diff --git a/datasets/recipes/recipe_a28d33f9eab14660bba2437a6dfda290/builder_config.json b/datasets/recipes/recipe_a28d33f9eab14660bba2437a6dfda290/builder_config.json new file mode 100644 index 0000000000..c204eab0fa --- /dev/null +++ b/datasets/recipes/recipe_a28d33f9eab14660bba2437a6dfda290/builder_config.json @@ -0,0 +1,72 @@ +{ + "data_designer": { + "columns": [ + { + "name": "generated_instruction", + "drop": false, + "allow_resize": false, + "column_type": "llm-text", + "prompt": "Based on this target answer:\n{{ output }}\n\nWrite one high-quality plain text short and brief user instruction that this answer would satisfy.\nReturn only the instruction.", + "model_alias": "ministral", + "system_prompt": null, + "multi_modal_context": null, + "tool_alias": null, + "with_trace": "none", + "extract_reasoning_content": false + }, + { + "name": "instruction", + "drop": false, + "allow_resize": false, + "column_type": "seed-dataset" + }, + { + "name": "input", + "drop": false, + "allow_resize": false, + "column_type": "seed-dataset" + }, + { + "name": "output", + "drop": false, + "allow_resize": false, + "column_type": "seed-dataset" + } + ], + "model_configs": [ + { + "alias": "ministral", + "model": "mistralai/ministral-8b-2512", + "inference_parameters": { + "generation_type": "chat-completion", + "max_parallel_requests": 4, + "timeout": null, + "extra_body": null, + "temperature": 0.7, + "top_p": null, + "max_tokens": 1024 + }, + "provider": "openai_provider", + "skip_health_check": false + } + ], + "tool_configs": [], + "seed_config": { + "source": { + "seed_type": "hf", + "path": "datasets/unsloth/alpaca-cleaned/**/*.json", + "token": null, + "endpoint": "https://huggingface.co" + }, + "sampling_strategy": "ordered", + "selection_strategy": { + "start": 1, + "end": 100 + } + }, + "constraints": null, + "profilers": null, + "processors": null + }, + "library_version": "0.5.1" +} \ No newline at end of file diff --git a/datasets/recipes/recipe_a28d33f9eab14660bba2437a6dfda290/metadata.json b/datasets/recipes/recipe_a28d33f9eab14660bba2437a6dfda290/metadata.json new file mode 100644 index 0000000000..4b84f8d0f4 --- /dev/null +++ b/datasets/recipes/recipe_a28d33f9eab14660bba2437a6dfda290/metadata.json @@ -0,0 +1,36 @@ +{ + "actual_num_records": 50, + "buffer_size": 50, + "column_statistics": [ + { + "column_name": "generated_instruction", + "column_type": "llm-text", + "input_tokens_mean": 170.52, + "input_tokens_median": 132.5, + "input_tokens_stddev": 119.91, + "num_null": 0, + "num_records": 50, + "num_unique": 50, + "output_tokens_mean": 39.74, + "output_tokens_median": 31.0, + "output_tokens_stddev": 39.58, + "pyarrow_dtype": "string", + "simple_dtype": "string" + } + ], + "dataset_name": "recipe_a28d33f9eab14660bba2437a6dfda290", + "file_paths": { + "parquet-files": [ + "parquet-files/batch_00000.parquet" + ] + }, + "num_completed_batches": 1, + "schema": { + "generated_instruction": "string", + "input": "string", + "instruction": "string", + "output": "string" + }, + "target_num_records": 50, + "total_num_batches": 1 +} \ No newline at end of file diff --git a/datasets/recipes/recipe_a28d33f9eab14660bba2437a6dfda290/parquet-files/batch_00000.parquet b/datasets/recipes/recipe_a28d33f9eab14660bba2437a6dfda290/parquet-files/batch_00000.parquet new file mode 100644 index 0000000000..06eebafa26 Binary files /dev/null and b/datasets/recipes/recipe_a28d33f9eab14660bba2437a6dfda290/parquet-files/batch_00000.parquet differ diff --git a/studio/backend/core/export/export.py