STUDIO_HOME / ".venv" / "Scripts" / "python.exe"
+    else:
+        p = STUDIO_HOME / ".venv" / "bin" / "python"
+    return p if p.is_file() else None
+
+
+def _find_run_py() -> Optional[Path]:
+    """Find studio/backend/run.py.
+
+    No CWD dependency — works from any directory.
+    Since studio/ is now a proper package (has __init__.py), it lives in
+    site-packages after pip install, right next to cli/.
+    """
+    # 1. Relative to __file__ (site-packages or editable repo root)
+    run_py = _PACKAGE_ROOT / "studio" / "backend" / "run.py"
+    if run_py.is_file():
+        return run_py
+    # 2. Studio venv's site-packages (Linux + Windows layouts)
+    for pattern in (
+        "lib/python*/site-packages/studio/backend/run.py",
+        "Lib/site-packages/studio/backend/run.py",
+    ):
+        for match in (STUDIO_HOME / ".venv").glob(pattern):
+            return match
+    return None
+
+
+def _find_install_script() -> Optional[Path]:
+    """Find studio/install_python_stack.py.
+
+    No CWD dependency — works from any directory.
+    """
+    # 1. Relative to __file__ (site-packages or editable repo root)
+    s = _PACKAGE_ROOT / "studio" / "install_python_stack.py"
+    if s.is_file():
+        return s
+    # 2. Studio venv's site-packages
+    for pattern in (
+        "lib/python*/site-packages/studio/install_python_stack.py",
+        "Lib/site-packages/studio/install_python_stack.py",
+    ):
+        for match in (STUDIO_HOME / ".venv").glob(pattern):
+            return match
+    return None
+
+
+def _find_setup_script() -> Optional[Path]:
+    """Find studio/setup.sh or studio/setup.ps1.
+
+    No CWD dependency — works from any directory.
+    """
+    name = "setup.ps1" if platform.system() == "Windows" else "setup.sh"
+    # 1. Relative to __file__ (site-packages or editable repo root)
+    s = _PACKAGE_ROOT / "studio" / name
+    if s.is_file():
+        return s
+    # 2. Studio venv's site-packages
+    for pattern in (
+        f"lib/python*/site-packages/studio/{name}",
+        f"Lib/site-packages/studio/{name}",
+    ):
+        for match in (STUDIO_HOME / ".venv").glob(pattern):
+            return match
+    return None
+
+
+# ── unsloth studio (server) ──────────────────────────────────────────
+
+
+@studio_app.callback(invoke_without_command = True)
+def studio_default(
+    ctx: typer.Context,
+    port: int = typer.Option(8000, "--port", "-p"),
+    host: str = typer.Option("0.0.0.0", "--host", "-H"),
+    frontend: Optional[Path] = typer.Option(None, "--frontend", "-f"),
+    silent: bool = typer.Option(False, "--silent", "-q"),
+):
+    """Launch the Unsloth Studio server."""
+    if ctx.invoked_subcommand is not None:
+        return
+
+    # Always use the studio venv if it exists and we're not already in it
+    studio_venv_dir = STUDIO_HOME / ".venv"
+    in_studio_venv = sys.prefix.startswith(str(studio_venv_dir))
+
+    if not in_studio_venv:
+        studio_python = _studio_venv_python()
+        run_py = _find_run_py()
+        if studio_python and run_py:
+            if not silent:
+                typer.echo("Launching with studio venv...")
+            args = [
+                str(studio_python),
+                str(run_py),
+                "--host",
+                host,
+                "--port",
+                str(port),
+            ]
+            if frontend:
+                args.extend(["--frontend", str(frontend)])
+            if silent:
+                args.append("--silent")
+            os.execvp(str(studio_python), args)
+        else:
+            typer.echo("Studio not set up. Run 'unsloth studio setup' first.")
+            raise typer.Exit(1)
+
+    from studio.backend.run import run_server
+
+    if not silent:
+        from studio.backend.run import _resolve_external_ip
+
+        display_host = _resolve_external_ip() if host == "0.0.0.0" else host
+        typer.echo(f"Starting Unsloth Studio on http://{display_host}:{port}")
+
+    run_server(
+        host = host,
+        port = port,
+        frontend_path = frontend,
+        silent = silent,
+    )
+
+    try:
+        while True:
+            time.sleep(1)
+    except KeyboardInterrupt:
+        typer.echo("\nShutting down...")
+
+
+# ── unsloth studio setup ─────────────────────────────────────────────
+
+
+@studio_app.command()
+def setup():
+    """Run one-time Studio environment setup."""
+    # If we're inside a git clone, use the full setup script (builds frontend, etc.)
+    repo = _get_repo_root()
+    if repo:
+        _dev_setup(repo)
+    else:
+        _pip_setup()
+
+
+def _dev_setup(repo_root: Path):
+    """Git-clone: run setup.sh / setup.ps1."""
+    studio_dir = repo_root / "studio"
+    if platform.system() == "Windows":
+        script = studio_dir / "setup.ps1"
+        subprocess.run(
+            ["powershell", "-ExecutionPolicy", "Bypass", "-File", str(script)],
+            check = True,
+        )
+    else:
+        script = studio_dir / "setup.sh"
+        subprocess.run(["bash", str(script)], check = True)
+
+
+def _pip_setup():
+    """Pip-install: create studio venv, install all deps, build extras."""
+    import venv as _venv
+
+    venv_dir = STUDIO_HOME / ".venv"
+    venv_t5_dir = STUDIO_HOME / ".venv_t5"
+
+    if platform.system() == "Windows":
+        venv_python = venv_dir / "Scripts" / "python.exe"
+        venv_pip = venv_dir / "Scripts" / "pip.exe"
+    else:
+        venv_python = venv_dir / "bin" / "python"
+        venv_pip = venv_dir / "bin" / "pip"
+
+    typer.echo("Setting up Unsloth Studio...")
+
+    # 1. Create venv
+    if not venv_python.is_file():
+        typer.echo(f"  Creating venv at {venv_dir}...")
+        STUDIO_HOME.mkdir(parents = True, exist_ok = True)
+        _venv.create(str(venv_dir), with_pip = True)
+
+    # 2. Install all Python deps via install_python_stack.py
+    install_script = _find_install_script()
+    if install_script:
+        typer.echo("  Installing Python dependencies...")
+        subprocess.run([str(venv_python), str(install_script)], check = True)
+    else:
+        typer.echo("Error: Could not find install_python_stack.py")
+        raise typer.Exit(1)
+
+    # 3. Pre-install transformers 5.x overlay
+    if venv_t5_dir.is_dir() and any(venv_t5_dir.iterdir()):
+        typer.echo(f"  Transformers 5.x overlay already at {venv_t5_dir}")
+    else:
+        typer.echo("  Installing transformers 5.x overlay...")
+        venv_t5_dir.mkdir(parents = True, exist_ok = True)
+        subprocess.run(
+            [
+                str(venv_pip),
+                "install",
+                "--target",
+                str(venv_t5_dir),
+                "--no-deps",
+                "transformers==5.2.0",
+            ],
+            check = True,
+        )
+        subprocess.run(
+            [
+                str(venv_pip),
+                "install",
+                "--target",
+                str(venv_t5_dir),
+                "--no-deps",
+                "huggingface_hub==1.3.0",
+            ],
+            check = True,
+        )
+        typer.echo(f"  Installed to {venv_t5_dir}")
+
+    # 4. Build llama.cpp
+    _build_llama_cpp()
+
+    typer.echo("")
+    typer.echo("Setup complete! Run 'unsloth studio' to start.")
+
+
+def _build_llama_cpp():
+    """Build llama.cpp at ~/.unsloth/llama.cpp/."""
+    import shutil
+
+    unsloth_home = Path.home() / ".unsloth"
+    llama_dir = unsloth_home / "llama.cpp"
+
+    if not shutil.which("cmake"):
+        typer.echo("  cmake not found — skipping llama.cpp build")
+        return
+    if not shutil.which("git"):
+        typer.echo("  git not found — skipping llama.cpp build")
+        return
+
+    typer.echo("  Building llama.cpp for GGUF inference...")
+
+    if llama_dir.exists():
+        # necessary because shutil.rmtree fails on Windows because .git pack files are read-only
+        def _force_remove_readonly(func, path, exc_info):
+            """Clear read-only flag and retry — needed on Windows for .git pack files."""
+            import stat
+            os.chmod(path, stat.S_IWRITE)
+            func(path)
+        shutil.rmtree(llama_dir, onerror = _force_remove_readonly)
+    unsloth_home.mkdir(parents = True, exist_ok = True)
+
+    result = subprocess.run(
+        [
+            "git",
+            "clone",
+            "--depth",
+            "1",
+            "https://github.com/ggml-org/llama.cpp.git",
+            str(llama_dir),
+        ],
+        stdout = subprocess.PIPE,
+        stderr = subprocess.STDOUT,
+    )
+    if result.returncode != 0:
+        typer.echo("  Failed to clone llama.cpp")
+        return
+
+    cmake_args = []
+    nvcc_path = shutil.which("nvcc")
+    if not nvcc_path and Path("/usr/local/cuda/bin/nvcc").is_file():
+        nvcc_path = "/usr/local/cuda/bin/nvcc"
+    if nvcc_path:
+        typer.echo(f"  Building with CUDA (nvcc: {nvcc_path})...")
+        cmake_args.append("-DGGML_CUDA=ON")
+    else:
+        typer.echo("  Building CPU-only...")
+
+    build_dir = llama_dir / "build"
+    result = subprocess.run(
+        ["cmake", "-S", str(llama_dir), "-B", str(build_dir)] + cmake_args,
+        stdout = subprocess.PIPE,
+        stderr = subprocess.STDOUT,
+    )
+    if result.returncode != 0:
+        typer.echo("  cmake configure failed")
+        return
+
+    ncpu = str(os.cpu_count() or 4)
+    result = subprocess.run(
+        [
+            "cmake",
+            "--build",
+            str(build_dir),
+            "--config",
+            "Release",
+            "--target",
+            "llama-server",
+            f"-j{ncpu}",
+        ],
+        stdout = subprocess.PIPE,
+        stderr = subprocess.STDOUT,
+    )
+    if result.returncode != 0:
+        typer.echo("  llama-server build failed")
+        return
+
+    subprocess.run(
+        [
+            "cmake",
+            "--build",
+            str(build_dir),
+            "--config",
+            "Release",
+            "--target",
+            "llama-quantize",
+            f"-j{ncpu}",
+        ],
+        stdout = subprocess.PIPE,
+        stderr = subprocess.STDOUT,
+    )
+
+    if sys.platform == "win32":
+        server_bin = build_dir / "bin" / "Release" / "llama-server.exe"
+    else:
+        server_bin = build_dir / "bin" / "llama-server"
+    if server_bin.is_file():
+        typer.echo(f"  llama-server built at {server_bin}")
+    else:
+        typer.echo("  llama-server binary not found after build")

