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fix: replace Unicode dashes with ASCII equivalents across codebase

Browse source 
Box-drawing chars (U+2500), em dashes (U+2014), and en dashes (U+2013)
in comments, section dividers, log messages, and docstrings are not
representable on legacy code pages like CP1252. Replace them with plain
ASCII dashes so the codebase is consistently ASCII-safe.

User-facing UI strings (placeholders, separators, display text in the
frontend) are left unchanged since they render in the browser which
handles Unicode natively.
This commit is contained in:
Daniel Han 2026-03-25 05:01:40 +00:00
commit a1eff8ee22
61 changed files with 448 additions and 448 deletions
Download patch file Download diff file Expand all files Collapse all files

20
install.ps1
View file

@ -14,7 +14,7 @@ function Install-UnslothStudio {
Write-Host "========================================="
Write-Host ""
# ── Helper: refresh PATH from registry (deduplicating entries) ──
# -- Helper: refresh PATH from registry (deduplicating entries) --
function Refresh-SessionPath {
$machine = [System.Environment]::GetEnvironmentVariable("Path", "Machine")
$user = [System.Environment]::GetEnvironmentVariable("Path", "User")
@ -31,7 +31,7 @@ function Install-UnslothStudio {
$env:Path = $unique -join ";"
}
# ── Check winget ──
# -- Check winget --
if (-not (Get-Command winget -ErrorAction SilentlyContinue)) {
Write-Host "Error: winget is not available." -ForegroundColor Red
Write-Host " Install it from https://aka.ms/getwinget" -ForegroundColor Yellow
@ -39,7 +39,7 @@ function Install-UnslothStudio {
return
}
# ── Helper: detect a working Python 3.11-3.13 on the system ──
# -- Helper: detect a working Python 3.11-3.13 on the system --
# Returns the version string (e.g. "3.13") or "" if none found.
# Uses try-catch + stderr redirection so that App Execution Alias stubs
# (WindowsApps) and other non-functional executables are probed safely
@ -109,7 +109,7 @@ function Install-UnslothStudio {
return $null
}
# ── Install Python if no compatible version (3.11-3.13) found ──
# -- Install Python if no compatible version (3.11-3.13) found --
# Find-CompatiblePython returns @{ Version = "3.13"; Path = "C:\...\python.exe" } or $null.
$DetectedPython = Find-CompatiblePython
if ($DetectedPython) {
@ -158,7 +158,7 @@ function Install-UnslothStudio {
}
}
# ── Install uv if not present ──
# -- Install uv if not present --
if (-not (Get-Command uv -ErrorAction SilentlyContinue)) {
Write-Host "==> Installing uv package manager..."
$prevEAP = $ErrorActionPreference
@ -180,7 +180,7 @@ function Install-UnslothStudio {
return
}
# ── Create venv (skip if it already exists and has a valid interpreter) ──
# -- Create venv (skip if it already exists and has a valid interpreter) --
# Pass the resolved executable path to uv so it does not re-resolve
# a version string back to a conda interpreter.
$VenvPython = Join-Path $VenvName "Scripts\python.exe"
@ -196,7 +196,7 @@ function Install-UnslothStudio {
Write-Host "==> Virtual environment ${VenvName} already exists, skipping creation."
}
# ── Detect GPU (robust: PATH + hardcoded fallback paths, mirrors setup.ps1) ──
# -- Detect GPU (robust: PATH + hardcoded fallback paths, mirrors setup.ps1) --
$HasNvidiaSmi = $false
$NvidiaSmiExe = $null
try {
@ -227,7 +227,7 @@ function Install-UnslothStudio {
Write-Host " https://www.nvidia.com/Download/index.aspx" -ForegroundColor Yellow
}
# ── Choose the correct PyTorch index URL based on driver CUDA version ──
# -- Choose the correct PyTorch index URL based on driver CUDA version --
# Mirrors Get-PytorchCudaTag in setup.ps1.
function Get-TorchIndexUrl {
$baseUrl = "https://download.pytorch.org/whl"
@ -249,7 +249,7 @@ function Install-UnslothStudio {
}
$TorchIndexUrl = Get-TorchIndexUrl
# ── Install PyTorch first, then unsloth separately ──
# -- Install PyTorch first, then unsloth separately --
#
# Why two steps?
# `uv pip install unsloth --torch-backend=cpu` on Windows resolves to
@ -281,7 +281,7 @@ function Install-UnslothStudio {
return
}
# ── Run studio setup ──
# -- Run studio setup --
# setup.ps1 will handle installing Git, CMake, Visual Studio Build Tools,
# CUDA Toolkit, Node.js, and other dependencies automatically via winget.
Write-Host "==> Running unsloth studio setup..."

18
install.sh
View file

@ -7,7 +7,7 @@ set -e
VENV_NAME="unsloth_studio"
PYTHON_VERSION="3.13"
# ── Helper: download a URL to a file (supports curl and wget) ──
# -- Helper: download a URL to a file (supports curl and wget) --
download() {
if command -v curl >/dev/null 2>&1; then
curl -LsSf "$1" -o "$2"
@ -19,7 +19,7 @@ download() {
fi
}
# ── Helper: check if a single package is available on the system ──
# -- Helper: check if a single package is available on the system --
_is_pkg_installed() {
case "$1" in
build-essential) command -v gcc >/dev/null 2>&1 ;;
@ -31,7 +31,7 @@ _is_pkg_installed() {
esac
}
# ── Helper: install packages via apt, escalating to sudo only if needed ──
# -- Helper: install packages via apt, escalating to sudo only if needed --
# Usage: _smart_apt_install pkg1 pkg2 pkg3 ...
_smart_apt_install() {
_PKGS="$*"
@ -95,7 +95,7 @@ echo " Unsloth Studio Installer"
echo "========================================="
echo ""
# ── Detect platform ──
# -- Detect platform --
OS="linux"
if [ "$(uname)" = "Darwin" ]; then
OS="macos"
@ -104,7 +104,7 @@ elif grep -qi microsoft /proc/version 2>/dev/null; then
fi
echo "==> Platform: $OS"
# ── Check system dependencies ──
# -- Check system dependencies --
# cmake and git are needed by unsloth studio setup to build the GGUF inference
# engine (llama.cpp). build-essential and libcurl-dev are also needed on Linux.
MISSING=""
@ -170,7 +170,7 @@ else
echo "==> All system dependencies found."
fi
# ── Install uv ──
# -- Install uv --
UV_MIN_VERSION="0.7.14"
version_ge() {
@ -224,7 +224,7 @@ if ! command -v uv >/dev/null 2>&1 || ! _uv_version_ok uv; then
export PATH="$HOME/.local/bin:$PATH"
fi
# ── Create venv (skip if it already exists and has a valid interpreter) ──
# -- Create venv (skip if it already exists and has a valid interpreter) --
if [ ! -x "$VENV_NAME/bin/python" ]; then
[ -e "$VENV_NAME" ] && rm -rf "$VENV_NAME"
echo "==> Creating Python ${PYTHON_VERSION} virtual environment (${VENV_NAME})..."
@ -233,11 +233,11 @@ else
echo "==> Virtual environment ${VENV_NAME} already exists, skipping creation."
fi
# ── Install unsloth directly into the venv (no activation needed) ──
# -- Install unsloth directly into the venv (no activation needed) --
echo "==> Installing unsloth (this may take a few minutes)..."
uv pip install --python "$VENV_NAME/bin/python" "unsloth>=2026.3.11" --torch-backend=auto
# ── Run studio setup ──
# -- Run studio setup --
# Ensure the venv's Python is on PATH for setup.sh's Python discovery.
# On macOS the system Python may be outside the 3.11-3.13 range that
# setup.sh requires, but uv already installed a compatible interpreter