b/studio/backend/core/export/export.py index da5b11c60d..231566cc22 100644 --- a/studio/backend/core/export/export.py +++ b/studio/backend/core/export/export.py @@ -2,6 +2,7 @@ """ Export backend - handles model exporting in various formats """ +import json import logging import os from pathlib import Path @@ -200,6 +201,18 @@ class ExportBackend: logger.error(traceback.format_exc()) return False, f"Failed to load checkpoint: {str(e)}" + def _write_export_metadata(self, save_directory: str): + """Write export_metadata.json with base model info for Chat page discovery.""" + try: + base_model = get_base_model_from_lora(self.current_checkpoint) if self.current_checkpoint else None + metadata = {"base_model": base_model} + metadata_path = os.path.join(save_directory, "export_metadata.json") + with open(metadata_path, "w") as f: + json.dump(metadata, f, indent=2) + logger.info(f"Wrote export metadata to {metadata_path}") + except Exception as e: + logger.warning(f"Could not write export metadata: {e}") + def export_merged_model(self, save_directory: str, format_type: str = "16-bit (FP16)", @@ -244,6 +257,9 @@ class ExportBackend: self.current_tokenizer, save_method=save_method ) + + # Write export metadata so the Chat page can identify the base model + self._write_export_metadata(save_directory) logger.info(f"Model saved successfully to {save_directory}") # Push to hub if requested @@ -297,6 +313,9 @@ class ExportBackend: self.current_model.save_pretrained(save_directory) self.current_tokenizer.save_pretrained(save_directory) + + # Write export metadata so the Chat page can identify the base model + self._write_export_metadata(save_directory) logger.info(f"Model saved successfully to {save_directory}") # Push to hub if requested diff --git a/studio/backend/core/inference/inference.py b/studio/backend/core/inference/inference.py index e90e6c0c2a..1147c281b7 100644 --- a/studio/backend/core/inference/inference.py +++ b/studio/backend/core/inference/inference.py @@ -6,8 +6,10 @@ from unsloth.chat_templates import get_chat_template from transformers import TextStreamer from peft import PeftModel, PeftModelForCausalLM +import json import sys import torch +from pathlib import Path from typing import Optional, Union, Generator, Tuple from utils.models import ModelConfig, get_base_model_from_lora from utils.paths import is_model_cached @@ -112,7 +114,18 @@ class InferenceBackend: # In that case, load the real processor from the base model. from transformers import ProcessorMixin if not (isinstance(processor, ProcessorMixin) or hasattr(processor, "image_processor")): + # For LoRA adapters, use the base model. For local merged exports, + # read export_metadata.json to find the original base model. processor_source = config.base_model if config.is_lora else config.identifier + if not config.is_lora and config.is_local: + _meta_path = Path(config.path) / "export_metadata.json" + try: + if _meta_path.exists(): + _meta = json.loads(_meta_path.read_text()) + if _meta.get("base_model"): + processor_source = _meta["base_model"] + except Exception: + pass logger.warning( f"FastVisionModel returned {type(processor).