cli/commands/train.py

@@ -0,0 +1,144 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import time
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+from cli.config import Config, load_config
+from cli.options import add_options_from_config
+
+
+@add_options_from_config(Config)
+def train(
+    config: Optional[Path] = typer.Option(
+        None,
+        "--config",
+        "-c",
+        help = "Path to YAML/JSON config file. CLI flags override config values.",
+    ),
+    hf_token: Optional[str] = typer.Option(
+        None, "--hf-token", envvar = "HF_TOKEN", help = "Hugging Face token if needed."
+    ),
+    wandb_token: Optional[str] = typer.Option(
+        None, "--wandb-token", envvar = "WANDB_API_KEY", help = "Weights & Biases API key."
+    ),
+    dry_run: bool = typer.Option(
+        False,
+        "--dry-run",
+        help = "Show resolved config and exit without training.",
+    ),
+    config_overrides: dict = None,
+):
+    """Launch training using the existing Unsloth training backend."""
+    try:
+        cfg = load_config(config)
+    except FileNotFoundError as e:
+        typer.echo(f"Error: {e}", err = True)
+        raise typer.Exit(code = 2)
+
+    cfg.apply_overrides(**config_overrides)
+
+    # CLI/env tokens take precedence over config
+    # Handle case where typer.Option isn't resolved (decorator interaction)
+    from typer.models import OptionInfo
+
+    if isinstance(hf_token, OptionInfo):
+        hf_token = None
+    if isinstance(wandb_token, OptionInfo):
+        wandb_token = None
+    hf_token = hf_token or cfg.logging.hf_token
+    wandb_token = wandb_token or cfg.logging.wandb_token
+
+    if dry_run:
+        import yaml
+
+        data = cfg.model_dump()
+        data["training"]["output_dir"] = str(data["training"]["output_dir"])
+        typer.echo(yaml.dump(data, default_flow_style = False, sort_keys = False))
+        raise typer.Exit(code = 0)
+
+    if not cfg.model:
+        typer.echo("Error: provide --model or set model in --config", err = True)
+        raise typer.Exit(code = 2)
+
+    if not cfg.data.dataset and not cfg.data.local_dataset:
+        typer.echo(
+            "Error: provide --dataset or --local-dataset (or via --config)", err = True
+        )
+        raise typer.Exit(code = 2)
+
+    # Check if the model path is a LoRA adapter (has adapter_config.json)
+    model_path = Path(cfg.model) if cfg.model else None
+    model_is_lora = (
+        model_path
+        and model_path.is_dir()
+        and (model_path / "adapter_config.json").exists()
+    )
+    use_lora = cfg.training.training_type.lower() == "lora"
+
+    if model_is_lora and not use_lora:
+        typer.echo(
+            "Error: Cannot do full finetuning on a LoRA adapter. "
+            "Use --training-type lora or provide a base model.",
+            err = True,
+        )
+        raise typer.Exit(code = 2)
+
+    from studio.backend.core.training.trainer import UnslothTrainer
+
+    trainer = UnslothTrainer()
+
+    # Load model (trainer.is_vlm is set after this)
+    if not trainer.load_model(
+        model_name = cfg.model,
+        max_seq_length = cfg.training.max_seq_length,
+        load_in_4bit = cfg.training.load_in_4bit if use_lora else False,
+        hf_token = hf_token,
+    ):
+        typer.echo("Model load failed", err = True)
+        raise typer.Exit(code = 1)
+
+    is_vision = trainer.is_vlm
+
+    if not trainer.prepare_model_for_training(**cfg.model_kwargs(use_lora, is_vision)):
+        typer.echo("Model preparation failed", err = True)
+        raise typer.Exit(code = 1)
+
+    result = trainer.load_and_format_dataset(
+        dataset_source = cfg.data.dataset or "",
+        format_type = cfg.data.format_type,
+        local_datasets = cfg.data.local_dataset,
+    )
+    if result is None:
+        typer.echo("Dataset load failed", err = True)
+        raise typer.Exit(code = 1)
+
+    ds, eval_ds = result
+
+    training_kwargs = cfg.training_kwargs()
+    training_kwargs["wandb_token"] = wandb_token  # CLI/env takes precedence
+    started = trainer.start_training(
+        dataset = ds, eval_dataset = eval_ds, **training_kwargs
+    )
+
+    if not started:
+        typer.echo("Training failed to start", err = True)
+        raise typer.Exit(code = 1)
+
+    try:
+        while trainer.training_thread and trainer.training_thread.is_alive():
+            time.sleep(1)
+    except KeyboardInterrupt:
+        typer.echo("Stopping training (Ctrl+C detected)...")
+        trainer.stop_training()
+    finally:
+        if trainer.training_thread:
+            trainer.training_thread.join()
+
+    final = trainer.get_training_progress()
+    if getattr(final, "error", None):
+        typer.echo(f"Training error: {final.error}", err = True)
+        raise typer.Exit(code = 1)

cli/commands/ui.py

@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import os
+import sys
+import time
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+
+def ui(
+    port: int = typer.Option(
+        8000, "--port", "-p", help = "Port to run the UI server on."
+    ),
+    host: str = typer.Option(
+        "0.0.0.0", "--host", "-H", help = "Host address to bind to."
+    ),
+    frontend: Optional[Path] = typer.Option(
+        None, "--frontend", "-f", help = "Path to frontend build directory."
+    ),
+    silent: bool = typer.Option(
+        False, "--silent", "-q", help = "Suppress startup messages."
+    ),
+):
+    """Launch the Unsloth web UI backend server (alias for 'unsloth studio')."""
+    from cli.commands.studio import _studio_venv_python, _find_run_py, STUDIO_HOME
+
+    # Re-execute in studio venv if available and not already inside it
+    studio_venv_dir = STUDIO_HOME / ".venv"
+    in_studio_venv = sys.prefix.startswith(str(studio_venv_dir))
+
+    if not in_studio_venv:
+        studio_python = _studio_venv_python()
+        run_py = _find_run_py()
+        if studio_python and run_py:
+            if not silent:
+                typer.echo("Launching with studio venv...")
+            args = [
+                str(studio_python),
+                str(run_py),
+                "--host",
+                host,
+                "--port",
+                str(port),
+            ]
+            if frontend:
+                args.extend(["--frontend", str(frontend)])
+            if silent:
+                args.append("--silent")
+            os.execvp(str(studio_python), args)
+        else:
+            typer.echo("Studio not set up. Run 'unsloth studio setup' first.")
+            raise typer.Exit(1)
+
+    from studio.backend.run import run_server
+
+    if not silent:
+        from studio.backend.run import _resolve_external_ip
+
+        display_host = _resolve_external_ip() if host == "0.0.0.0" else host
+        typer.echo(f"Starting Unsloth Studio on http://{display_host}:{port}")
+
+    run_server(
+        host = host,
+        port = port,
+        frontend_path = frontend,
+        silent = silent,
+    )
+
+    try:
+        while True:
+            time.sleep(1)
+    except KeyboardInterrupt:
+        typer.echo("\nShutting down...")

cli/config.py

@@ -0,0 +1,149 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+from pathlib import Path
+from typing import Literal, Optional, List
+
+import yaml
+from pydantic import BaseModel, Field
+
+
+class DataConfig(BaseModel):
+    dataset: Optional[str] = None
+    local_dataset: Optional[List[str]] = None
+    format_type: Literal["auto", "alpaca", "chatml", "sharegpt"] = "auto"
+
+
+class TrainingConfig(BaseModel):
+    training_type: Literal["lora", "full"] = "lora"
+    max_seq_length: int = 2048
+    load_in_4bit: bool = True
+    output_dir: Path = Path("./outputs")
+    num_epochs: int = 3
+    learning_rate: float = 2e-4
+    batch_size: int = 2
+    gradient_accumulation_steps: int = 4
+    warmup_steps: int = 5
+    max_steps: int = 0
+    save_steps: int = 0
+    weight_decay: float = 0.01
+    random_seed: int = 3407
+    packing: bool = False
+    train_on_completions: bool = False
+    gradient_checkpointing: Literal["unsloth", "true", "none"] = "unsloth"
+
+
+class LoraConfig(BaseModel):
+    lora_r: int = 64
+    lora_alpha: int = 16
+    lora_dropout: float = 0.0
+    target_modules: str = "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj"
+    vision_all_linear: bool = False
+    use_rslora: bool = False
+    use_loftq: bool = False
+    finetune_vision_layers: bool = True
+    finetune_language_layers: bool = True
+    finetune_attention_modules: bool = True
+    finetune_mlp_modules: bool = True
+
+
+class LoggingConfig(BaseModel):
+    enable_wandb: bool = False
+    wandb_project: str = "unsloth-training"
+    wandb_token: Optional[str] = None
+    enable_tensorboard: bool = False
+    tensorboard_dir: str = "runs"
+    hf_token: Optional[str] = None
+
+
+class Config(BaseModel):
+    model: Optional[str] = None
+    data: DataConfig = Field(default_factory = DataConfig)
+    training: TrainingConfig = Field(default_factory = TrainingConfig)
+    lora: LoraConfig = Field(default_factory = LoraConfig)
+    logging: LoggingConfig = Field(default_factory = LoggingConfig)
+
+    def apply_overrides(self, **kwargs):
+        """Apply CLI overrides by matching arg names to config fields."""
+        for key, value in kwargs.items():
+            if value is None:
+                continue
+            if hasattr(self, key):
+                setattr(self, key, value)
+            else:
+                for section in (self.data, self.training, self.lora, self.logging):
+                    if hasattr(section, key):
+                        setattr(section, key, value)
+                        break
+
+    def model_kwargs(self, use_lora: bool, is_vision: bool) -> dict:
+        """Return kwargs for trainer.prepare_model_for_training()."""
+        # Determine target modules based on model type
+        if use_lora and is_vision:
+            # Vision models expect a string (e.g., "all-linear"); fall back to None to use trainer defaults
+            target_modules = "all-linear" if self.lora.vision_all_linear else None
+        else:
+            parsed = [
+                m.strip()
+                for m in str(self.lora.target_modules).split(",")
+                if m and m.strip()
+            ]
+            target_modules = parsed or None
+
+        return {
+            "use_lora": use_lora,
+            "finetune_vision_layers": self.lora.finetune_vision_layers,
+            "finetune_language_layers": self.lora.finetune_language_layers,
+            "finetune_attention_modules": self.lora.finetune_attention_modules,
+            "finetune_mlp_modules": self.lora.finetune_mlp_modules,
+            "target_modules": target_modules,
+            "lora_r": self.lora.lora_r,
+            "lora_alpha": self.lora.lora_alpha,
+            "lora_dropout": self.lora.lora_dropout,
+            "use_gradient_checkpointing": self.training.gradient_checkpointing,
+            "use_rslora": self.lora.use_rslora,
+            "use_loftq": self.lora.use_loftq,
+        }
+
+    def training_kwargs(self) -> dict:
+        """Return kwargs for trainer.start_training()."""
+        return {
+            "output_dir": str(self.training.output_dir),
+            "num_epochs": self.training.num_epochs,
+            "learning_rate": self.training.learning_rate,
+            "batch_size": self.training.batch_size,
+            "gradient_accumulation_steps": self.training.gradient_accumulation_steps,
+            "warmup_steps": self.training.warmup_steps,
+            "max_steps": self.training.max_steps,
+            "save_steps": self.training.save_steps,
+            "weight_decay": self.training.weight_decay,
+            "random_seed": self.training.random_seed,
+            "packing": self.training.packing,
+            "train_on_completions": self.training.train_on_completions,
+            "max_seq_length": self.training.max_seq_length,
+            "enable_wandb": self.logging.enable_wandb,
+            "wandb_project": self.logging.wandb_project,
+            "wandb_token": self.logging.wandb_token,
+            "enable_tensorboard": self.logging.enable_tensorboard,
+            "tensorboard_dir": self.logging.tensorboard_dir,
+        }
+
+
+def load_config(path: Optional[Path]) -> Config:
+    """Load config from YAML/JSON file, or return defaults if no path given."""
+    if not path:
+        return Config()
+
+    path = Path(path)
+    if not path.exists():
+        raise FileNotFoundError(f"Config file not found: {path}")
+
+    text = path.read_text(encoding = "utf-8")
+    if path.suffix.lower() in {".yaml", ".yml"}:
+        data = yaml.safe_load(text) or {}
+    else:
+        import json
+
+        data = json.loads(text or "{}")
+
+    return Config(**data)