2
studio/backend/auth/authentication.py
View file

@ -84,7 +84,7 @@ def refresh_access_token(refresh_token: str) -> Tuple[Optional[str], Optional[st
"""
Validate a refresh token and issue a new access token.
The refresh token itself is NOT consumed — it stays valid until expiry.
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)

6
studio/backend/auth/storage.py
View file

@ -16,7 +16,7 @@ from utils.paths import auth_db_path, ensure_dir
DB_PATH = auth_db_path()
DEFAULT_ADMIN_USERNAME = "unsloth"
# Plaintext bootstrap password file — lives beside auth.db, deleted on
# Plaintext bootstrap password file -- lives beside auth.db, deleted on
# first password change so the credential never lingers on disk.
_BOOTSTRAP_PW_PATH = DB_PATH.parent / ".bootstrap_password"
@ -43,7 +43,7 @@ def generate_bootstrap_password() -> str:
if _bootstrap_password:
return _bootstrap_password
# 3. First-ever startup — generate a fresh passphrase.
# 3. First-ever startup -- generate a fresh passphrase.
import diceware
_bootstrap_password = diceware.get_passphrase(
@ -314,7 +314,7 @@ 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.
The token is NOT consumed -- it stays valid until it expires.
"""
token_hash = _hash_token(token)
conn = get_connection()

4
studio/backend/core/export/export.py
View file

@ -53,7 +53,7 @@ def _apply_wsl_sudo_patch():
def _wsl_do_we_need_sudo(system_type = "debian"):
logger.info(
"WSL detected — skipping sudo check "
"WSL detected -- skipping sudo check "
"(build deps pre-installed by setup.sh)"
)
return False
@ -532,7 +532,7 @@ class ExportBackend:
cwd = os.getcwd()
pre_existing_ggufs = set(glob.glob(os.path.join(cwd, "*.gguf")))
# Pass absolute path — no os.chdir needed.
# Pass absolute path -- no os.chdir needed.
# unsloth saves intermediate HF model files into model_save_path.
# unsloth-zoo's check_llama_cpp() uses ~/.unsloth/llama.cpp by default.
model_save_path = os.path.join(abs_save_dir, "model")

16
studio/backend/core/export/orchestrator.py
View file

@ -2,7 +2,7 @@
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""
Export orchestrator — subprocess-based.
Export orchestrator -- subprocess-based.
Provides the same API as ExportBackend, but delegates all ML work
to a persistent subprocess. The subprocess is spawned on first checkpoint
@ -32,7 +32,7 @@ _CTX = mp.get_context("spawn")
class ExportOrchestrator:
"""
Export backend orchestrator — subprocess-based.
Export backend orchestrator -- subprocess-based.
Exposes the same API surface as ExportBackend so routes/export.py
needs minimal changes. Internally, all heavy ML operations happen in
@ -154,7 +154,7 @@ class ExportOrchestrator:
def _wait_response(self, expected_type: str, timeout: float = 3600.0) -> dict:
"""Block until a response of the expected type arrives.
Export operations can take a very long time — GGUF conversion for
Export operations can take a very long time -- GGUF conversion for
large models (30B+) easily takes 20-30 minutes. Default timeout
is 1 hour.
"""
@ -183,7 +183,7 @@ class ExportOrchestrator:
logger.info("Export subprocess status: %s", resp.get("message", ""))
continue
# Other response types during wait — skip
# Other response types during wait -- skip
logger.debug(
"Skipping response type '%s' while waiting for '%s'",
rtype,
@ -208,7 +208,7 @@ class ExportOrchestrator:
return events
# ------------------------------------------------------------------
# Public API — same interface as ExportBackend
# Public API -- same interface as ExportBackend
# ------------------------------------------------------------------
def load_checkpoint(
@ -365,7 +365,7 @@ class ExportOrchestrator:
def cleanup_memory(self) -> bool:
"""Cleanup export-related models from memory."""
if not self._ensure_subprocess_alive():
# No subprocess — just clear local state
# No subprocess -- just clear local state
self.current_checkpoint = None
self.is_vision = False
self.is_peft = False
@ -378,7 +378,7 @@ class ExportOrchestrator:
except RuntimeError:
success = False
# Shut down subprocess after cleanup — no model loaded
# Shut down subprocess after cleanup -- no model loaded
self._shutdown_subprocess()
self.current_checkpoint = None
@ -389,7 +389,7 @@ class ExportOrchestrator:
def scan_checkpoints(
self, outputs_dir: str = str(outputs_root())
) -> List[Tuple[str, list]]:
"""Scan for checkpoints — no ML imports needed, runs locally."""
"""Scan for checkpoints -- no ML imports needed, runs locally."""
from utils.models.checkpoints import scan_checkpoints
return scan_checkpoints(outputs_dir = outputs_dir)

18
studio/backend/core/export/worker.py
View file

@ -5,7 +5,7 @@
Export subprocess entry point.
Each export session runs in a persistent subprocess (mp.get_context("spawn")).
This gives us a clean Python interpreter with no stale module state —
This gives us a clean Python interpreter with no stale module state --
solving the transformers version-switching problem completely.
The subprocess stays alive while a model is loaded, accepting commands
@ -217,7 +217,7 @@ def run_export_process(
resp_queue: Any,
config: dict,
) -> None:
"""Subprocess entrypoint. Persistent — runs command loop until shutdown.
"""Subprocess entrypoint. Persistent -- runs command loop until shutdown.
Args:
cmd_queue: mp.Queue for receiving commands from parent.
@ -244,7 +244,7 @@ def run_export_process(
checkpoint_path = config["checkpoint_path"]
# ── 1. Activate correct transformers version BEFORE any ML imports ──
# -- 1. Activate correct transformers version BEFORE any ML imports --
try:
_activate_transformers_version(checkpoint_path)
except Exception as exc:
@ -259,20 +259,20 @@ def run_export_process(
)
return
# ── 1b. On Windows, check Triton availability (must be before import torch) ──
# -- 1b. On Windows, check Triton availability (must be before import torch) --
if sys.platform == "win32":
try:
import triton # noqa: F401
logger.info("Triton available — torch.compile enabled")
logger.info("Triton available -- torch.compile enabled")
except ImportError:
os.environ["TORCHDYNAMO_DISABLE"] = "1"
logger.warning(
"Triton not found on Windows — torch.compile disabled. "
"Triton not found on Windows -- torch.compile disabled. "
'Install for better performance: pip install "triton-windows<3.7"'
)
# ── 2. Import ML libraries (fresh in this clean process) ──
# -- 2. Import ML libraries (fresh in this clean process) --
try:
_send_response(
resp_queue,
@ -307,7 +307,7 @@ def run_export_process(
)
return
# ── 3. Create export backend and load initial checkpoint ──
# -- 3. Create export backend and load initial checkpoint --
try:
backend = ExportBackend()
@ -325,7 +325,7 @@ def run_export_process(
)
return
# ── 4. Command loop — process commands until shutdown ──
# -- 4. Command loop -- process commands until shutdown --
logger.info("Export subprocess ready, entering command loop")
while True:

12
studio/backend/core/inference/audio_codecs.py
View file

@ -64,7 +64,7 @@ class AudioCodecManager:
else:
raise ValueError(f"Unknown audio_type: {audio_type}")
# ── Lazy loaders ─────────────────────────────────────────────
# -- Lazy loaders ---------------------------------------------
def _load_snac(self, device: str) -> None:
if self._snac_model is not None:
@ -84,7 +84,7 @@ class AudioCodecManager:
import subprocess
# Clone SparkAudio/Spark-TTS GitHub repo for the sparktts Python package
# (same approach as training — the HF model repos don't contain the package)
# (same approach as training -- the HF model repos don't contain the package)
spark_code_dir = os.path.join(
os.path.dirname(model_repo_path or "."), "Spark-TTS"
)
@ -167,7 +167,7 @@ class AudioCodecManager:
self._dac_audio_codec = processor.audio_codec
logger.info("Loaded DAC audio codec")
# ── Decoders ─────────────────────────────────────────────────
# -- Decoders -------------------------------------------------
def decode_snac(
self, generated_ids: torch.Tensor, device: str
@ -188,7 +188,7 @@ class AudioCodecManager:
else:
# Gracefully fall back to using entire output if marker not found
logger.warning(
"No START_OF_SPEECH token (128257) found — using full generated output"
"No START_OF_SPEECH token (128257) found -- using full generated output"
)
cropped = generated_ids
row = cropped[0]
@ -309,7 +309,7 @@ class AudioCodecManager:
token_ids: Optional[list] = None,
text: Optional[str] = None,
) -> Tuple[bytes, int]:
"""Unified decode — dispatches to the right codec decoder."""
"""Unified decode -- dispatches to the right codec decoder."""
if audio_type == "snac":
if not token_ids:
raise ValueError("SNAC decoding requires token_ids")
@ -324,7 +324,7 @@ class AudioCodecManager:
return self.decode_dac(text, device)
raise ValueError(f"Cannot decode audio_type: {audio_type}")
# ── Cleanup ──────────────────────────────────────────────────
# -- Cleanup --------------------------------------------------
def unload(self) -> None:
"""Release all codec models from memory."""

48
studio/backend/core/inference/inference.py
View file

@ -74,14 +74,14 @@ class HarmonyTextStreamer:
self._is_first_put: bool = True
self._stop: bool = False
# Stateful channel tracking — avoids delta-on-transformed bugs
# Stateful channel tracking -- avoids delta-on-transformed bugs
self._emitted_think_open: bool = False
self._emitted_think_close: bool = False
self._analysis_emitted: int = 0 # chars of analysis content emitted
self._final_emitted: int = 0 # chars of final content emitted
# ------------------------------------------------------------------
# put / end — called from the generation thread
# put / end -- called from the generation thread
# ------------------------------------------------------------------
def put(self, value):
@ -89,7 +89,7 @@ class HarmonyTextStreamer:
import torch
if isinstance(value, torch.Tensor):
# value shape: (batch, seq) — take first batch element
# value shape: (batch, seq) -- take first batch element
ids = value[0].tolist() if value.dim() > 1 else value.tolist()
elif isinstance(value, (list, tuple)):
ids = list(value)
@ -127,7 +127,7 @@ class HarmonyTextStreamer:
self._queue.put(None) # sentinel
# ------------------------------------------------------------------
# Iterator interface — consumed by the streaming loop
# Iterator interface -- consumed by the streaming loop
# ------------------------------------------------------------------
def __iter__(self):
@ -164,16 +164,16 @@ class HarmonyTextStreamer:
- final content deltas
"""
# If raw contains <|channel|> but no complete channel+message pair yet,
# buffer silently — don't emit partial channel names as text.
# buffer silently -- don't emit partial channel names as text.
has_channel_token = "<|channel|>" in raw
matches = list(self._HARMONY_RE.finditer(raw))
if has_channel_token and not matches:
# Partial harmony markup still building — wait for more tokens
# Partial harmony markup still building -- wait for more tokens
return
if not has_channel_token and not matches:
# No harmony protocol at all — should not happen for gpt-oss
# No harmony protocol at all -- should not happen for gpt-oss
# but handle gracefully by not emitting anything
return
@ -216,7 +216,7 @@ class InferenceBackend:
self.device = get_device().value
self._audio_codec_manager = AudioCodecManager()
# Thread safety — _generation_lock serializes model.generate() calls.
# Thread safety -- _generation_lock serializes model.generate() calls.
# Must be a regular Lock (NOT RLock) because in async FastAPI, multiple
# requests share the same event-loop thread, so RLock reentrancy lets
# concurrent compare-mode requests race on the GPU. The lock is
@ -273,7 +273,7 @@ class InferenceBackend:
"active_adapter": None,
}
# ── Audio model loading path ──────────────────────────
# -- Audio model loading path --------------------------
if config.is_audio:
audio_type = config.audio_type
adapter_info = " (LoRA adapter)" if config.is_lora else ""
@ -309,7 +309,7 @@ class InferenceBackend:
if os.path.isdir(base_path):
abs_repo_path = os.path.abspath(os.path.dirname(base_path))
else:
# base_model is an HF ID — download it
# base_model is an HF ID -- download it
from huggingface_hub import snapshot_download
local_dir = base_path.split("/")[-1]
@ -368,7 +368,7 @@ class InferenceBackend:
self.models[model_name]["model"] = model
self.models[model_name]["tokenizer"] = tokenizer
elif audio_type == "whisper":
# Whisper ASR — uses FastModel with WhisperForConditionalGeneration
# Whisper ASR -- uses FastModel with WhisperForConditionalGeneration
from unsloth import FastModel
from transformers import WhisperForConditionalGeneration
@ -413,7 +413,7 @@ class InferenceBackend:
self.models[model_name]["tokenizer"] = tokenizer
# Load the external codec for TTS audio types
# (Whisper is ASR, audio_vlm is audio input — neither needs a codec)
# (Whisper is ASR, audio_vlm is audio input -- neither needs a codec)
if audio_type not in ("whisper", "audio_vlm"):
model_repo_path = self.models[model_name].get("model_repo_path")
self._audio_codec_manager.load_codec(
@ -473,7 +473,7 @@ class InferenceBackend:
pass
logger.warning(
f"FastVisionModel returned {type(processor).__name__} (no image_processor) "
f"for '{model_name}' — loading proper processor from '{processor_source}'"
f"for '{model_name}' -- loading proper processor from '{processor_source}'"
)
from transformers import AutoProcessor
@ -725,7 +725,7 @@ class InferenceBackend:
Uses PEFT's disable_adapter_layers() / enable_adapter_layers() which toggle
a boolean flag on each LoRA layer. Unsloth's fast_linear_forward checks this
flag (proj.disable_adapters) and skips LoRA computation when True.
This is non-destructive — no model unloading/reloading needed.
This is non-destructive -- no model unloading/reloading needed.
Args:
use_adapter: None = no change, False = disable (base model),
@ -788,7 +788,7 @@ class InferenceBackend:
Thread-safe generation with optional adapter toggling.
The adapter toggle + model.generate() are serialized by _generation_lock
inside the background generation thread — NOT in the event-loop thread.
inside the background generation thread -- NOT in the event-loop thread.
This prevents the RLock-reentrant race that occurs when two async SSE
handlers share the same event-loop thread.
@ -891,7 +891,7 @@ class InferenceBackend:
else:
logger.warning(
f"Model '{self.active_model_name}' is marked as vision but its processor "
f"({type(processor).__name__}) has no image_processor — "
f"({type(processor).__name__}) has no image_processor -- "
f"falling back to text-only generation (image will be ignored)."
)
@ -938,7 +938,7 @@ class InferenceBackend:
raise ValueError(
f"Model '{self.active_model_name}' has no chat_template set in its "
f"tokenizer_config.json. This is usually a problem with the model's "
f"HuggingFace repository — it is missing a 'chat_template' key. "
f"HuggingFace repository -- it is missing a 'chat_template' key. "
f"Please use a model that includes a chat template, or manually set "
f"one via tokenizer.chat_template before inference."
)
@ -1138,7 +1138,7 @@ class InferenceBackend:
repetition_penalty,
cancel_event = None,
) -> Generator[str, None, None]:
"""Handle audio input (ASR) generation — accepts audio numpy array, streams text output.
"""Handle audio input (ASR) generation -- accepts audio numpy array, streams text output.
Uses processor.apply_chat_template with audio embedded in messages (Gemma 3n pattern).
"""
@ -1150,7 +1150,7 @@ class InferenceBackend:
processor = model_info.get("processor") or model_info.get("tokenizer")
raw_tokenizer = getattr(processor, "tokenizer", processor)
# Extract last user text — default matches notebook prompt
# Extract last user text -- default matches notebook prompt
user_text = "Please transcribe this audio."
if messages:
for msg in reversed(messages):
@ -1162,7 +1162,7 @@ class InferenceBackend:
if not system_prompt:
system_prompt = "You are an assistant that transcribes speech accurately."
# Build messages in Gemma 3n format — audio goes INTO apply_chat_template
# Build messages in Gemma 3n format -- audio goes INTO apply_chat_template
audio_messages = [
{"role": "system", "content": [{"type": "text", "text": system_prompt}]},
{
@ -1257,7 +1257,7 @@ class InferenceBackend:
def generate_whisper_response(
self, audio_array, cancel_event = None
) -> Generator[str, None, None]:
"""Whisper ASR — takes audio numpy array, yields transcribed text.
"""Whisper ASR -- takes audio numpy array, yields transcribed text.
Uses the pre-built transformers pipeline (created during model loading).
"""
@ -1433,7 +1433,7 @@ class InferenceBackend:
finally:
# Only set cancel_event when we exited early (user cancel),
# NOT on normal completion. cancel_event is a shared mp.Event
# — setting it unconditionally would leave a stale cancel
# -- setting it unconditionally would leave a stale cancel
# signal that could interfere with the next serialized
# generation request (e.g. in compare mode).
if cancel_event is not None and not generation_complete:
@ -1451,7 +1451,7 @@ class InferenceBackend:
logger.error(f"Error during generation: {e}")
yield f"Error: {str(e)}"
# ── Audio (TTS) Generation ────────────────────────────────────
# -- Audio (TTS) Generation ------------------------------------
def generate_audio_response(
self,
@ -1467,7 +1467,7 @@ class InferenceBackend:
"""
Generate audio from text for TTS models.
Returns (wav_bytes, sample_rate).
Blocking — generates complete audio before returning.
Blocking -- generates complete audio before returning.
"""
if not self.active_model_name:
raise RuntimeError("No active model")