__name__} (no image_processor) " f"for '{model_name}' — loading proper processor from '{processor_source}'" diff --git a/studio/backend/core/training/trainer.py b/studio/backend/core/training/trainer.py index 804baa33c0..43cb5ff1a1 100644 --- a/studio/backend/core/training/trainer.py +++ b/studio/backend/core/training/trainer.py @@ -343,7 +343,8 @@ class UnslothTrainer: custom_format_mapping: dict = None, subset: str = None, train_split: str = "train", - eval_split: str = None) -> Optional[tuple]: + eval_split: str = None, + eval_steps: float = 0.00) -> Optional[tuple]: """ Load and prepare dataset for training. @@ -358,6 +359,7 @@ class UnslothTrainer: dataset = None eval_dataset = None has_separate_eval_source = False # True if eval comes from a separate HF split + eval_enabled = eval_steps is not None and eval_steps > 0 if local_datasets: # Load local datasets @@ -410,23 +412,26 @@ class UnslothTrainer: print(f"Loaded dataset from Hugging Face: {dataset_source}\n") # Resolve eval split from a separate HF split (explicit or auto-detected) - if eval_split: - # Explicit eval split provided - load it directly - print(f"Loading explicit eval split: '{eval_split}'\n") - eval_load_kwargs = {"path": dataset_source, "split": eval_split} - if subset: - eval_load_kwargs["name"] = subset - eval_dataset = load_dataset(**eval_load_kwargs) - has_separate_eval_source = True - print(f"Loaded eval split '{eval_split}' with {len(eval_dataset)} rows\n") - else: - # Auto-detect eval split from HF (returns a separate dataset, or None) - eval_dataset = self._auto_detect_eval_split_from_hf( - dataset_source=dataset_source, - subset=subset, - ) - if eval_dataset is not None: + if eval_enabled: + if eval_split: + # Explicit eval split provided - load it directly + print(f"Loading explicit eval split: '{eval_split}'\n") + eval_load_kwargs = {"path": dataset_source, "split": eval_split} + if subset: + eval_load_kwargs["name"] = subset + eval_dataset = load_dataset(**eval_load_kwargs) has_separate_eval_source = True + print(f"Loaded eval split '{eval_split}' with {len(eval_dataset)} rows\n") + else: + # Auto-detect eval split from HF (returns a separate dataset, or None) + eval_dataset = self._auto_detect_eval_split_from_hf( + dataset_source=dataset_source, + subset=subset, + ) + if eval_dataset is not None: + has_separate_eval_source = True + else: + print("Eval disabled (eval_steps <= 0), skipping eval split detection\n") if dataset is None: raise ValueError("No dataset provided") @@ -472,7 +477,7 @@ class UnslothTrainer: ) eval_dataset = eval_info["dataset"] print(f"Eval dataset formatted successfully\n") - elif not has_separate_eval_source: + elif eval_enabled and not has_separate_eval_source: # No separate eval source — split the already-formatted dataset formatted_dataset = dataset_info["dataset"] split_result = self._resolve_eval_split_from_dataset(formatted_dataset) @@ -543,7 +548,7 @@ class UnslothTrainer: def start_training(self, dataset: Dataset, eval_dataset: Dataset = None, - eval_steps: float = 0.01, + eval_steps: float = 0.00, output_dir: str = "./outputs", num_epochs: int = 3, learning_rate: float = 5e-5, @@ -743,12 +748,16 @@ class UnslothTrainer: # ========== EVAL CONFIGURATION ========== eval_dataset = training_args.get('eval_dataset', None) - eval_steps_val = training_args.get('eval_steps', 0.01) + eval_steps_val = training_args.get('eval_steps', 0.00) if eval_dataset is not None: - config_args["eval_strategy"] = "steps" - config_args["eval_steps"] = eval_steps_val - print(f"Evaluation enabled: eval_steps={eval_steps_val} (fraction of total steps)\n") - print(f"Eval dataset: {len(eval_dataset)} rows\n") + if eval_steps_val > 0: + config_args["eval_strategy"] = "steps" + config_args["eval_steps"] = eval_steps_val + print(f"✅ Evaluation enabled: eval_steps={eval_steps_val} (fraction of total steps)\n") + print(f"Eval dataset: {len(eval_dataset)} rows\n") + else: + print(f"⚠️ Eval dataset provided but