cli/options.py

@@ -0,0 +1,153 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Generate Typer CLI options from Pydantic models."""
+
+import functools
+import inspect
+from pathlib import Path
+from typing import Any, Callable, Optional, get_args, get_origin
+
+import typer
+from pydantic import BaseModel
+
+
+def _python_name_to_cli_flag(name: str) -> str:
+    """Convert python_name to --cli-flag."""
+    return "--" + name.replace("_", "-")
+
+
+def _unwrap_optional(annotation: Any) -> Any:
+    """Unwrap Optional[X] to X."""
+    origin = get_origin(annotation)
+    if origin is not None:
+        args = get_args(annotation)
+        if type(None) in args:
+            non_none = [a for a in args if a is not type(None)]
+            if non_none:
+                return non_none[0]
+    return annotation
+
+
+def _is_bool_field(annotation: Any) -> bool:
+    """Check if field is a boolean (including Optional[bool])."""
+    return _unwrap_optional(annotation) is bool
+
+
+def _is_list_type(annotation: Any) -> bool:
+    """Check if type is a List."""
+    return get_origin(annotation) is list
+
+
+def _get_python_type(annotation: Any) -> type:
+    """Get the Python type for annotation."""
+    unwrapped = _unwrap_optional(annotation)
+    if unwrapped in (str, int, float, bool, Path):
+        return unwrapped
+    return str
+
+
+def _collect_config_fields(config_class: type[BaseModel]) -> list[tuple[str, Any]]:
+    """
+    Collect all fields from a config class, flattening nested models. Returns list of
+    (name, field_info) tuples. Raises ValueError on duplicate field names.
+    """
+    fields = []
+    seen_names: set[str] = set()
+
+    for name, field_info in config_class.model_fields.items():
+        annotation = field_info.annotation
+        # Skip nested models - recurse into them
+        if isinstance(annotation, type) and issubclass(annotation, BaseModel):
+            for nested_name, nested_field in annotation.model_fields.items():
+                if nested_name in seen_names:
+                    raise ValueError(f"Duplicate field name '{nested_name}' in config")
+                seen_names.add(nested_name)
+                fields.append((nested_name, nested_field))
+        else:
+            if name in seen_names:
+                raise ValueError(f"Duplicate field name '{name}' in config")
+            seen_names.add(name)
+            fields.append((name, field_info))
+    return fields
+
+
+def add_options_from_config(config_class: type[BaseModel]) -> Callable:
+    """
+    Decorator that adds CLI options for all fields in a Pydantic config model.
+
+    The decorated function should declare a `config_overrides: dict = None` parameter
+    which will receive a dict of all CLI-provided config values.
+    """
+    fields = _collect_config_fields(config_class)
+    field_names = {
+        name for name, field_info in fields if not _is_list_type(field_info.annotation)
+    }
+
+    def decorator(func: Callable) -> Callable:
+        sig = inspect.signature(func)
+        original_params = list(sig.parameters.values())
+        original_param_names = {p.name for p in original_params}
+
+        # Build new parameters: config fields first, then original params
+        new_params = []
+
+        for field_name, field_info in fields:
+            # Skip fields already defined in function signature (e.g., with envvar)
+            if field_name in original_param_names:
+                continue
+            annotation = field_info.annotation
+            if _is_list_type(annotation):
+                continue
+
+            flag_name = _python_name_to_cli_flag(field_name)
+            help_text = field_info.description or ""
+
+            if _is_bool_field(annotation):
+                default = typer.Option(
+                    None,
+                    f"{flag_name}/--no-{field_name.replace('_', '-')}",
+                    help = help_text,
+                )
+                param = inspect.Parameter(
+                    field_name,
+                    inspect.Parameter.POSITIONAL_OR_KEYWORD,
+                    default = default,
+                    annotation = Optional[bool],
+                )
+            else:
+                py_type = _get_python_type(annotation)
+                default = typer.Option(None, flag_name, help = help_text)
+                param = inspect.Parameter(
+                    field_name,
+                    inspect.Parameter.POSITIONAL_OR_KEYWORD,
+                    default = default,
+                    annotation = Optional[py_type],
+                )
+            new_params.append(param)
+
+        # Add original params, excluding config_overrides (will be injected)
+        for param in original_params:
+            if param.name != "config_overrides":
+                new_params.append(param)
+
+        new_sig = sig.replace(parameters = new_params)
+
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            config_overrides = {}
+            for key in list(kwargs.keys()):
+                if key in field_names:
+                    if kwargs[key] is not None:
+                        config_overrides[key] = kwargs[key]
+                    # Only delete if not an explicitly declared parameter
+                    if key not in original_param_names:
+                        del kwargs[key]
+
+            kwargs["config_overrides"] = config_overrides
+            return func(*args, **kwargs)
+
+        wrapper.__signature__ = new_sig
+        return wrapper
+
+    return decorator

studio/LICENSE.AGPL-3.0

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  Developers that use our General Public Licenses protect your rights
+with two steps: (1) assert copyright on the software, and (2) offer
+you this License which gives you legal permission to copy, distribute
+and/or modify the software.
+
+  A secondary benefit of defending all users' freedom is that
+improvements made in alternate versions of the program, if they
+receive widespread use, become available for other developers to
+incorporate.  Many developers of free software are heartened and
+encouraged by the resulting cooperation.  However, in the case of
+software used on network servers, this result may fail to come about.
+The GNU General Public License permits making a modified version and
+letting the public access it on a server without ever releasing its
+source code to the public.
+
+  The GNU Affero General Public License is designed specifically to
+ensure that, in such cases, the modified source code becomes available
+to the community.  It requires the operator of a network server to
+provide the source code of the modified version running there to the
+users of that server.  Therefore, public use of a modified version, on
+a publicly accessible server, gives the public access to the source
+code of the modified version.
+
+  An older license, called the Affero General Public License and
+published by Affero, was designed to accomplish similar goals.  This is
+a different license, not a version of the Affero GPL, but Affero has
+released a new version of the Affero GPL which permits relicensing under
+this license.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU Affero General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy.  The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+  A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
+computer or modifying a private copy.  Propagation includes copying,
+distribution (with or without modification), making available to the
+public, and in some countries other activities as well.
+
+  To "convey" a work means any kind of propagation that enables other
+parties to make or receive copies.  Mere interaction with a user through
+a computer network, with no transfer of a copy, is not conveying.
+
+  An interactive user interface displays "Appropriate Legal Notices"
+to the extent that it includes a convenient and prominently visible
+feature that (1) displays an appropriate copyright notice, and (2)
+tells the user that there is no warranty for the work (except to the
+extent that warranties are provided), that licensees may convey the
+work under this License, and how to view a copy of this License.  If
+the interface presents a list of user commands or options, such as a
+menu, a prominent item in the list meets this criterion.
+
+  1. Source Code.
+
+  The "source code" for a work means the preferred form of the work
+for making modifications to it.  "Object code" means any non-source
+form of a work.
+
+  A "Standard Interface" means an interface that either is an official
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+
+  The "System Libraries" of an executable work include anything, other
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+(kernel, window system, and so on) of the specific operating system
+(if any) on which the executable work runs, or a compiler used to
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+subprograms and other parts of the work.
+
+  The Corresponding Source need not include anything that users
+can regenerate automatically from other parts of the Corresponding
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+
+  The Corresponding Source for a work in source code form is that
+same work.
+
+  2. Basic Permissions.
+
+  All rights granted under this License are granted for the term of
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studio/Unsloth_Studio_Colab.ipynb

@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f2b0c6a1",
+   "metadata": {},
+   "source": [
+    "**License Notice**\n",
+    "\n",
+    "SPDX-License-Identifier: AGPL-3.0-only\n",
+    "\n",
+    "Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "447c1156",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ===========================================\n",
+    "# ⚠️  GPU Check - Run This First!\n",
+    "# ===========================================\n",
+    "import torch\n",
+    "\n",
+    "print(\"🔍 Checking for GPU...\")\n",
+    "if not torch.cuda.is_available():\n",
+    "    print(\"❌ ERROR: No GPU detected!\")\n",
+    "    print(\"\\n📋 To enable GPU:\")\n",
+    "    print(\"   1. Go to: Runtime → Change runtime type\")\n",
+    "    print(\"   2. Select: Hardware accelerator → GPU (T4 is free)\")\n",
+    "    print(\"   3. Click: Save\")\n",
+    "    print(\"   4. Restart and re-run all cells\")\n",
+    "    raise RuntimeError(\"⛔ GPU required for Unsloth Studio\")\n",
+    "else:\n",
+    "    gpu_name = torch.cuda.get_device_name(0)\n",
+    "    print(f\"✅ GPU detected: {gpu_name}\")\n",
+    "    print(\"   Ready to proceed!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f04a9b46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ===========================================\n",
+    "# GitHub Authentication (Private Repo)\n",
+    "# ===========================================\n",
+    "from getpass import getpass\n",
+    "import os\n",
+    "\n",
+    "print(\"🔐 GitHub Token Required\")\n",
+    "print(\"Get token: https://github.com/settings/tokens\")\n",
+    "print(\"Scope needed: 'repo'\")\n",
+    "print(\"-\" * 50)\n",
+    "\n",
+    "github_token = getpass(\"Enter GitHub Token: \")\n",
+    "os.environ['GITHUB_TOKEN'] = github_token\n",
+    "print(\"✅ Token stored\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27e68f91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ===========================================\n",
+    "# Setup: Clone repo and run setup\n",
+    "# ===========================================\n",
+    "\n",
+    "import os\n",
+    "github_token = os.environ['GITHUB_TOKEN']\n",
+    "!git clone https://{github_token}@github.com/unslothai/new-ui-prototype.git\n",
+    "%cd /content/new-ui-prototype\n",
+    "\n",
+    "# Run setup script\n",
+    "!chmod +x setup.sh\n",
+    "!./setup.sh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "277e431e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ===========================================\n",
+    "# Start Unsloth Studio\n",
+    "# ===========================================\n",
+    "import sys\n",
+    "sys.path.insert(0, '/content/new-ui-prototype/studio/backend')\n",
+    "\n",
+    "from colab import start\n",
+    "start()"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