50
studio/backend/core/inference/llama_cpp.py
View file

@ -33,9 +33,9 @@ class LlamaCppBackend:
Manages a llama-server subprocess for GGUF model inference.
Lifecycle:
1. load_model() — starts llama-server with the GGUF file
2. generate_chat_completion() — proxies to /v1/chat/completions, streams back
3. unload_model() — terminates llama-server subprocess
1. load_model() -- starts llama-server with the GGUF file
2. generate_chat_completion() -- proxies to /v1/chat/completions, streams back
3. unload_model() -- terminates llama-server subprocess
"""
def __init__(self):
@ -61,7 +61,7 @@ class LlamaCppBackend:
self._kill_orphaned_servers()
atexit.register(self._cleanup)
# ── Properties ────────────────────────────────────────────────
# -- Properties ------------------------------------------------
@property
def is_loaded(self) -> bool:
@ -112,7 +112,7 @@ class LlamaCppBackend:
def cache_type_kv(self) -> Optional[str]:
return self._cache_type_kv
# ── Binary discovery ──────────────────────────────────────────
# -- Binary discovery ------------------------------------------
@staticmethod
def _find_llama_server_binary() -> Optional[str]:
@ -135,12 +135,12 @@ class LlamaCppBackend:
binary_name = "llama-server.exe" if sys.platform == "win32" else "llama-server"
# 1. Env var — direct path to binary
# 1. Env var -- direct path to binary
env_path = os.environ.get("LLAMA_SERVER_PATH")
if env_path and Path(env_path).is_file():
return env_path
# 1b. UNSLOTH_LLAMA_CPP_PATH — custom llama.cpp install directory
# 1b. UNSLOTH_LLAMA_CPP_PATH -- custom llama.cpp install directory
custom_llama_cpp = os.environ.get("UNSLOTH_LLAMA_CPP_PATH")
if custom_llama_cpp:
custom_dir = Path(custom_llama_cpp)
@ -158,7 +158,7 @@ class LlamaCppBackend:
if win_bin.is_file():
return str(win_bin)
# 2–4. ~/.unsloth/llama.cpp (primary — setup.sh / setup.ps1 build here)
# 2-4. ~/.unsloth/llama.cpp (primary -- setup.sh / setup.ps1 build here)
unsloth_home = Path.home() / ".unsloth" / "llama.cpp"
# Root dir (make builds copy binaries here)
home_root = unsloth_home / binary_name
@ -175,7 +175,7 @@ class LlamaCppBackend:
if home_win.is_file():
return str(home_win)
# 5–6. Legacy: in-tree build (older setup.sh / setup.ps1 versions)
# 5-6. Legacy: in-tree build (older setup.sh / setup.ps1 versions)
project_root = Path(__file__).resolve().parents[4]
# Root dir (make builds)
root_path = project_root / "llama.cpp" / binary_name
@ -204,7 +204,7 @@ class LlamaCppBackend:
return None
# ── GPU allocation ────────────────────────────────────────────
# -- GPU allocation --------------------------------------------
@staticmethod
def _get_gguf_size_bytes(model_path: str) -> int:
@ -318,7 +318,7 @@ class LlamaCppBackend:
# Model is too large even for all GPUs, let --fit handle it
return None, True
# ── Variant fallback ────────────────────────────────────────────
# -- Variant fallback --------------------------------------------
@staticmethod
def _find_smallest_fitting_variant(
@ -374,7 +374,7 @@ class LlamaCppBackend:
except Exception:
return None
# ── Port allocation ───────────────────────────────────────────
# -- Port allocation -------------------------------------------
@staticmethod
def _find_free_port() -> int:
@ -383,7 +383,7 @@ class LlamaCppBackend:
s.bind(("127.0.0.1", 0))
return s.getsockname()[1]
# ── Stdout drain (prevents pipe deadlock on Windows) ─────────
# -- Stdout drain (prevents pipe deadlock on Windows) ---------
def _drain_stdout(self):
"""
@ -400,7 +400,7 @@ class LlamaCppBackend:
self._stdout_lines.append(line)
logger.debug(f"[llama-server] {line}")
except (ValueError, OSError):
# Pipe closed — process is terminating
# Pipe closed -- process is terminating
pass
# GGUF KV type sizes for fast skipping
@ -530,7 +530,7 @@ class LlamaCppBackend:
except Exception as e:
logger.warning(f"Failed to read GGUF metadata: {e}")
# ── HF download (no lock held) ───────────────────────────────
# -- HF download (no lock held) -------------------------------
def _download_gguf(
self,
@ -740,7 +740,7 @@ class LlamaCppBackend:
logger.warning(f"Could not download mmproj: {e}")
return None
# ── Lifecycle ─────────────────────────────────────────────────
# -- Lifecycle -------------------------------------------------
def load_model(
self,
@ -776,7 +776,7 @@ class LlamaCppBackend:
"""
self._cancel_event.clear()
# ── Phase 1: kill old process (under lock, fast) ──────────
# -- Phase 1: kill old process (under lock, fast) ----------
with self._lock:
self._kill_process()
@ -788,7 +788,7 @@ class LlamaCppBackend:
"or set LLAMA_SERVER_PATH environment variable."
)
# ── Phase 2: download (NO lock held, so cancel can proceed) ──
# -- Phase 2: download (NO lock held, so cancel can proceed) --
if hf_repo:
model_path = self._download_gguf(
hf_repo = hf_repo,
@ -819,7 +819,7 @@ class LlamaCppBackend:
logger.info("Load cancelled after download phase")
return False
# ── Phase 3: start llama-server (under lock) ──────────────
# -- Phase 3: start llama-server (under lock) --------------
with self._lock:
# Re-check cancel inside lock
if self._cancel_event.is_set():
@ -1163,7 +1163,7 @@ class LlamaCppBackend:
logger.error(f"llama-server health check timed out after {timeout}s")
return False
# ── Message building (OpenAI format) ──────────────────────────
# -- Message building (OpenAI format) --------------------------
@staticmethod
def _parse_tool_calls_from_text(content: str) -> list[dict]:
@ -1327,7 +1327,7 @@ class LlamaCppBackend:
return result
# ── Generation (proxy to llama-server) ────────────────────────
# -- Generation (proxy to llama-server) ------------------------
@staticmethod
def _iter_text_cancellable(
@ -1463,7 +1463,7 @@ class LlamaCppBackend:
"""
Send a chat completion request to llama-server and stream tokens back.
Uses /v1/chat/completions — llama-server handles chat template
Uses /v1/chat/completions -- llama-server handles chat template
application and vision (multimodal image_url parts) natively.
Yields cumulative text (matching InferenceBackend's convention).
@ -1598,7 +1598,7 @@ class LlamaCppBackend:
return
raise
# ── Tool-calling agentic loop ──────────────────────────────
# -- Tool-calling agentic loop ------------------------------
def generate_chat_completion_with_tools(
self,
@ -2028,7 +2028,7 @@ class LlamaCppBackend:
return
raise
# ── TTS support ────────────────────────────────────────────
# -- TTS support --------------------------------------------
def detect_audio_type(self) -> Optional[str]:
"""Detect audio/TTS codec by probing the loaded model's vocabulary."""
@ -2103,7 +2103,7 @@ class LlamaCppBackend:
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_path = None
# BiCodec needs a repo with BiCodec/ weights — download canonical SparkTTS
# BiCodec needs a repo with BiCodec/ weights -- download canonical SparkTTS
if audio_type == "bicodec":
from huggingface_hub import snapshot_download
import os