eval_steps={eval_steps_val} (disabled)\n") + print("To enable evaluation, set eval_steps > 0.0\n") else: print("No eval dataset — evaluation disabled\n") diff --git a/studio/backend/core/training/training.py b/studio/backend/core/training/training.py index 6fe08c2b9e..9123d36b39 100644 --- a/studio/backend/core/training/training.py +++ b/studio/backend/core/training/training.py @@ -115,7 +115,7 @@ class TrainingBackend: subset: str = None, train_split: str = "train", eval_split: str = None, - eval_steps: float = 0.01, + eval_steps: float = 0.00, is_dataset_multimodal: bool = False) -> bool: """ Start training. @@ -223,6 +223,7 @@ class TrainingBackend: subset=subset, train_split=train_split, eval_split=eval_split, + eval_steps=eval_steps, ) # Unpack: load_and_format_dataset returns (dataset, eval_dataset) @@ -232,10 +233,6 @@ class TrainingBackend: dataset = dataset_result eval_dataset = None - # If user set eval_steps to 0, disable evaluation entirely - if eval_steps is not None and float(eval_steps) <= 0: - eval_dataset = None - # Track whether eval is enabled for status reporting self.eval_enabled = eval_dataset is not None diff --git a/studio/backend/models/models.py b/studio/backend/models/models.py index 5542db76d5..10d87d7825 100644 --- a/studio/backend/models/models.py +++ b/studio/backend/models/models.py @@ -57,10 +57,12 @@ class ModelDetails(BaseModel): class LoRAInfo(BaseModel): - """LoRA adapter information""" + """LoRA adapter or exported model information""" display_name: str = Field(..., description="Display name for the LoRA") - adapter_path: str = Field(..., description="Path to the LoRA adapter") + adapter_path: str = Field(..., description="Path to the LoRA adapter or exported model") base_model: Optional[str] = Field(None, description="Base model identifier") + source: Optional[str] = Field(None, description="'training' or 'exported'") + export_type: Optional[str] = Field(None, description="'lora' or 'merged' (for exports)") class LoRAScanResponse(BaseModel): diff --git a/studio/backend/models/training.py b/studio/backend/models/training.py index 2b989e6a82..54de974100 100644 --- a/studio/backend/models/training.py +++ b/studio/backend/models/training.py @@ -21,7 +21,7 @@ class TrainingStartRequest(BaseModel): subset: Optional[str] = None train_split: Optional[str] = Field("train", description="Training split name") eval_split: Optional[str] = Field(None, description="Eval split name. None = auto-detect") - eval_steps: float = Field(0.01, description="Fraction of total steps between evals (0-1)") + eval_steps: float = Field(0.00, description="Fraction of total steps between evals (0-1)") @model_validator(mode="before") @classmethod diff --git a/studio/backend/routes/models.py b/studio/backend/routes/models.py index 761c04d3e7..8c96ee5f28 100644 --- a/studio/backend/routes/models.py +++ b/studio/backend/routes/models.py @@ -18,6 +18,7 @@ from auth.authentication import get_current_subject try: from utils.models import ( scan_trained_loras, + scan_exported_models, load_model_defaults, get_base_model_from_lora, is_vision_model, @@ -32,6 +33,7 @@ except ImportError: sys.path.insert(0, str(parent_backend)) from utils.models import ( scan_trained_loras, + scan_exported_models, load_model_defaults, get_base_model_from_lora, is_vision_model, @@ -289,35 +291,45 @@ async def get_model_config( @router.get("/loras") async def scan_loras( outputs_dir: str = Query(default="./outputs", description="Directory to scan for LoRA adapters"), + exports_dir: str = Query(default="./exports", description="Directory