studio/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

studio/backend/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

studio/backend/assets/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

studio/backend/assets/configs/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

studio/backend/assets/configs/full_finetune.yaml

@@ -0,0 +1,42 @@
+model: unsloth/Qwen2.5-0.5B
+
+data:
+  dataset: tatsu-lab/alpaca
+  format_type: auto
+
+training:
+  training_type: full
+  max_seq_length: 2048
+  load_in_4bit: false
+  output_dir: outputs
+  num_epochs: 1
+  learning_rate: 0.0002
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 0
+  save_steps: 0
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: "unsloth"
+
+lora:
+  lora_r: 64
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules: ""
+  vision_all_linear: false
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: unsloth-training
+  enable_tensorboard: false
+  tensorboard_dir: runs

studio/backend/assets/configs/lora_text.yaml

@@ -0,0 +1,42 @@
+model: unsloth/Qwen2.5-0.5B
+
+data:
+  dataset: tatsu-lab/alpaca
+  format_type: auto
+
+training:
+  training_type: lora
+  max_seq_length: 2048
+  load_in_4bit: true
+  output_dir: outputs
+  num_epochs: 1
+  learning_rate: 0.0002
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 0
+  save_steps: 0
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: "unsloth"
+
+lora:
+  lora_r: 64
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules: "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj"
+  vision_all_linear: false
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: unsloth-training
+  enable_tensorboard: false
+  tensorboard_dir: runs

studio/backend/assets/configs/model_defaults/default.yaml

@@ -0,0 +1,56 @@
+# Default model training parameters
+# Used for models without specific configurations
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 5e-5
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_ratio: 0.1
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.7
+  top_p: 0.95
+  top_k: -1
+  min_p: 0.01
+

studio/backend/assets/configs/model_defaults/embedding/unsloth_Qwen3-Embedding-0.6B.yaml

@@ -0,0 +1,43 @@
+# Model defaults for unsloth/Qwen3-Embedding-0.6B
+# Based on Qwen3_Embedding_(0_6B).py embedding notebook
+# Also applies to: unsloth/Qwen3-Embedding-4B
+
+training:
+  max_seq_length: 512
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 3e-5
+  batch_size: 256
+  gradient_accumulation_steps: 1
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: false
+  optim: "adamw_8bit"
+  lr_scheduler_type: "constant_with_warmup"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 50

studio/backend/assets/configs/model_defaults/embedding/unsloth_all-MiniLM-L6-v2.yaml

@@ -0,0 +1,39 @@
+# Model defaults for unsloth/all-MiniLM-L6-v2
+# Based on All_MiniLM_L6_v2.py embedding notebook
+
+training:
+  max_seq_length: 512
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 256
+  gradient_accumulation_steps: 1
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: false
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 64
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "value"
+    - "key"
+    - "dense"
+    - "query"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 50

studio/backend/assets/configs/model_defaults/embedding/unsloth_bge-m3.yaml

@@ -0,0 +1,39 @@
+# Model defaults for unsloth/bge-m3
+# Based on BGE_M3.py embedding notebook
+
+training:
+  max_seq_length: 512
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 3e-5
+  batch_size: 256
+  gradient_accumulation_steps: 1
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: false
+  optim: "adamw_8bit"
+  lr_scheduler_type: "constant_with_warmup"
+
+lora:
+  lora_r: 32
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "key"
+    - "query"
+    - "dense"
+    - "value"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 50

studio/backend/assets/configs/model_defaults/embedding/unsloth_embeddinggemma-300m.yaml

@@ -0,0 +1,42 @@
+# Model defaults for unsloth/embeddinggemma-300m
+# Based on EmbeddingGemma_(300M).py embedding notebook
+
+training:
+  max_seq_length: 1024
+  # num_epochs: 1
+  num_epochs: 0
+  learning_rate: 2e-5
+  batch_size: 64
+  gradient_accumulation_steps: 2
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 5

studio/backend/assets/configs/model_defaults/embedding/unsloth_gte-modernbert-base.yaml

@@ -0,0 +1,38 @@
+# Model defaults for unsloth/gte-modernbert-base
+# Based on ModernBert.py embedding notebook
+
+training:
+  max_seq_length: 512
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 3e-5
+  batch_size: 256
+  gradient_accumulation_steps: 1
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "constant_with_warmup"
+
+lora:
+  lora_r: 64
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "Wi"
+    - "Wo"
+    - "Wqkv"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 50

studio/backend/assets/configs/model_defaults/ernie/unsloth_ERNIE-4.5-21B-A3B-PT.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/ERNIE-4.5-21B-A3B-PT
+# Based on ERNIE_4_5_21B_A3B_PT-Conversational.ipynb
+# Also applies to: unsloth/ERNIE-4.5-21B-A3B-PT
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/ernie/unsloth_ERNIE-4.5-VL-28B-A3B-PT.yaml

@@ -0,0 +1,55 @@
+# Model defaults for unsloth/ERNIE-4.5-VL-28B-A3B-PT
+# Based on ERNIE_4_5_VL_28B_A3B_PT_Vision.ipynb
+# Also applies to: unsloth/ERNIE-4.5-VL-28B-A3B-PT
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: true
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: true
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/falcon/tiiuae_Falcon-H1-0.5B-Instruct.yaml

@@ -0,0 +1,47 @@
+# Model defaults for tiiuae/Falcon-H1-0.5B-Instruct
+# Based on Falcon_H1_(0.5B)-Alpaca.ipynb
+# Also applies to: tiiuae/Falcon-H1-0.5B-Instruct, unsloth/Falcon-H1-0.5B-Instruct
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 8
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: false
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.1
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/gemma/unsloth_codegemma-7b-bnb-4bit.yaml

@@ -0,0 +1,50 @@
+# Model defaults for unsloth/codegemma-7b-bnb-4bit
+# Based on CodeGemma_(7B)-Conversational.ipynb
+# Also applies to: unsloth/codegemma-7b, google/codegemma-7b
+# added inference parameters from Ollama
+
+training:
+  trust_remote_code: false
+  max_seq_length: 4096
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0
+  top_p: 0.9

studio/backend/assets/configs/model_defaults/gemma/unsloth_functiongemma-270m-it.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/functiongemma-270m-it
+# Based on FunctionGemma_(270M).ipynb
+# Also applies to: unsloth/functiongemma-270m-it-unsloth-bnb-4bit, google/functiongemma-270m-it, unsloth/functiongemma-270m-it-unsloth-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 4096
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 10
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 128
+  lora_alpha: 256
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-2-27b-bnb-4bit.yaml

@@ -0,0 +1,46 @@
+# Model defaults for unsloth/gemma-2-27b-bnb-4bit
+# Based on Gemma2_(9B)-Alpaca.ipynb (same defaults for larger models)
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-2-2b.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-2-2b
+# Based on Gemma2_(2B)-Alpaca.ipynb
+# Also applies to: unsloth/gemma-2-2b-bnb-4bit, google/gemma-2-2b
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-270m-it.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/gemma-3-270m-it
+# Based on Gemma3_(270M).ipynb
+# Also applies to: unsloth/gemma-3-270m-it-unsloth-bnb-4bit, google/gemma-3-270m-it, unsloth/gemma-3-270m-it-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 5e-5
+  batch_size: 4
+  gradient_accumulation_steps: 1
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 128
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-27b-it.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/gemma-3-27b-it
+# Based on Gemma3_(27B)_A100-Conversational.ipynb
+# Also applies to: unsloth/gemma-3-27b-it-unsloth-bnb-4bit, google/gemma-3-27b-it, unsloth/gemma-3-27b-it-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-4b-it.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/gemma-3-4b-it
+# Based on Gemma3_(4B).ipynb
+# Also applies to: unsloth/gemma-3-4b-it-unsloth-bnb-4bit, google/gemma-3-4b-it, unsloth/gemma-3-4b-it-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-4b-pt.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/gemma-3-4b-pt
+# Based on Gemma3_(4B)-Vision.ipynb
+# Also applies to: unsloth/gemma-3-4b-pt-unsloth-bnb-4bit, google/gemma-3-4b-pt, unsloth/gemma-3-4b-pt-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: true
+  optim: "adamw_torch_fused"
+  lr_scheduler_type: "cosine"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3n-E4B-it.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/gemma-3n-E4B-it
+# Based on Gemma3N_(4B)-Conversational.ipynb
+# Also applies to: unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit, google/gemma-3n-E4B-it, unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 1024
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+audio_input: true
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3n-E4B.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/gemma-3n-E4B
+# Based on Gemma3N_(4B)-Vision.ipynb
+# Also applies to: unsloth/gemma-3n-E4B-unsloth-bnb-4bit, google/gemma-3n-E4B
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: true
+  optim: "adamw_torch_fused"
+  lr_scheduler_type: "cosine"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+audio_input: true
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gpt-oss/unsloth_gpt-oss-120b.yaml

@@ -0,0 +1,52 @@
+# Model defaults for unsloth/gpt-oss-120b
+# Based on gpt-oss-(120B)_A100-Fine-tuning.ipynb
+# Also applies to: openai/gpt-oss-120b, unsloth/gpt-oss-120b-unsloth-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 4096
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 1
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 1.0
+  top_k: 0
+

studio/backend/assets/configs/model_defaults/gpt-oss/unsloth_gpt-oss-20b.yaml

@@ -0,0 +1,52 @@
+# Model defaults for unsloth/gpt-oss-20b
+# Based on gpt-oss-(20B)-Fine-tuning.ipynb
+# Also applies to: openai/gpt-oss-20b, unsloth/gpt-oss-20b-unsloth-bnb-4bit, unsloth/gpt-oss-20b-BF16
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 1024
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 1.0
+  top_k: 0
+

studio/backend/assets/configs/model_defaults/granite/unsloth_granite-4.0-350m-unsloth-bnb-4bit.yaml

@@ -0,0 +1,54 @@
+# Model defaults for unsloth/granite-4.0-350m
+# Based on Granite4.0_350M.ipynb
+# Also applies to: ibm-granite/granite-4.0-350m, unsloth/granite-4.0-350m-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "shared_mlp.input_linear"
+    - "shared_mlp.output_linear"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.0
+  top_p: 1.0
+  top_k: 0
+

studio/backend/assets/configs/model_defaults/granite/unsloth_granite-4.0-h-micro.yaml