54
studio/backend/core/inference/orchestrator.py
View file

@ -2,7 +2,7 @@
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""
Inference orchestrator — subprocess-based.
Inference orchestrator -- subprocess-based.
Provides the same API as InferenceBackend, but delegates all ML work
to a persistent subprocess. The subprocess is spawned on first model load
@ -42,7 +42,7 @@ _DISPATCH_DRAIN_TIMEOUT = 5.0
class InferenceOrchestrator:
"""
Inference backend orchestrator — subprocess-based.
Inference backend orchestrator -- subprocess-based.
Exposes the same API surface as InferenceBackend so routes/inference.py
needs minimal changes. Internally, all heavy ML operations happen in
@ -54,13 +54,13 @@ class InferenceOrchestrator:
self._proc: Optional[mp.Process] = None
self._cmd_queue: Any = None
self._resp_queue: Any = None
self._cancel_event: Any = None # mp.Event — set to cancel generation instantly
self._cancel_event: Any = None # mp.Event -- set to cancel generation instantly
self._lock = threading.Lock()
self._gen_lock = (
threading.Lock()
) # Serializes generation — one request at a time
) # Serializes generation -- one request at a time
# Dispatcher state — for compare mode (adapter-controlled requests).
# Dispatcher state -- for compare mode (adapter-controlled requests).
# Instead of serializing via _gen_lock, adapter-controlled requests
# send commands directly to the subprocess and read from per-request
# mailboxes. A dispatcher thread routes resp_queue events by request_id.
@ -294,7 +294,7 @@ class InferenceOrchestrator:
logger.info("Subprocess status: %s", resp.get("message", ""))
continue
# Other response types during wait — skip
# Other response types during wait -- skip
logger.debug(
"Skipping response type '%s' while waiting for '%s'",
rtype,
@ -337,7 +337,7 @@ class InferenceOrchestrator:
logger.warning("Timed out waiting for gen_done after cancel")
# ------------------------------------------------------------------
# Dispatcher — per-request mailbox routing for compare mode
# Dispatcher -- per-request mailbox routing for compare mode
# ------------------------------------------------------------------
def _start_dispatcher(self) -> None:
@ -385,7 +385,7 @@ class InferenceOrchestrator:
rid = resp.get("request_id")
rtype = resp.get("type", "")
# Status messages — log and skip
# Status messages -- log and skip
if rtype == "status":
logger.info("Subprocess status: %s", resp.get("message", ""))
continue
@ -398,7 +398,7 @@ class InferenceOrchestrator:
mbox.put(resp)
continue
# No matching mailbox — might be for a _gen_lock reader or orphaned
# No matching mailbox -- might be for a _gen_lock reader or orphaned
# Push it back so _read_resp can pick it up. But we can't un-get
# from mp.Queue, so log a warning.
if rtype not in ("status",):
@ -422,14 +422,14 @@ class InferenceOrchestrator:
cancel_event = None,
use_adapter = None,
) -> Generator[str, None, None]:
"""Dispatched generation — sends command without holding _gen_lock.
"""Dispatched generation -- sends command without holding _gen_lock.
Uses a per-request mailbox to receive tokens. This allows two
compare-mode requests to be queued in the subprocess simultaneously,
eliminating the inter-generation round-trip overhead.
The subprocess processes commands sequentially from its cmd_queue,
so generation is still serialized at the GPU level — we just avoid
so generation is still serialized at the GPU level -- we just avoid
the orchestrator-level lock contention.
"""
if not self._ensure_subprocess_alive():
@ -486,7 +486,7 @@ class InferenceOrchestrator:
try:
resp = mailbox.get(timeout = _DISPATCH_READ_TIMEOUT)
except queue.Empty:
# Timeout — check subprocess health
# Timeout -- check subprocess health
if not self._ensure_subprocess_alive():
yield "Error: Inference subprocess crashed during generation"
return
@ -560,7 +560,7 @@ class InferenceOrchestrator:
self._stop_dispatcher()
# ------------------------------------------------------------------
# Public API — same interface as InferenceBackend
# Public API -- same interface as InferenceBackend
# ------------------------------------------------------------------
def load_model(
@ -575,7 +575,7 @@ class InferenceOrchestrator:
"""Load a model for inference.
Always spawns a fresh subprocess for each model load. This ensures
a clean Python interpreter — no stale unsloth patches, torch.compile
a clean Python interpreter -- no stale unsloth patches, torch.compile
caches, or inspect.getsource() failures from a previous model.
"""
from utils.transformers_version import needs_transformers_5
@ -605,7 +605,7 @@ class InferenceOrchestrator:
self._shutdown_subprocess()
elif self._proc is not None:
# Dead subprocess — clean up
# Dead subprocess -- clean up
self._shutdown_subprocess(timeout = 2)
logger.info(
@ -648,7 +648,7 @@ class InferenceOrchestrator:
def unload_model(self, model_name: str) -> bool:
"""Unload a model from the subprocess."""
if not self._ensure_subprocess_alive():
# No subprocess — just clear local state
# No subprocess -- just clear local state
self.models.pop(model_name, None)
if self.active_model_name == model_name:
self.active_model_name = None
@ -739,7 +739,7 @@ class InferenceOrchestrator:
cancel_event = None,
use_adapter = None,
) -> Generator[str, None, None]:
"""Inner generation logic — sends command to subprocess, yields tokens.
"""Inner generation logic -- sends command to subprocess, yields tokens.
Serialized by _gen_lock: only one generation runs at a time.
This prevents concurrent readers from consuming each other's
@ -758,7 +758,7 @@ class InferenceOrchestrator:
# so we can safely read from resp_queue directly.
self._wait_dispatcher_idle()
# Serialize generation — single GPU, one generation at a time.
# Serialize generation -- single GPU, one generation at a time.
# Without this lock, two concurrent readers on the same resp_queue
# can consume and drop each other's token events.
with self._gen_lock:
@ -790,7 +790,7 @@ class InferenceOrchestrator:
cancel_event = None,
use_adapter = None,
) -> Generator[str, None, None]:
"""Actual generation logic — must be called under _gen_lock."""
"""Actual generation logic -- must be called under _gen_lock."""
request_id = str(uuid.uuid4())
# Convert PIL Image to base64 if needed
@ -821,7 +821,7 @@ class InferenceOrchestrator:
yield f"Error: {exc}"
return
# Yield tokens from response queue — we are the only reader
# Yield tokens from response queue -- we are the only reader
# because _gen_lock is held.
while True:
resp = self._read_resp(timeout = 30.0)
@ -835,7 +835,7 @@ class InferenceOrchestrator:
rtype = resp.get("type", "")
# Status messages — skip
# Status messages -- skip
if rtype == "status":
continue
@ -874,7 +874,7 @@ class InferenceOrchestrator:
pass
# ------------------------------------------------------------------
# Audio generation — TTS, ASR, audio input
# Audio generation -- TTS, ASR, audio input
# ------------------------------------------------------------------
def generate_audio_response(
@ -890,7 +890,7 @@ class InferenceOrchestrator:
) -> Tuple[bytes, int]:
"""Generate TTS audio. Returns (wav_bytes, sample_rate).
Blocking — sends command and waits for the complete audio response.
Blocking -- sends command and waits for the complete audio response.
"""
if not self._ensure_subprocess_alive():
raise RuntimeError("Inference subprocess is not running")
@ -953,7 +953,7 @@ class InferenceOrchestrator:
audio_array,
cancel_event = None,
) -> Generator[str, None, None]:
"""Whisper ASR — sends audio to subprocess, yields text."""
"""Whisper ASR -- sends audio to subprocess, yields text."""
yield from self._generate_audio_input_inner(
audio_array = audio_array,
audio_type = "whisper",
@ -975,7 +975,7 @@ class InferenceOrchestrator:
repetition_penalty: float = 1.0,
cancel_event = None,
) -> Generator[str, None, None]:
"""Audio input generation (e.g. Gemma 3n) — streams text tokens."""
"""Audio input generation (e.g. Gemma 3n) -- streams text tokens."""
yield from self._generate_audio_input_inner(
audio_array = audio_array,
audio_type = None, # worker will use generate_audio_input_response
@ -1045,7 +1045,7 @@ class InferenceOrchestrator:
yield f"Error: {exc}"
return
# Yield tokens — same pattern as _generate_locked
# Yield tokens -- same pattern as _generate_locked
while True:
resp = self._read_resp(timeout = 30.0)
@ -1084,7 +1084,7 @@ class InferenceOrchestrator:
def resize_image(self, img, max_size: int = 800):
"""Resize image while maintaining aspect ratio.
No ML imports needed — runs locally in parent process.
No ML imports needed -- runs locally in parent process.
"""
if img is None:
return None