to scan for exported models"), current_subject: str = Depends(get_current_subject), ): """ - Scan for trained LoRA adapters in the outputs directory. - - This endpoint wraps the backend scan_trained_loras function. + Scan for trained LoRA adapters and exported models. + + Returns both training outputs (from outputs_dir) and exported models + (from exports_dir) in a single list, distinguished by source field. """ try: - # Call backend scan function - trained_loras = scan_trained_loras(outputs_dir=outputs_dir) - - # Convert to LoRAInfo objects lora_list = [] + + # Scan training outputs + trained_loras = scan_trained_loras(outputs_dir=outputs_dir) for display_name, adapter_path in trained_loras: - # Get base model if available base_model = get_base_model_from_lora(adapter_path) - - lora_info = LoRAInfo( + lora_list.append(LoRAInfo( display_name=display_name, adapter_path=adapter_path, - base_model=base_model - ) - lora_list.append(lora_info) - + base_model=base_model, + source="training", + )) + + # Scan exported models (merged, LoRA, base — skips GGUF) + exported = scan_exported_models(exports_dir=exports_dir) + for display_name, model_path, export_type, base_model in exported: + lora_list.append(LoRAInfo( + display_name=display_name, + adapter_path=model_path, + base_model=base_model, + source="exported", + export_type=export_type, + )) + return LoRAScanResponse( loras=lora_list, outputs_dir=outputs_dir ) - + except Exception as e: logger.error(f"Error scanning LoRAs: {e}", exc_info=True) raise HTTPException( diff --git a/studio/backend/utils/models/__init__.py b/studio/backend/utils/models/__init__.py index 505fd35edd..006deb99c0 100644 --- a/studio/backend/utils/models/__init__.py +++ b/studio/backend/utils/models/__init__.py @@ -5,6 +5,7 @@ from .model_config import ( ModelConfig, is_vision_model, scan_trained_loras, + scan_exported_models, load_model_defaults, get_base_model_from_lora, load_model_config, @@ -17,6 +18,7 @@ __all__ = [ 'ModelConfig', 'is_vision_model', 'scan_trained_loras', + 'scan_exported_models', 'load_model_defaults', 'get_base_model_from_lora', 'load_model_config', diff --git a/studio/backend/utils/models/model_config.py b/studio/backend/utils/models/model_config.py index fdf89fce39..f7b95fad11 100644 --- a/studio/backend/utils/models/model_config.py +++ b/studio/backend/utils/models/model_config.py @@ -465,6 +465,90 @@ def scan_trained_loras(outputs_dir: str = "./outputs") -> List[Tuple[str, str]]: logger.error(f"Error scanning outputs folder: {e}") return [] +def scan_exported_models(exports_dir: str = "./exports") -> List[Tuple[str, str, str, Optional[str]]]: + """ + Scan exports folder for exported models (merged, LoRA, base). + Skips GGUF-only exports (not loadable by Unsloth inference backend). + + The exports directory is two levels deep: {run}/{checkpoint}/ + + Returns: + List of tuples: [(display_name, model_path, export_type, base_model), ...] + export_type: "lora" | "merged" + """ + results = [] + exports_path = Path(exports_dir) + + if not exports_path.exists(): + return results + + try: + for run_dir in exports_path.iterdir(): + if not run_dir.is_dir(): + continue + for checkpoint_dir in run_dir.iterdir(): + if not checkpoint_dir.is_dir(): + continue + + adapter_config = checkpoint_dir / "adapter_config.json" + config_file = checkpoint_dir / "config.json" + has_weights = ( + any(checkpoint_dir.glob("*.safetensors")) + or any(checkpoint_dir.glob("*.bin")) + ) + has_gguf = any(checkpoint_dir.glob("*.gguf")) + + base_model = None + export_type = None + + if