@@ -0,0 +1,54 @@
+# Model defaults for unsloth/granite-4.0-h-micro
+# Based on Granite4.0.ipynb
+# Also applies to: ibm-granite/granite-4.0-h-micro, unsloth/granite-4.0-h-micro-bnb-4bit, unsloth/granite-4.0-h-micro-unsloth-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "shared_mlp.input_linear"
+    - "shared_mlp.output_linear"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.0
+  top_p: 1.0
+  top_k: 0
+

studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.2-11B-Vision-Instruct.yaml

@@ -0,0 +1,49 @@
+# Model defaults for unsloth/Llama-3.2-11B-Vision-Instruct
+# Based on Llama3.2_(11B)-Vision.ipynb
+# Also applies to: unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit, meta-llama/Llama-3.2-11B-Vision-Instruct, unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.2-1B-Instruct.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Llama-3.2-1B-Instruct
+# Based on Llama3.2_(1B)-RAFT.ipynb
+# Also applies to: unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit, meta-llama/Llama-3.2-1B-Instruct, unsloth/Llama-3.2-1B-Instruct-bnb-4bit, RedHatAI/Llama-3.2-1B-Instruct-FP8, unsloth/Llama-3.2-1B-Instruct-FP8-Block, unsloth/Llama-3.2-1B-Instruct-FP8-Dynamic
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 5
+  num_epochs: 0
+  learning_rate: 2e-5
+  batch_size: 1
+  gradient_accumulation_steps: 8
+  warmup_steps: 0
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: true
+  optim: "adamw_torch"
+  lr_scheduler_type: "cosine"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.2-3B-Instruct.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/Llama-3.2-3B-Instruct
+# Based on Llama3.2_(1B_and_3B)-Conversational.ipynb
+# Also applies to: unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit, meta-llama/Llama-3.2-3B-Instruct, unsloth/Llama-3.2-3B-Instruct-bnb-4bit, RedHatAI/Llama-3.2-3B-Instruct-FP8, unsloth/Llama-3.2-3B-Instruct-FP8-Block, unsloth/Llama-3.2-3B-Instruct-FP8-Dynamic
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.3-70B-Instruct.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/Llama-3.3-70B-Instruct
+# Based on Llama3.3_(70B)_A100-Conversational.ipynb
+# Also applies to: unsloth/Llama-3.3-70B-Instruct-unsloth-bnb-4bit, meta-llama/Llama-3.3-70B-Instruct, unsloth/Llama-3.3-70B-Instruct-bnb-4bit, RedHatAI/Llama-3.3-70B-Instruct-FP8, unsloth/Llama-3.3-70B-Instruct-FP8-Block, unsloth/Llama-3.3-70B-Instruct-FP8-Dynamic
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/llama/unsloth_Meta-Llama-3.1-70B-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Meta-Llama-3.1-70B-bnb-4bit
+# Based on Llama3.1_(8B)-Alpaca.ipynb
+# Also applies to: unsloth/Meta-Llama-3.1-8B-bnb-4bit, unsloth/Meta-Llama-3.1-8B-unsloth-bnb-4bit, meta-llama/Meta-Llama-3.1-8B, unsloth/Meta-Llama-3.1-8B, unsloth/Meta-Llama-3.1-70B, meta-llama/Meta-Llama-3.1-70B, unsloth/Meta-Llama-3.1-405B-bnb-4bit, meta-llama/Meta-Llama-3.1-405B
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llama/unsloth_Meta-Llama-3.1-8B-Instruct-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
+# Based on Llama3.1_(8B)-Inference.ipynb
+# Also applies to: "unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "meta-llama/Meta-Llama-3.1-8B-Instruct", "unsloth/Meta-Llama-3.1-8B-Instruct","RedHatAI/Llama-3.1-8B-Instruct-FP8","unsloth/Llama-3.1-8B-Instruct-FP8-Block","unsloth/Llama-3.1-8B-Instruct-FP8-Dynamic"
+
+training:
+  trust_remote_code: false
+  max_seq_length: 8192
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llama/unsloth_llama-3-8b-Instruct-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/llama-3-8b-Instruct-bnb-4bit
+# Based on Llama3_(8B)-Conversational.ipynb
+# Also applies to: unsloth/llama-3-8b-Instruct, meta-llama/Meta-Llama-3-8B-Instruct
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llama/unsloth_llama-3-8b-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/llama-3-8b-bnb-4bit
+# Based on Llama3_(8B)-Alpaca.ipynb
+# Also applies to: unsloth/llama-3-8b, meta-llama/Meta-Llama-3-8B
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llasa/unsloth_Llasa-3B.yaml

@@ -0,0 +1,46 @@
+# Model defaults for unsloth/Llasa-3B
+# Based on Llasa_TTS_(3B).ipynb and Llasa_TTS_(1B).ipynb
+# Also applies to: HKUSTAudio/Llasa-1B
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 5e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 128
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "v_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.2
+  top_p: 1.2
+

studio/backend/assets/configs/model_defaults/mistral/unsloth_Magistral-Small-2509-unsloth-bnb-4bit.yaml

@@ -0,0 +1,56 @@
+# Model defaults for unsloth/Magistral-Small-2509
+# Based on Magistral_(24B)-Reasoning-Conversational.ipynb
+# Also applies to: mistralai/Magistral-Small-2509, unsloth/Magistral-Small-2509-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.7
+  min_p: 0.01
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/mistral/unsloth_Ministral-3-3B-Instruct-2512.yaml

@@ -0,0 +1,55 @@
+# Model defaults for unsloth/Ministral-3-3B-Instruct-2512
+# Based on Ministral_3_VL_(3B)_Vision.ipynb
+# Also applies to: unsloth/Ministral-3-3B-Instruct-2512
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.15
+  top_p: default
+

studio/backend/assets/configs/model_defaults/mistral/unsloth_Mistral-Nemo-Base-2407-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Mistral-Nemo-Base-2407-bnb-4bit
+# Based on Mistral_Nemo_(12B)-Alpaca.ipynb
+# Also applies to:  "unsloth/Mistral-Nemo-Base-2407",  "mistralai/Mistral-Nemo-Base-2407", "unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "unsloth/Mistral-Nemo-Instruct-2407", "mistralai/Mistral-Nemo-Instruct-2407",
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/mistral/unsloth_Mistral-Small-Instruct-2409.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Mistral-Small-Instruct-2409
+# Based on Mistral_Small_(22B)-Alpaca.ipynb 
+# Also applies to: unsloth/Mistral-Small-Instruct-2409-bnb-4bit, mistralai/Mistral-Small-Instruct-2409
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/mistral/unsloth_Pixtral-12B-2409.yaml

@@ -0,0 +1,49 @@
+# Model defaults for unsloth/Pixtral-12B-2409
+# Based on Pixtral_(12B)-Vision.ipynb
+# Also applies to: unsloth/Pixtral-12B-2409-unsloth-bnb-4bit, mistralai/Pixtral-12B-2409, unsloth/Pixtral-12B-2409-bnb-4bit
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "paged_adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: false
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/mistral/unsloth_mistral-7b-instruct-v0.3-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/mistral-7b-instruct-v0.3-bnb-4bit
+# Based on Mistral_v0.3_(7B)-Conversational.ipynb
+# Also applies to: unsloth/mistral-7b-instruct-v0.3, mistralai/Mistral-7B-Instruct-v0.3
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/mistral/unsloth_mistral-7b-v0.3-bnb-4bit.yaml

@@ -0,0 +1,46 @@
+# Model defaults for unsloth/mistral-7b-v0.3-bnb-4bit
+# Based on Mistral_v0.3_(7B)-Alpaca.ipynb
+# Also applies to: "unsloth/mistral-7b-v0.3", "mistralai/Mistral-7B-v0.3",
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/other/OuteAI_Llama-OuteTTS-1.0-1B.yaml

@@ -0,0 +1,51 @@
+# Model defaults for OuteAI/Llama-OuteTTS-1.0-1B
+# Based on Oute_TTS_(1B).ipynb
+# Also applies to: OuteAI/Llama-OuteTTS-1.0-1B
+# added inference parameters from unsloth notebook
+
+audio_type: dac
+
+training:
+  trust_remote_code: false
+  eval_steps: 0
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 128
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "v_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.4
+  top_k: 40
+  top_p: 0.9
+  min_p: 0.05
+

studio/backend/assets/configs/model_defaults/other/Spark-TTS-0.5B_LLM.yaml

@@ -0,0 +1,55 @@
+# Model defaults for Spark-TTS-0.5B/LLM
+# Based on Spark_TTS_(0_5B).ipynb
+# Also applies to: Spark-TTS-0.5B/LLM
+# added inference parameters from unsloth notebook
+
+audio_type: bicodec
+
+training:
+  trust_remote_code: false
+  eval_steps: 0
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 128
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.8
+  top_k: 50
+  top_p: 1.0
+

studio/backend/assets/configs/model_defaults/other/sesame_csm-1b.yaml

@@ -0,0 +1,50 @@
+# Model defaults for sesame/csm-1b
+# Based on Sesame_CSM_(1B)-TTS.ipynb
+# Also applies to: sesame/csm-1b
+
+audio_type: csm
+
+training:
+  trust_remote_code: false
+  eval_steps: 0
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/other/unsloth_GLM-4.7-Flash.yaml

@@ -0,0 +1,52 @@
+# Model defaults for unsloth/GLM-4.7-Flash
+# Based on GLM_Flash_A100(80GB).py
+# Also applies to: unsloth/GLM-4.7-Flash-unsloth-bnb-4bit, unsloth/GLM-4.7-Flash-bnb-4bit, THUDM/GLM-4.7-Flash
+
+training:
+  trust_remote_code: true
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 60
+  save_steps: 60
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "out_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: true
+  temperature: 0.7
+  top_p: 0.8
+  top_k: 20
+

studio/backend/assets/configs/model_defaults/other/unsloth_LFM2-1.2B.yaml

@@ -0,0 +1,45 @@
+# Model defaults for unsloth/LFM2-1.2B
+# Based on Liquid_LFM2_(1.2B)-Conversational.ipynb
+# Also applies to: unsloth/LFM2-1.2B
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.3
+  min_p: 0.15
+

studio/backend/assets/configs/model_defaults/other/unsloth_Nemotron-3-Nano-30B-A3B.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/Nemotron-3-Nano-30B-A3B
+# Based on Nemotron-3-Nano-30B-A3B_A100.ipynb
+# Also applies to: unsloth/Nemotron-3-Nano-30B-A3B
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: true
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "in_proj"
+    - "out_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: true
+  temperature: 1.0
+  top_p: 1.0
+

studio/backend/assets/configs/model_defaults/other/unsloth_PaddleOCR-VL.yaml

@@ -0,0 +1,55 @@
+# Model defaults for unsloth/PaddleOCR-VL
+# Based on Paddle_OCR_(1B)_Vision.ipynb
+# Also applies to: unsloth/PaddleOCR-VL
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: true
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 5e-5
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 64
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: true
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/other/unsloth_answerdotai_ModernBERT-large.yaml