34
studio/backend/core/inference/worker.py
View file

@ -5,7 +5,7 @@
Inference subprocess entry point.
Each inference session runs in a persistent subprocess (mp.get_context("spawn")).
This gives us a clean Python interpreter with no stale module state —
This gives us a clean Python interpreter with no stale module state --
solving the transformers version-switching problem completely.
The subprocess stays alive while a model is loaded, accepting commands
@ -135,12 +135,12 @@ def _handle_load(backend, config: dict, resp_queue: Any) -> None:
training_method = adapter_cfg.get("unsloth_training_method")
if training_method == "lora" and load_in_4bit:
logger.info(
"adapter_config.json says lora — setting load_in_4bit=False"
"adapter_config.json says lora -- setting load_in_4bit=False"
)
load_in_4bit = False
elif training_method == "qlora" and not load_in_4bit:
logger.info(
"adapter_config.json says qlora — setting load_in_4bit=True"
"adapter_config.json says qlora -- setting load_in_4bit=True"
)
load_in_4bit = True
elif not training_method:
@ -150,7 +150,7 @@ def _handle_load(backend, config: dict, resp_queue: Any) -> None:
and load_in_4bit
):
logger.info(
"No training method, base model has no -bnb-4bit — setting load_in_4bit=False"
"No training method, base model has no -bnb-4bit -- setting load_in_4bit=False"
)
load_in_4bit = False
except Exception as e:
@ -259,7 +259,7 @@ def _handle_generate(
logger.info("Starting text generation for request_id=%s", request_id)
for cumulative_text in generator:
# cancel_event is an mp.Event — checked instantly, no queue polling
# cancel_event is an mp.Event -- checked instantly, no queue polling
if cancel_event.is_set():
logger.info("Generation cancelled for request %s", request_id)
break
@ -303,7 +303,7 @@ def _handle_generate_audio(
cmd: dict,
resp_queue: Any,
) -> None:
"""Handle TTS audio generation — returns WAV bytes + sample_rate."""
"""Handle TTS audio generation -- returns WAV bytes + sample_rate."""
request_id = cmd.get("request_id", "")
try:
logger.info("Starting audio generation for request_id=%s", request_id)
@ -351,7 +351,7 @@ def _handle_generate_audio_input(
resp_queue: Any,
cancel_event,
) -> None:
"""Handle audio input generation (ASR/Whisper) — streams text tokens back."""
"""Handle audio input generation (ASR/Whisper) -- streams text tokens back."""
request_id = cmd.get("request_id", "")
try:
@ -461,12 +461,12 @@ def run_inference_process(
cancel_event,
config: dict,
) -> None:
"""Subprocess entrypoint. Persistent — runs command loop until shutdown.
"""Subprocess entrypoint. Persistent -- runs command loop until shutdown.
Args:
cmd_queue: mp.Queue for receiving commands from parent.
resp_queue: mp.Queue for sending responses to parent.
cancel_event: mp.Event shared with parent — set by parent to cancel generation.
cancel_event: mp.Event shared with parent -- set by parent to cancel generation.
config: Initial configuration dict with model info.
"""
os.environ["TOKENIZERS_PARALLELISM"] = "false"
@ -487,7 +487,7 @@ def run_inference_process(
model_name = config["model_name"]
# ── 1. Activate correct transformers version BEFORE any ML imports ──
# -- 1. Activate correct transformers version BEFORE any ML imports --
try:
_activate_transformers_version(model_name)
except Exception as exc:
@ -502,20 +502,20 @@ def run_inference_process(
)
return
# ── 1b. On Windows, check Triton availability (must be before import torch) ──
# -- 1b. On Windows, check Triton availability (must be before import torch) --
if sys.platform == "win32":
try:
import triton # noqa: F401
logger.info("Triton available — torch.compile enabled")
logger.info("Triton available -- torch.compile enabled")
except ImportError:
os.environ["TORCHDYNAMO_DISABLE"] = "1"
logger.warning(
"Triton not found on Windows — torch.compile disabled. "
"Triton not found on Windows -- torch.compile disabled. "
'Install for better performance: pip install "triton-windows<3.7"'
)
# ── 2. Import ML libraries (fresh in this clean process) ──
# -- 2. Import ML libraries (fresh in this clean process) --
try:
_send_response(
resp_queue,
@ -548,7 +548,7 @@ def run_inference_process(
)
return
# ── 3. Create inference backend and load initial model ──
# -- 3. Create inference backend and load initial model --
try:
backend = InferenceBackend()
@ -575,8 +575,8 @@ def run_inference_process(
)
return
# ── 4. Command loop — process commands until shutdown ──
# cancel_event is an mp.Event shared with parent — parent can set it
# -- 4. Command loop -- process commands until shutdown --
# cancel_event is an mp.Event shared with parent -- parent can set it
# at any time to cancel generation instantly (no queue polling needed).
logger.info("Inference subprocess ready, entering command loop")