adapter_config.exists(): + export_type = "lora" + try: + cfg = json.loads(adapter_config.read_text()) + base_model = cfg.get("base_model_name_or_path") + except Exception: + pass + elif config_file.exists() and has_weights: + export_type = "merged" + # Read base model from export_metadata.json (written at export time) + export_meta = checkpoint_dir / "export_metadata.json" + try: + if export_meta.exists(): + meta = json.loads(export_meta.read_text()) + base_model = meta.get("base_model") + except Exception: + pass + elif has_gguf: + # GGUF-only — not loadable by current inference backend + continue + else: + continue + + # Fallback: read base model from the original training run's + # adapter_config.json in ./outputs/{run_name}/ + if not base_model: + outputs_adapter_cfg = Path("./outputs") / run_dir.name / "adapter_config.json" + try: + if outputs_adapter_cfg.exists(): + cfg = json.loads(outputs_adapter_cfg.read_text()) + base_model = cfg.get("base_model_name_or_path") + except Exception: + pass + + display_name = f"{run_dir.name} / {checkpoint_dir.name}" + model_path = str(checkpoint_dir) + results.append((display_name, model_path, export_type, base_model)) + logger.debug(f"Found exported model: {display_name} ({export_type})") + + results.sort(key=lambda x: Path(x[1]).stat().st_mtime, reverse=True) + logger.info(f"Found {len(results)} exported models in {exports_dir}") + return results + + except Exception as e: + logger.error(f"Error scanning exports folder: {e}") + return [] + + def get_base_model_from_lora(lora_path: str) -> Optional[str]: """ Read the base model name from a LoRA adapter's config. diff --git a/studio/frontend/bun.lock b/studio/frontend/bun.lock index 5b9c526480..f200156928 100644 --- a/studio/frontend/bun.lock +++ b/studio/frontend/bun.lock @@ -42,6 +42,7 @@ "date-fns": "^4.1.0", "dexie": "^4.3.0", "framer-motion": "^11.18.2", + "js-yaml": "^4.1.1", "katex": "^0.16.28", "lucide-react": "^0.575.0", "mammoth": "^1.11.0", @@ -68,6 +69,7 @@ "devDependencies": { "@biomejs/biome": "^1.9.4", "@eslint/js": "^9.39.1", + "@types/js-yaml": "^4.0.9", "@types/node": "^24.10.1", "@types/react": "^19.2.5", "@types/react-dom": "^19.2.3", @@ -87,15 +89,17 @@ "@antfu/ni": ["@antfu/ni@25.0.0", "", { "dependencies": { "ansis": "^4.0.0", "fzf": "^0.5.2", "package-manager-detector": "^1.3.0", "tinyexec": "^1.0.1" }, "bin": { "na": "bin/na.mjs", "ni": "bin/ni.mjs", "nr": "bin/nr.mjs", "nci": "bin/nci.mjs", "nlx": "bin/nlx.mjs", "nun": "bin/nun.mjs", "nup": "bin/nup.mjs" } }, "sha512-9q/yCljni37pkMr4sPrI3G4jqdIk074+iukc5aFJl7kmDCCsiJrbZ6zKxnES1Gwg+i9RcDZwvktl23puGslmvA=="], - 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"motion/framer-motion": ["framer-motion@12.34.0", "", { "dependencies": { "motion-dom": "^12.34.0", "motion-utils": "^12.29.2", "tslib": "^2.4.0" }, "peerDependencies": { "@emotion/is-prop-valid": "*", "react": "^18.0.0 || ^19.0.0", "react-dom": "^18.0.0 || ^19.0.0" }, "optionalPeers": ["@emotion/is-prop-valid", "react", "react-dom"] }, "sha512-+/H49owhzkzQyxtn7nZeF4kdH++I2FWrESQ184Zbcw5cEqNHYkE5yxWxcTLSj5lNx3NWdbIRy5FHqUvetD8FWg=="], + "motion/framer-motion": ["framer-motion@12.34.3", "", { "dependencies": { "motion-dom": "^12.34.3", "motion-utils": "^12.29.2", "tslib": "^2.4.0" }, "peerDependencies": { "@emotion/is-prop-valid": "*", "react": "^18.0.0 || ^19.0.0", "react-dom": "^18.0.0 || ^19.0.0" }, "optionalPeers": ["@emotion/is-prop-valid", "react", "react-dom"] }, "sha512-v81ecyZKYO/DfpTwHivqkxSUBzvceOpoI+wLfgCgoUIKxlFKEXdg0oR9imxwXumT4SFy8vRk9xzJ5l3/Du/55Q=="], "next/postcss": ["postcss@8.4.31", "", { "dependencies": { "nanoid": "^3.3.6", "picocolors": "^1.0.0", "source-map-js": "^1.0.2" } }, "sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ=="], @@ -2353,8 +2355,6 @@ "shadcn/commander": ["commander@14.0.3", "", {}, "sha512-H+y0Jo/T1RZ9qPP4Eh1pkcQcLRglraJaSLoyOtHxu6AapkjWVCy2Sit1QQ4x3Dng8qDlSsZEet7g5Pq06MvTgw=="], - "shadcn/zod": ["zod@3.25.76", "", {}, "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ=="], - "sharp/semver": ["semver@7.7.4", "", { "bin": { "semver": "bin/semver.js" } }, "sha512-vFKC2IEtQnVhpT78h1Yp8wzwrf8CM+MzKMHGJZfBtzhZNycRFnXsHk6E5TxIkkMsgNS7mdX3AGB7x2QM2di4lA=="], "string-width/strip-ansi": ["strip-ansi@6.0.1", "", { "dependencies": { "ansi-regex": "^5.0.1" } }, "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A=="], @@ -2441,9 +2441,9 @@ "@toolwind/corner-shape/@types/node/undici-types": ["undici-types@6.21.0", "", {}, "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ=="], - "@ts-morph/common/minimatch/brace-expansion": ["brace-expansion@5.0.2", "", { "dependencies": { "balanced-match": "^4.0.2" } }, "sha512-Pdk8c9poy+YhOgVWw1JNN22/HcivgKWwpxKq04M/jTmHyCZn12WPJebZxdjSa5TmBqISrUSgNYU3eRORljfCCw=="], + "@ts-morph/common/minimatch/brace-expansion": ["brace-expansion@5.0.3", "", { "dependencies": { "balanced-match": "^4.0.2" } }, "sha512-fy6KJm2RawA5RcHkLa1z/ScpBeA762UF9KmZQxwIbDtRJrgLzM10depAiEQ+CXYcoiqW1/m96OAAoke2nE9EeA=="], - "@typescript-eslint/typescript-estree/minimatch/brace-expansion": ["brace-expansion@2.0.2", "", { "dependencies": { "balanced-match": "^1.0.0" } }, "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ=="], + "@typescript-eslint/typescript-estree/minimatch/brace-expansion": ["brace-expansion@5.0.3", "", { "dependencies": { "balanced-match": "^4.0.2" } }, "sha512-fy6KJm2RawA5RcHkLa1z/ScpBeA762UF9KmZQxwIbDtRJrgLzM10depAiEQ+CXYcoiqW1/m96OAAoke2nE9EeA=="], "ajv-formats/ajv/json-schema-traverse": ["json-schema-traverse@1.0.0", "", {}, "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug=="], @@ -2451,13 +2451,15 @@ "cliui/wrap-ansi/ansi-styles": ["ansi-styles@4.3.0", "", { "dependencies": { "color-convert": "^2.0.1" } }, "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg=="], + "cmdk/@radix-ui/react-primitive/@radix-ui/react-slot": ["@radix-ui/react-slot@1.2.3", "", { "dependencies": { "@radix-ui/react-compose-refs": "1.1.2" }, "peerDependencies": { "@types/react": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react"] }, "sha512-aeNmHnBxbi2St0au6VBVC7JXFlhLlOnvIIlePNniyUNAClzmtAUEY8/pBiK3iHjufOlwA+c20/8jngo7xcrg8A=="], + "cytoscape-fcose/cose-base/layout-base": ["layout-base@2.0.1", "", {}, "sha512-dp3s92+uNI1hWIpPGH3jK2kxE2lMjdXdr+DH8ynZHpd6PUlH6x6cbuXnoMmiNumznqaNO31xu9e79F0uuZ0JFg=="], "d3-sankey/d3-array/internmap": ["internmap@1.0.1", "", {}, "sha512-lDB5YccMydFBtasVtxnZ3MRBHuaoE8GKsppq+EchKL2U4nK/DmEpPHNH8MZe5HkMtpSiTSOZwfN0tzYjO/lJEw=="], "d3-sankey/d3-shape/d3-path": ["d3-path@1.0.9", "", {}, "sha512-VLaYcn81dtHVTjEHd8B+pbe9yHWpXKZUC87PzoFmsFrJqgFwDe/qxfp5MlfsfM1V5E/iVt0MmEbWQ7FVIXh/bg=="], - "motion/framer-motion/motion-dom": ["motion-dom@12.34.0", "", { "dependencies": { "motion-utils": "^12.29.2" } }, "sha512-Lql3NuEcScRDxTAO6GgUsRHBZOWI/3fnMlkMcH5NftzcN37zJta+bpbMAV9px4Nj057TuvRooMK7QrzMCgtz6Q=="], + "motion/framer-motion/motion-dom": ["motion-dom@12.34.3", "", { "dependencies": { "motion-utils": "^12.29.2" } }, "sha512-sYgFe+pR9aIM7o4fhs2aXtOI+oqlUd33N9Yoxcgo1Fv7M20sRkHtCmzE/VRNIcq7uNJ+qio+Xubt1FXH3pQ+eQ=="], "motion/framer-motion/motion-utils": ["motion-utils@12.29.2", "", {}, "sha512-G3kc34H2cX2gI63RqU+cZq+zWRRPSsNIOjpdl9TN4AQwC4sgwYPl/Q/Obf/d53nOm569T0fYK+tcoSV50BWx8A=="], @@ -2469,6 +2471,8 @@ "wrap-ansi/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="], - "@ts-morph/common/minimatch/brace-expansion/balanced-match": ["balanced-match@4.0.2", "", { "dependencies": { "jackspeak": "^4.2.3" } }, "sha512-x0K50QvKQ97fdEz2kPehIerj+YTeptKF9hyYkKf6egnwmMWAkADiO0QCzSp0R5xN8FTZgYaBfSaue46Ej62nMg=="], + "@ts-morph/common/minimatch/brace-expansion/balanced-match": ["balanced-match@4.0.4", "", {}, "sha512-BLrgEcRTwX2o6gGxGOCNyMvGSp35YofuYzw9h1IMTRmKqttAZZVU67bdb9Pr2vUHA8+j3i2tJfjO6C6+4myGTA=="], + + "@typescript-eslint/typescript-estree/minimatch/brace-expansion/balanced-match": ["balanced-match@4.0.4", "", {}, "sha512-BLrgEcRTwX2o6gGxGOCNyMvGSp35YofuYzw9h1IMTRmKqttAZZVU67bdb9Pr2vUHA8+j3i2tJfjO6C6+4myGTA=="], } } diff --git a/studio/frontend/package.json b/studio/frontend/package.json index b4f1dea296..f4b636c2c6 100644 --- a/studio/frontend/package.json +++ b/studio/frontend/package.json @@ -50,6 +50,7 @@ "date-fns": "^4.1.0", "dexie": "^4.3.0", "framer-motion": "^11.18.2", + "js-yaml": "^4.1.1", "katex": "^0.16.28", "lucide-react": "^0.575.0", "mammoth": "^1.11.0", @@ -76,6 +77,7 @@ "devDependencies": { "@biomejs/biome": "^1.9.4", "@eslint/js": "^9.39.1", + "@types/js-yaml": "^4.0.9", "@types/node": "^24.10.1", "@types/react": "^19.2.5", "@types/react-dom": "^19.2.3", @@ -88,4 +90,4 @@ "typescript-eslint": "^8.55.0", "vite": "^7.3.1" } -} +} \ No newline at end of file diff --git a/studio/frontend/src/components/assistant-ui/model-selector.tsx b/studio/frontend/src/components/assistant-ui/model-selector.tsx index bf5f219558..d470034832 100644 --- a/studio/frontend/src/components/assistant-ui/model-selector.tsx +++ b/studio/frontend/src/components/assistant-ui/model-selector.tsx @@ -62,7 +62,7 @@ function ModelSelectorTrigger({ className={cn( "flex items-center gap-2 transition-colors", variant === "outline" && - "rounded-full border border-border/60 hover:bg-accent", + "rounded-full border border-border/60 hover:bg-accent", variant === "ghost" && "rounded-md hover:bg-accent", variant === "muted" && "rounded-md bg-muted hover:bg-muted/80", size === "sm" && "h-8 px-3 text-xs", @@ -183,9 +183,20 @@ export function ModelSelector({ all.set(model.id, model); } for (const lora of loraModels) { + // Strip "/ suffix" from display name (e.g. "foo_123/foo" → "foo_123") + const displayName = lora.name.includes("/") + ? lora.name.split("/")[0].trim() + : lora.name; + // Show type tag instead of base model name + const isExported = lora.source === "exported"; + const isMerged = lora.exportType === "merged"; + const tag = isExported + ? isMerged ? "Merged · Exported" : "LoRA" + : "LoRA"; all.set(lora.id, { ...lora, - description: lora.baseModel || lora.description, + name: displayName, + description: tag, }); } return all; diff --git a/studio/frontend/src/components/assistant-ui/model-selector/pickers.tsx b/studio/frontend/src/components/assistant-ui/model-selector/pickers.tsx index 80e9dd0b6b..441fdf0f02 100644 --- a/studio/frontend/src/components/assistant-ui/model-selector/pickers.tsx +++ b/studio/frontend/src/components/assistant-ui/model-selector/pickers.tsx @@ -365,15 +365,26 @@ export function LoraModelPicker({
+ Total optimizer steps. Use 0 to run by epochs. +
+- Total optimizer steps. Use 0 to run by epochs. -
-- Max sequence length for training samples -
-- Recommended: 2e-4 for LoRA, 2e-5 for full fine-tune -
-+ Max sequence length for training samples +