@@ -0,0 +1,46 @@
+# Model defaults for answerdotai/ModernBERT-large
+# Based on bert_classification.ipynb
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 1
+  num_epochs: 0
+  learning_rate: 5e-5
+  batch_size: 32
+  gradient_accumulation_steps: 1
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/other/unsloth_orpheus-3b-0.1-ft.yaml

@@ -0,0 +1,54 @@
+# Model defaults for unsloth/orpheus-3b-0.1-ft
+# Based on Orpheus_(3B)-TTS.ipynb
+# Also applies to: unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit, canopylabs/orpheus-3b-0.1-ft, unsloth/orpheus-3b-0.1-ft-bnb-4bit
+# added inference parameters from unsloth notebook
+
+audio_type: snac
+
+training:
+  trust_remote_code: false
+  eval_steps: 0
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 64
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.6
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/other/unsloth_tinyllama-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/tinyllama
+# Based on TinyLlama_(1.1B)-Alpaca.ipynb
+# Also applies to: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+
+training:
+  trust_remote_code: false
+  max_seq_length: 4096
+  # num_epochs: 1
+  num_epochs: 0
+  learning_rate: 2e-5
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_ratio: 0.1
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.1
+  random_seed: 3407
+  packing: true
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/other/unsloth_whisper-large-v3.yaml

@@ -0,0 +1,46 @@
+# Model defaults for unsloth/whisper-large-v3
+# Based on Whisper.ipynb
+# Also applies to: unsloth/whisper-large-v3, openai/whisper-large-v3
+
+audio_type: whisper
+audio_input: true
+
+training:
+  trust_remote_code: false
+  eval_steps: 5
+  max_seq_length: 448
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 1e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 64
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "v_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/phi/unsloth_Phi-3-medium-4k-instruct.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Phi-3-medium-4k-instruct
+# Based on Phi_3_Medium-Conversational.ipynb
+# Also applies to: "unsloth/Phi-3-medium-4k-instruct-bnb-4bit", "microsoft/Phi-3-medium-4k-instruct",
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/phi/unsloth_Phi-3.5-mini-instruct.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Phi-3.5-mini-instruct
+# Based on Phi_3.5_Mini-Conversational.ipynb
+# Also applies to: "unsloth/Phi-3.5-mini-instruct-bnb-4bit", "microsoft/Phi-3.5-mini-instruct"
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/phi/unsloth_Phi-4.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/Phi-4
+# Based on Phi_4-Conversational.ipynb
+# Also applies to: unsloth/phi-4-unsloth-bnb-4bit, microsoft/phi-4, unsloth/phi-4-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.8
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/qwen/imdatta0_tiny_qwen3_moe_2.8B_0.7B.yaml

@@ -0,0 +1,53 @@
+# Model defaults for imdatta0/tiny_qwen3_moe_2.8B_0.7B
+# Based on TinyQwen3_MoE.py
+# Dummy model of qwen3moe architecture created to fit in T4
+# MoE model - includes gate_up_proj for MoE layers
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 1
+  warmup_steps: 5
+  max_steps: 50
+  save_steps: 50
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "gate_up_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.6
+  top_k: 20
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2-7B.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Qwen2-7B
+# Based on Qwen2_(7B)-Alpaca.ipynb
+# Also applies to: unsloth/Qwen2-7B-bnb-4bit, Qwen/Qwen2-7B
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2-VL-7B-Instruct.yaml

@@ -0,0 +1,49 @@
+# Model defaults for unsloth/Qwen2-VL-7B-Instruct
+# Based on Qwen2_VL_(7B)-Vision.ipynb 
+# Also applies to: unsloth/Qwen2-VL-7B-Instruct-unsloth-bnb-4bit, Qwen/Qwen2-VL-7B-Instruct, unsloth/Qwen2-VL-7B-Instruct-bnb-4bit
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-1.5B-Instruct.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Qwen2.5-1.5B-Instruct
+# Based on nemo_gym_sudoku.ipynb
+# Also applies to: unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit, Qwen/Qwen2.5-1.5B-Instruct, unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit
+
+training:
+  trust_remote_code: false
+  max_seq_length: 4096
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 1e-5
+  batch_size: 1
+  gradient_accumulation_steps: 64
+  warmup_ratio: 0.1
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 42
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 4
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-7B.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Qwen2.5-7B
+# Based on Qwen2.5_(7B)-Alpaca.ipynb
+# Also applies to: unsloth/Qwen2.5-7B-unsloth-bnb-4bit, Qwen/Qwen2.5-7B, unsloth/Qwen2.5-7B-bnb-4bit
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-Coder-1.5B-Instruct.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Qwen2.5-Coder-1.5B-Instruct
+# Based on Qwen2.5_Coder_(1.5B)-Tool_Calling.ipynb
+# Also applies to: unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit, Qwen/Qwen2.5-Coder-1.5B-Instruct
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-Coder-14B-Instruct.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/Qwen2.5-Coder-14B-Instruct
+# Based on Qwen2.5_Coder_(14B)-Conversational.ipynb
+# Also applies to: unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit, Qwen/Qwen2.5-Coder-14B-Instruct
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "paged_adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-Coder-7B-Instruct-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
+# Based on CodeForces-cot-Finetune_for_Reasoning_on_CodeForces.ipynb
+# Also applies to: unsloth/Qwen2.5-Coder-7B-Instruct, Qwen/Qwen2.5-Coder-7B-Instruct
+
+training:
+  trust_remote_code: false
+  max_seq_length: 32768
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-VL-7B-Instruct-bnb-4bit.yaml

@@ -0,0 +1,49 @@
+# Model defaults for unsloth/Qwen2.5-VL-7B-Instruct-bnb-4bit
+# Based on Qwen2.5_VL_(7B)-Vision.ipynb
+# Also applies to: unsloth/Qwen2.5-VL-7B-Instruct, Qwen/Qwen2.5-VL-7B-Instruct, unsloth/Qwen2.5-VL-7B-Instruct-unsloth-bnb-4bit
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-0.6B.yaml

@@ -0,0 +1,52 @@
+# Model defaults for unsloth/Qwen3-0.6B
+# Based on Qwen3_(0_6B)-Phone_Deployment.ipynb
+# Also applies to: unsloth/Qwen3-0.6B-unsloth-bnb-4bit, Qwen/Qwen3-0.6B, unsloth/Qwen3-0.6B-bnb-4bit, Qwen/Qwen3-0.6B-FP8, unsloth/Qwen3-0.6B-FP8
+# added inference parameters from Ollama
+
+training:
+  trust_remote_code: false
+  max_seq_length: 1024
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 5e-5
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.6
+  top_k: 20
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-14B-Base-unsloth-bnb-4bit.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/Qwen3-14B-Base
+# Based on Qwen3_(14B)-Alpaca.ipynb
+# Also applies to: unsloth/Qwen3-14B-Base, Qwen/Qwen3-14B-Base, unsloth/Qwen3-14B-Base-bnb-4bit
+# added inference parameters from Ollama
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.6
+  top_k: 20
+  top_p: 0.95

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-14B.yaml

@@ -0,0 +1,52 @@
+# Model defaults for unsloth/Qwen3-14B
+# Based on Qwen3_(14B).ipynb
+# Also applies to: unsloth/Qwen3-14B-unsloth-bnb-4bit, Qwen/Qwen3-14B, unsloth/Qwen3-14B-bnb-4bit, Qwen/Qwen3-14B-FP8, unsloth/Qwen3-14B-FP8
+# added inference parameters from Ollama
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.6
+  top_k: 20
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-30B-A3B-Instruct-2507.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/Qwen3-30B-A3B-Instruct-2507
+# Based on Qwen3_MoE.py
+# Also applies to: Qwen/Qwen3-30B-A3B-Instruct-2507, unsloth/Qwen3-30B-A3B-Instruct-2507-bnb-4bit
+# MoE model - includes gate_up_proj for MoE layers
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 1
+  warmup_steps: 5
+  max_steps: 50
+  save_steps: 50
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "gate_up_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.6
+  top_k: 20
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-32B.yaml

@@ -0,0 +1,52 @@
+# Model defaults for unsloth/Qwen3-32B
+# Based on Qwen3_(32B)_A100-Reasoning-Conversational.ipynb
+# Also applies to: unsloth/Qwen3-32B-unsloth-bnb-4bit, Qwen/Qwen3-32B, unsloth/Qwen3-32B-bnb-4bit, Qwen/Qwen3-32B-FP8, unsloth/Qwen3-32B-FP8
+# added inference parameters from Ollama
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.6
+  top_k: 20
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-4B-Instruct-2507.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/Qwen3-4B-Instruct-2507
+# Based on Qwen3_(4B)-Instruct.ipynb
+# Also applies to: unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit, Qwen/Qwen3-4B-Instruct-2507, unsloth/Qwen3-4B-Instruct-2507-bnb-4bit, Qwen/Qwen3-4B-Instruct-2507-FP8, unsloth/Qwen3-4B-Instruct-2507-FP8
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.7
+  top_p: 0.80
+  top_k: 20
+  min_p: 0.00
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-4B-Thinking-2507.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/Qwen3-4B-Thinking-2507
+# Based on Qwen3_(4B)-Thinking.ipynb
+# Also applies to: unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit, Qwen/Qwen3-4B-Thinking-2507, unsloth/Qwen3-4B-Thinking-2507-bnb-4bit, Qwen/Qwen3-4B-Thinking-2507-FP8, unsloth/Qwen3-4B-Thinking-2507-FP8
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.6
+  top_p: 0.95
+  top_k: 20
+  min_p: 0.00
+

studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-VL-8B-Instruct-unsloth-bnb-4bit.yaml

@@ -0,0 +1,50 @@
+# Model defaults for unsloth/Qwen3-VL-8B-Instruct
+# Based on Qwen3_VL_(8B)-Vision.ipynb
+# Also applies to: Qwen/Qwen3-VL-8B-Instruct-FP8, unsloth/Qwen3-VL-8B-Instruct-FP8, unsloth/Qwen3-VL-8B-Instruct, Qwen/Qwen3-VL-8B-Instruct, unsloth/Qwen3-VL-8B-Instruct-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.7
+  top_p: 0.8
+  top_k: 20
+

studio/backend/assets/configs/vision_lora.yaml

@@ -0,0 +1,42 @@
+model: unsloth/Qwen2-VL-2B-Instruct-bnb-4bit
+
+data:
+  dataset: philschmid/amazon-product-descriptions-vlm
+  format_type: auto
+
+training:
+  training_type: lora
+  max_seq_length: 2048
+  load_in_4bit: true
+  output_dir: outputs
+  num_epochs: 1
+  learning_rate: 0.0002
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 0
+  save_steps: 0
+  weight_decay: 0.01
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: "unsloth"
+
+lora:
+  lora_r: 64
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules: ""   # vision uses vision_all_linear by default
+  vision_all_linear: true
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: unsloth-training
+  enable_tensorboard: false
+  tensorboard_dir: runs

studio/backend/auth/__init__.py

@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""
+Authentication module for JWT-based auth with SQLite storage.
+"""
+
+from .authentication import (
+    create_access_token,
+    create_refresh_token,
+    refresh_access_token,
+    get_current_subject,
+    reload_secret,
+)
+from .storage import (
+    is_initialized,
+    create_initial_user,
+    get_user_and_secret,
+    load_jwt_secret,
+    save_setup_token,
+    consume_setup_token,
+    has_pending_setup_token,
+    save_refresh_token,
+    verify_refresh_token,
+    revoke_user_refresh_tokens,
+)
+from .hashing import hash_password, verify_password
+
+__all__ = [
+    "create_access_token",
+    "create_refresh_token",
+    "refresh_access_token",
+    "get_current_subject",
+    "reload_secret",
+    "is_initialized",
+    "create_initial_user",
+    "get_user_and_secret",
+    "load_jwt_secret",
+    "save_setup_token",
+    "consume_setup_token",
+    "has_pending_setup_token",
+    "save_refresh_token",
+    "verify_refresh_token",
+    "revoke_user_refresh_tokens",
+    "hash_password",
+    "verify_password",
+]

studio/backend/auth/authentication.py

@@ -0,0 +1,108 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import secrets
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+from fastapi import Depends, HTTPException, status
+from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
+import jwt
+
+from .storage import load_jwt_secret, save_refresh_token, verify_refresh_token
+
+ALGORITHM = "HS256"
+ACCESS_TOKEN_EXPIRE_MINUTES = 60
+REFRESH_TOKEN_EXPIRE_DAYS = 7
+
+# Load stable secret from SQLite (set during first-time setup)
+# This will raise RuntimeError if auth hasn't been initialized yet
+try:
+    SECRET_KEY = load_jwt_secret()
+except RuntimeError:
+    # Fallback: use a temporary secret until setup is complete
+    # This allows the app to start, but protected routes will fail until setup
+    SECRET_KEY = secrets.token_urlsafe(64)
+
+security = HTTPBearer()  # Reads Authorization: Bearer <token>
+
+
+def create_access_token(
+    subject: str,
+    expires_delta: Optional[timedelta] = None,
+) -> str:
+    """
+    Create a signed JWT for the given subject (e.g. username).
+
+    Tokens are valid across restarts because SECRET_KEY is stored in SQLite.
+    """
+    to_encode = {"sub": subject}
+    expire = datetime.now(timezone.utc) + (
+        expires_delta or timedelta(minutes = ACCESS_TOKEN_EXPIRE_MINUTES)
+    )
+    to_encode.update({"exp": expire})
+    return jwt.encode(to_encode, SECRET_KEY, algorithm = ALGORITHM)
+
+
+def create_refresh_token(subject: str) -> str:
+    """
+    Create a random refresh token, store its hash in SQLite, and return it.
+
+    Refresh tokens are opaque (not JWTs) and expire after REFRESH_TOKEN_EXPIRE_DAYS.
+    """
+    token = secrets.token_urlsafe(48)
+    expires_at = datetime.now(timezone.utc) + timedelta(days = REFRESH_TOKEN_EXPIRE_DAYS)
+    save_refresh_token(token, subject, expires_at.isoformat())
+    return token
+
+
+def refresh_access_token(refresh_token: str) -> Optional[str]:
+    """
+    Validate a refresh token and issue a new access token.
+
+    The refresh token itself is NOT consumed — it stays valid until expiry.
+    Returns a new access_token or None if the refresh token is invalid/expired.
+    """
+    username = verify_refresh_token(refresh_token)
+    if username is None:
+        return None
+    return create_access_token(subject = username)
+
+
+def reload_secret() -> None:
+    """
+    Reload the JWT secret from SQLite.
+
+    Call this after setup to ensure new tokens use the persistent secret.
+    """
+    global SECRET_KEY
+    SECRET_KEY = load_jwt_secret()
+
+
+async def get_current_subject(
+    credentials: HTTPAuthorizationCredentials = Depends(security),
+) -> str:
+    """
+    FastAPI dependency to validate the JWT and return the subject.
+
+    Use this as a dependency on routes that should be protected, e.g.:
+
+        @router.get("/secure")
+        async def secure_endpoint(current_subject: str = Depends(get_current_subject)):
+            ...
+    """
+    token = credentials.credentials
+    try:
+        payload = jwt.decode(token, SECRET_KEY, algorithms = [ALGORITHM])
+        subject: Optional[str] = payload.get("sub")
+        if subject is None:
+            raise HTTPException(
+                status_code = status.HTTP_401_UNAUTHORIZED,
+                detail = "Invalid token payload",
+            )
+        return subject
+    except jwt.InvalidTokenError:
+        raise HTTPException(
+            status_code = status.HTTP_401_UNAUTHORIZED,
+            detail = "Invalid or expired token",
+        )

studio/backend/auth/hashing.py

@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""
+Password hashing utilities using PBKDF2.
+"""
+
+import hashlib
+import hmac
+import secrets
+from typing import Tuple
+
+
+def hash_password(password: str, salt: str | None = None) -> Tuple[str, str]:
+    """
+    Hash a password using PBKDF2-HMAC-SHA256.
+
+    Returns (salt, hex_hash) tuple.
+    """
+    if salt is None:
+        salt = secrets.token_hex(16)
+    dk = hashlib.pbkdf2_hmac(
+        "sha256",
+        password.encode("utf-8"),
+        salt.encode("utf-8"),
+        100_000,  # 100k iterations
+    )
+    return salt, dk.hex()
+
+
+def verify_password(password: str, salt: str, hashed: str) -> bool:
+    """
+    Verify a password against a stored salt and hash.
+
+    Uses constant-time comparison to prevent timing attacks.
+    """
+    dk = hashlib.pbkdf2_hmac(
+        "sha256",
+        password.encode("utf-8"),
+        salt.encode("utf-8"),
+        100_000,
+    )
+    return hmac.compare_digest(dk.hex(), hashed)

studio/backend/auth/storage.py

@@ -0,0 +1,263 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""
+SQLite storage for authentication data (user credentials + JWT secret).
+"""
+
+import hashlib
+import sqlite3
+from datetime import datetime, timezone
+from typing import Optional, Tuple
+
+from utils.paths import auth_db_path, ensure_dir
+
+DB_PATH = auth_db_path()
+
+
+def _hash_token(token: str) -> str:
+    """SHA-256 hash a setup token for safe storage."""
+    return hashlib.sha256(token.encode("utf-8")).hexdigest()
+
+
+def get_connection() -> sqlite3.Connection:
+    """Get a connection to the auth database, creating tables if needed."""
+    ensure_dir(DB_PATH.parent)
+    conn = sqlite3.connect(DB_PATH)
+    conn.row_factory = sqlite3.Row
+    conn.execute(
+        """
+        CREATE TABLE IF NOT EXISTS auth_user (
+            id INTEGER PRIMARY KEY,
+            username TEXT UNIQUE NOT NULL,
+            password_salt TEXT NOT NULL,
+            password_hash TEXT NOT NULL,
+            jwt_secret TEXT NOT NULL
+        );
+        """
+    )
+    conn.execute(
+        """
+        CREATE TABLE IF NOT EXISTS setup_tokens (
+            id INTEGER PRIMARY KEY,
+            token_hash TEXT NOT NULL
+        );
+        """
+    )
+    conn.execute(
+        """
+        CREATE TABLE IF NOT EXISTS refresh_tokens (
+            id INTEGER PRIMARY KEY,
+            token_hash TEXT NOT NULL,
+            username TEXT NOT NULL,
+            expires_at TEXT NOT NULL
+        );
+        """
+    )
+    conn.commit()
+    return conn
+
+
+def is_initialized() -> bool:
+    """Check if auth has been set up (user exists in DB)."""
+    conn = get_connection()
+    cur = conn.execute("SELECT COUNT(*) AS c FROM auth_user")
+    row = cur.fetchone()
+    conn.close()
+    return bool(row["c"])
+
+
+def create_initial_user(username: str, password: str, jwt_secret: str) -> None:
+    """
+    Create the initial admin user in the database.
+
+    Raises sqlite3.IntegrityError if username already exists.
+    """
+    from .hashing import hash_password
+
+    salt, pwd_hash = hash_password(password)
+    conn = get_connection()
+    try:
+        conn.execute(
+            """
+            INSERT INTO auth_user (username, password_salt, password_hash, jwt_secret)
+            VALUES (?, ?, ?, ?)
+            """,
+            (username, salt, pwd_hash, jwt_secret),
+        )
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def delete_user(username: str) -> None:
+    """
+    Delete a user from the database.
+
+    Used for rollback when setup fails after user creation.
+    """
+    conn = get_connection()
+    try:
+        conn.execute("DELETE FROM auth_user WHERE username = ?", (username,))
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def get_user_and_secret(username: str) -> Optional[Tuple[str, str, str]]:
+    """
+    Get user's password salt, hash, and JWT secret.
+
+    Returns (password_salt, password_hash, jwt_secret) or None if user not found.
+    """
+    conn = get_connection()
+    try:
+        cur = conn.execute(
+            """
+            SELECT password_salt, password_hash, jwt_secret
+            FROM auth_user
+            WHERE username = ?
+            """,
+            (username,),
+        )
+        row = cur.fetchone()
+        if not row:
+            return None
+        return row["password_salt"], row["password_hash"], row["jwt_secret"]
+    finally:
+        conn.close()
+
+
+def load_jwt_secret() -> str:
+    """
+    Load the JWT secret from the database.
+
+    Raises RuntimeError if auth is not initialized.
+    """
+    conn = get_connection()
+    try:
+        cur = conn.execute("SELECT jwt_secret FROM auth_user LIMIT 1")
+        row = cur.fetchone()
+        if not row:
+            raise RuntimeError(
+                "Auth is not initialized. Please set up a password first."
+            )
+        return row["jwt_secret"]
+    finally:
+        conn.close()
+
+
+def save_setup_token(token: str) -> None:
+    """
+    Store a hashed setup token, replacing any existing one.
+    """
+    token_hash = _hash_token(token)
+    conn = get_connection()
+    try:
+        conn.execute("DELETE FROM setup_tokens")
+        conn.execute("INSERT INTO setup_tokens (token_hash) VALUES (?)", (token_hash,))
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def consume_setup_token(token: str) -> bool:
+    """
+    Verify a setup token and delete it if valid.
+
+    Returns True if the token was valid (and is now consumed), False otherwise.
+    """
+    token_hash = _hash_token(token)
+    conn = get_connection()
+    try:
+        cur = conn.execute(
+            "SELECT id FROM setup_tokens WHERE token_hash = ?", (token_hash,)
+        )
+        row = cur.fetchone()
+        if row is None:
+            return False
+        conn.execute("DELETE FROM setup_tokens WHERE id = ?", (row["id"],))
+        conn.commit()
+        return True
+    finally:
+        conn.close()
+
+
+def has_pending_setup_token() -> bool:
+    """Check if a setup token is waiting to be consumed."""
+    conn = get_connection()
+    try:
+        cur = conn.execute("SELECT COUNT(*) AS c FROM setup_tokens")
+        row = cur.fetchone()
+        return bool(row["c"])
+    finally:
+        conn.close()
+
+
+def save_refresh_token(token: str, username: str, expires_at: str) -> None:
+    """
+    Store a hashed refresh token with its associated username and expiry.
+    """
+    token_hash = _hash_token(token)
+    conn = get_connection()
+    try:
+        conn.execute(
+            """
+            INSERT INTO refresh_tokens (token_hash, username, expires_at)
+            VALUES (?, ?, ?)
+            """,
+            (token_hash, username, expires_at),
+        )
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def verify_refresh_token(token: str) -> Optional[str]:
+    """
+    Verify a refresh token and return the username.
+
+    Returns the username if valid and not expired, None otherwise.
+    The token is NOT consumed — it stays valid until it expires.
+    """
+    token_hash = _hash_token(token)
+    conn = get_connection()
+    try:
+        # Clean up any expired tokens while we're here
+        conn.execute(
+            "DELETE FROM refresh_tokens WHERE expires_at < ?",
+            (datetime.now(timezone.utc).isoformat(),),
+        )
+        conn.commit()
+
+        cur = conn.execute(
+            """
+            SELECT id, username, expires_at FROM refresh_tokens
+            WHERE token_hash = ?
+            """,
+            (token_hash,),
+        )
+        row = cur.fetchone()
+        if row is None:
+            return None
+
+        # Check expiry
+        expires_at = datetime.fromisoformat(row["expires_at"])
+        if datetime.now(timezone.utc) > expires_at:
+            conn.execute("DELETE FROM refresh_tokens WHERE id = ?", (row["id"],))
+            conn.commit()
+            return None
+
+        return row["username"]
+    finally:
+        conn.close()
+
+
+def revoke_user_refresh_tokens(username: str) -> None:
+    """Revoke all refresh tokens for a user (e.g. on logout)."""
+    conn = get_connection()
+    try:
+        conn.execute("DELETE FROM refresh_tokens WHERE username = ?", (username,))
+        conn.commit()
+    finally:
+        conn.close()

studio/backend/colab.py

@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""
+Colab-specific helpers for running Unsloth Studio.
+Uses Colab's built-in proxy - no external tunneling needed!
+"""
+
+from pathlib import Path
+import sys
+
+# Add backend to path early so local modules like loggers can be imported
+backend_path = str(Path(__file__).parent)
+if backend_path not in sys.path:
+    sys.path.insert(0, backend_path)
+
+from loggers import get_logger
+
+logger = get_logger(__name__)
+
+
+def get_colab_url(port: int = 8000) -> str:
+    """
+    Get the actual Colab proxy URL for a port.
+    """
+    try:
+        from google.colab.output import eval_js
+
+        # Use Colab's proxy mechanism
+        url = eval_js(f"google.colab.kernel.proxyPort({port})", timeout_sec = 5)
+        return url if url else f"http://localhost:{port}"
+    except Exception as e:
+        logger.info(f"Note: Could not get Colab URL ({e})")
+        return f"http://localhost:{port}"
+
+
+def show_link(port: int = 8000):
+    """Display a styled clickable link to the UI."""
+    from IPython.display import display, HTML
+
+    # Get real Colab proxy URL
+    url = get_colab_url(port)
+
+    html = f"""
+    <div style="padding: 20px; background: linear-gradient(135deg, #22c55e 0%, #16a34a 100%);
+                border-radius: 12px; margin: 10px 0; font-family: system-ui, -apple-system, sans-serif;">
+        <h2 style="color: white; margin: 0 0 12px 0; font-size: 24px;">
+            🦥 Unsloth Studio is Ready!
+        </h2>
+        <a href="{url}" target="_blank"
+           style="display: inline-block; padding: 14px 28px; background: white; color: #16a34a;
+                  text-decoration: none; border-radius: 8px; font-weight: 600; font-size: 16px;
+                  box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
+            🚀 Open Unsloth Studio
+        </a>
+        <p style="color: rgba(255,255,255,0.9); margin: 16px 0 0 0; font-size: 13px;
+                  word-break: break-all; font-family: monospace;">
+            {url}
+        </p>
+    </div>
+    """
+    display(HTML(html))
+
+
+def start(port: int = 8000):
+    """
+    Start Unsloth Studio server in Colab and display the URL.
+
+    Usage:
+        from colab import start
+        start()
+    """
+    import sys
+
+    logger.info("🦥 Starting Unsloth Studio...")
+
+    logger.info("   Loading backend...")
+    from run import run_server
+
+    # Auto-detect frontend path
+    repo_root = Path(__file__).parent.parent
+    frontend_path = repo_root / "frontend" / "dist"
+
+    if not frontend_path.exists():
+        logger.info("❌ Frontend not built! Please run the setup cell first.")
+        return
+
+    logger.info("   Starting server...")
+    # Start server silently
+    run_server(host = "0.0.0.0", port = port, frontend_path = frontend_path, silent = True)
+
+    logger.info("   Server started!")
+
+    # Show the clickable link with real URL
+    show_link(port)
+
+
+if __name__ == "__main__":
+    start()

studio/backend/core/__init__.py

@@ -0,0 +1,134 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""
+Unified core module for Unsloth backend
+
+Imports are LAZY (via __getattr__) so that training subprocesses can
+import core.training.worker without pulling in heavy ML dependencies
+like unsloth, transformers, or torch before the version activation
+code has a chance to run.
+"""
+
+__all__ = [
+    # Inference
+    "InferenceBackend",
+    "get_inference_backend",
+    # Training
+    "get_training_backend",
+    "TrainingBackend",
+    "TrainingProgress",
+    # Config
+    "ModelConfig",
+    "is_vision_model",
+    "scan_trained_loras",
+    "load_model_defaults",
+    "get_base_model_from_lora",
+    # Utils
+    "format_and_template_dataset",
+    "normalize_path",
+    "is_local_path",
+    "is_model_cached",
+    "without_hf_auth",
+    "format_error_message",
+    "get_gpu_memory_info",
+    "log_gpu_memory",
+    "get_device",
+    "is_apple_silicon",
+    "clear_gpu_cache",
+    "DeviceType",
+]
+
+
+def __getattr__(name):
+    # Inference
+    if name in ("InferenceBackend", "get_inference_backend"):
+        from .inference import InferenceBackend, get_inference_backend
+
+        globals()["InferenceBackend"] = InferenceBackend
+        globals()["get_inference_backend"] = get_inference_backend
+        return globals()[name]
+
+    # Training
+    if name in ("TrainingBackend", "get_training_backend", "TrainingProgress"):
+        from .training import TrainingBackend, get_training_backend, TrainingProgress
+
+        globals()["TrainingBackend"] = TrainingBackend
+        globals()["get_training_backend"] = get_training_backend
+        globals()["TrainingProgress"] = TrainingProgress
+        return globals()[name]
+
+    # Config (from utils.models)
+    if name in (
+        "is_vision_model",
+        "ModelConfig",
+        "scan_trained_loras",
+        "load_model_defaults",
+        "get_base_model_from_lora",
+    ):
+        from utils.models import (
+            is_vision_model,
+            ModelConfig,
+            scan_trained_loras,
+            load_model_defaults,
+            get_base_model_from_lora,
+        )
+
+        globals()["is_vision_model"] = is_vision_model
+        globals()["ModelConfig"] = ModelConfig
+        globals()["scan_trained_loras"] = scan_trained_loras
+        globals()["load_model_defaults"] = load_model_defaults
+        globals()["get_base_model_from_lora"] = get_base_model_from_lora
+        return globals()[name]
+
+    # Paths
+    if name in ("normalize_path", "is_local_path", "is_model_cached"):
+        from utils.paths import normalize_path, is_local_path, is_model_cached
+
+        globals()["normalize_path"] = normalize_path
+        globals()["is_local_path"] = is_local_path
+        globals()["is_model_cached"] = is_model_cached
+        return globals()[name]
+
+    # Utils
+    if name in ("without_hf_auth", "format_error_message"):
+        from utils.utils import without_hf_auth, format_error_message
+
+        globals()["without_hf_auth"] = without_hf_auth
+        globals()["format_error_message"] = format_error_message
+        return globals()[name]
+
+    # Hardware
+    if name in (
+        "get_device",
+        "is_apple_silicon",
+        "clear_gpu_cache",
+        "get_gpu_memory_info",
+        "log_gpu_memory",
+        "DeviceType",
+    ):
+        from utils.hardware import (
+            get_device,
+            is_apple_silicon,
+            clear_gpu_cache,
+            get_gpu_memory_info,
+            log_gpu_memory,
+            DeviceType,
+        )
+
+        globals()["get_device"] = get_device
+        globals()["is_apple_silicon"] = is_apple_silicon
+        globals()["clear_gpu_cache"] = clear_gpu_cache
+        globals()["get_gpu_memory_info"] = get_gpu_memory_info
+        globals()["log_gpu_memory"] = log_gpu_memory
+        globals()["DeviceType"] = DeviceType
+        return globals()[name]
+
+    # Datasets
+    if name == "format_and_template_dataset":
+        from utils.datasets import format_and_template_dataset
+
+        globals()["format_and_template_dataset"] = format_and_template_dataset
+        return format_and_template_dataset
+
+    raise AttributeError(f"module 'core' has no attribute {name!r}")

studio/backend/core/data_recipe/__init__.py

@@ -0,0 +1,10 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""
+Data Recipe core (DataDesigner wrapper + job runner).
+"""
+
+from .jobs import JobManager, get_job_manager
+
+__all__ = ["JobManager", "get_job_manager"]

studio/backend/core/data_recipe/jobs/__init__.py

@@ -0,0 +1,6 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+from .manager import JobManager, get_job_manager
+
+__all__ = ["JobManager", "get_job_manager"]

studio/backend/core/data_recipe/jobs/constants.py

@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+from __future__ import annotations
+
+# stages parsed from data-designer logs
+STAGE_CREATE = "create"
+STAGE_PREVIEW = "preview"
+STAGE_DAG = "dag"
+STAGE_HEALTHCHECK = "healthcheck"
+STAGE_SAMPLING = "sampling"
+STAGE_COLUMN_CONFIG = "column_config"
+STAGE_GENERATING = "generating"
+STAGE_BATCH = "batch"
+STAGE_PROFILING = "profiling"
+
+USAGE_RESET_STAGES = {
+    STAGE_CREATE,
+    STAGE_PREVIEW,
+    STAGE_DAG,
+    STAGE_HEALTHCHECK,
+    STAGE_SAMPLING,
+    STAGE_GENERATING,
+    STAGE_PROFILING,
+}
+
+# job event types emitted by worker/manager
+EVENT_JOB_ENQUEUED = "job.enqueued"
+EVENT_JOB_STARTED = "job.started"
+EVENT_JOB_CANCELLING = "job.cancelling"
+EVENT_JOB_CANCELLED = "job.cancelled"
+EVENT_JOB_COMPLETED = "job.completed"
+EVENT_JOB_ERROR = "job.error"