48
studio/backend/core/training/trainer.py
View file

@ -151,7 +151,7 @@ class UnslothTrainer:
is_dataset_audio: bool = False,
trust_remote_code: bool = False,
) -> None:
"""Lightweight detection and tokenizer load — no model weights, no VRAM.
"""Lightweight detection and tokenizer load -- no model weights, no VRAM.
Sets is_vlm, _audio_type, is_audio_vlm, model_name and loads a
lightweight tokenizer for dataset formatting. Call this before
@ -159,7 +159,7 @@ class UnslothTrainer:
BEFORE loading the training model (avoids VRAM contention with
the LLM-assisted detection helper).
load_model() may be called afterwards — it will re-detect and load
load_model() may be called afterwards -- it will re-detect and load
the full model + tokenizer, overwriting the lightweight one set here.
"""
self.model_name = model_name
@ -513,7 +513,7 @@ class UnslothTrainer:
# the compiled cache. unsloth_compile_transformers() sets __UNSLOTH_PATCHED__
# on each modeling module and replaces methods with exec'd code.
# clear_unsloth_compiled_cache() deletes the disk cache, but the flag
# prevents re-compilation — leaving missing cache files. Reloading
# prevents re-compilation -- leaving missing cache files. Reloading
# restores original class definitions so Unsloth can re-compile cleanly.
import sys as _sys
import importlib
@ -524,7 +524,7 @@ class UnslothTrainer:
try:
importlib.reload(_mod)
except Exception:
pass # Non-critical — Unsloth will handle stale modules
pass # Non-critical -- Unsloth will handle stale modules
# Remove stale compiled cache so the new model gets a fresh one
from utils.cache_cleanup import clear_unsloth_compiled_cache
@ -733,7 +733,7 @@ class UnslothTrainer:
elif self.is_audio_vlm:
# Audio VLM: multimodal model trained on audio (e.g. Gemma 3N)
# Uses FastModel (general loader) — returns (model, processor)
# Uses FastModel (general loader) -- returns (model, processor)
from unsloth import FastModel
self.model, self.tokenizer = FastModel.from_pretrained(
@ -814,7 +814,7 @@ class UnslothTrainer:
# second attempt because the failed first call's partial
# imports clean up the stale state as a side effect.
self._source_code_retried = True
logger.info(f"\n'could not get source code' — retrying once...\n")
logger.info(f"\n'could not get source code' -- retrying once...\n")
return self.load_model(
model_name = model_name,
max_seq_length = max_seq_length,
@ -1324,7 +1324,7 @@ class UnslothTrainer:
trust_remote_code = getattr(self, "trust_remote_code", False),
)
# Strip pad_to_multiple_of from tokenizer init_kwargs — fine-tuned models
# Strip pad_to_multiple_of from tokenizer init_kwargs -- fine-tuned models
# (e.g. keanteng/sesame-csm-elise) save it in tokenizer_config.json, and
# _merge_kwargs leaks it into audio_kwargs where EncodecFeatureExtractor rejects it.
processor.tokenizer.init_kwargs.pop("pad_to_multiple_of", None)
@ -1383,7 +1383,7 @@ class UnslothTrainer:
],
}
]
# NOTE: pad_to_multiple_of intentionally omitted from text_kwargs —
# NOTE: pad_to_multiple_of intentionally omitted from text_kwargs --
# CsmProcessor._merge_kwargs leaks it to EncodecFeatureExtractor which rejects it.
model_inputs = processor.apply_chat_template(
conversation,
@ -1654,7 +1654,7 @@ class UnslothTrainer:
# Truncate to max_length
input_ids = input_ids[:max_length]
# Labels = input_ids (no masking — Orpheus trains on full sequence)
# Labels = input_ids (no masking -- Orpheus trains on full sequence)
labels = list(input_ids)
attention_mask = [1] * len(input_ids)
@ -1753,7 +1753,7 @@ class UnslothTrainer:
)
# Cast audio column so datasets 4.x AudioDecoder objects are decoded to dicts.
# Don't resample here — BiCodec's target_sr may differ; the loop handles resampling.
# Don't resample here -- BiCodec's target_sr may differ; the loop handles resampling.
from datasets import Audio
dataset = dataset.cast_column(audio_col, Audio())
@ -2305,7 +2305,7 @@ class UnslothTrainer:
"""
Load and prepare dataset for training.
Strategy: format first, then split — ensures both train and eval
Strategy: format first, then split -- ensures both train and eval
portions are properly formatted and templated.
Returns:
@ -2368,7 +2368,7 @@ class UnslothTrainer:
and dataset_slice_end >= 0
and dataset_slice_end >= _slice_start
):
# Manual slice — stream only the rows we need instead of
# Manual slice -- stream only the rows we need instead of
# downloading the entire dataset.
rows_to_stream = dataset_slice_end + 1
logger.info(
@ -2419,9 +2419,9 @@ class UnslothTrainer:
f"Loaded eval split '{eval_split}' with {len(eval_dataset)} rows\n"
)
elif eval_split and eval_split == effective_train:
# Same split as training — will do 80/20 split after formatting
# Same split as training -- will do 80/20 split after formatting
logger.info(
f"Eval split '{eval_split}' is the same as train split — will split 80/20\n"
f"Eval split '{eval_split}' is the same as train split -- will split 80/20\n"
)
else:
# Auto-detect eval split from HF (returns a separate dataset, or None)
@ -2538,7 +2538,7 @@ class UnslothTrainer:
# ========== THEN SPLIT ==========
if has_separate_eval_source and eval_dataset is not None:
# Eval came from a separate HF split — format it too
# Eval came from a separate HF split -- format it too
logger.info(f"Formatting eval dataset ({len(eval_dataset)} rows)...\n")
eval_info = format_and_template_dataset(
eval_dataset,
@ -2552,7 +2552,7 @@ class UnslothTrainer:
eval_dataset = eval_info["dataset"]
logger.info(f"Eval dataset formatted successfully\n")
elif eval_enabled and not has_separate_eval_source:
# No separate eval source — split the already-formatted dataset
# No separate eval source -- split the already-formatted dataset
formatted_dataset = dataset_info["dataset"]
split_result = self._resolve_eval_split_from_dataset(formatted_dataset)
if split_result is not None:
@ -2599,7 +2599,7 @@ class UnslothTrainer:
except Exception as e:
logger.warning(f"Could not check dataset splits: {e}")
# No separate HF eval split found — caller will handle programmatic splitting
# No separate HF eval split found -- caller will handle programmatic splitting
return None
def _resolve_eval_split_from_dataset(self, dataset) -> Optional[tuple]:
@ -2666,7 +2666,7 @@ class UnslothTrainer:
# Unsloth's patched_import hook (deepseek_v3_moe.py) is not thread-safe
# with Python's importlib cache, causing KeyError: 'size' if these are
# first imported inside the worker thread.
import transformers # noqa: F401 – ensures submodules are cached
import transformers # noqa: F401 - ensures submodules are cached
from transformers import ( # noqa: F401
Trainer as _HFTrainer,
TrainingArguments as _TrainingArguments,
@ -2796,7 +2796,7 @@ class UnslothTrainer:
return
elif self._audio_type == "snac":
# Orpheus: language model with SNAC codec tokens — plain HF Trainer
# Orpheus: language model with SNAC codec tokens -- plain HF Trainer
# DataCollatorForSeq2Seq dynamically pads variable-length sequences per batch
# (text + audio codes vary in length) and pads labels with -100.
from transformers import (
@ -3083,7 +3083,7 @@ class UnslothTrainer:
)
logger.info("To enable evaluation, set eval_steps > 0.0\n")
else:
logger.info("No eval dataset — evaluation disabled\n")
logger.info("No eval dataset -- evaluation disabled\n")
# Add model-specific parameters
# Use optim and lr_scheduler_type from training_args if provided, otherwise use defaults
@ -3130,7 +3130,7 @@ class UnslothTrainer:
f"Sequence packing: {'enabled' if packing_enabled else 'disabled'}\n"
)
# Audio codec overrides — BiCodec/DAC use the text SFTTrainer path
# Audio codec overrides -- BiCodec/DAC use the text SFTTrainer path
if self._audio_type == "bicodec":
config_args["packing"] = False
logger.info("Applied BiCodec overrides: packing=False\n")
@ -3284,7 +3284,7 @@ class UnslothTrainer:
)
logger.info("Train on responses only configured successfully\n")
# ── Safety net: check if all samples were filtered out ──
# -- Safety net: check if all samples were filtered out --
# Unsloth's train_on_responses_only masks non-response
# tokens with -100. If max_seq_length is too short and the
# response portion gets truncated away, EVERY sample ends
@ -3304,7 +3304,7 @@ class UnslothTrainer:
error_msg = (
f"{dropped}/{original_len} samples ({drop_pct}%) "
f"were dropped after applying 'train on responses "
f"only' — only {filtered_len} remain. This usually "
f"only' -- only {filtered_len} remain. This usually "
f"means max_seq_length ({max_seq}) is too short "
f"and the response portion is being truncated "
f"away. Try increasing max_seq_length (e.g. 8192) "
@ -3394,7 +3394,7 @@ class UnslothTrainer:
"""
config_path = os.path.join(output_dir, "adapter_config.json")
if not os.path.exists(config_path):
logger.info("No adapter_config.json found — skipping training method patch")
logger.info("No adapter_config.json found -- skipping training method patch")
return
try:

14
studio/backend/core/training/training.py
View file

@ -2,7 +2,7 @@
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""
Training backend — subprocess orchestrator.
Training backend -- subprocess orchestrator.
Each training job runs in a fresh subprocess (mp.get_context("spawn")),
solving the transformers version-switching problem. The old in-process
@ -38,7 +38,7 @@ PLOT_HEIGHT = 3.5
@dataclass
class TrainingProgress:
"""Mirror of trainer.TrainingProgress — kept here so the parent process
"""Mirror of trainer.TrainingProgress -- kept here so the parent process
never needs to import the heavy ML modules."""
epoch: float = 0
@ -59,7 +59,7 @@ class TrainingProgress:
class TrainingBackend:
"""
Training orchestration backend — subprocess-based.
Training orchestration backend -- subprocess-based.
Launches a fresh subprocess per training job, communicates via mp.Queue.
"""
@ -321,7 +321,7 @@ class TrainingBackend:
return None
# ------------------------------------------------------------------
# Compatibility shims — routes/training.py accesses these
# Compatibility shims -- routes/training.py accesses these
# ------------------------------------------------------------------
class _TrainerShim:
@ -369,11 +369,11 @@ class TrainingBackend:
self._handle_event(event)
continue
# No event — check if process is still alive
# No event -- check if process is still alive
if self._proc.is_alive():
continue
# Process exited — drain remaining events
# Process exited -- drain remaining events
for e in self._drain_queue(self._event_queue):
self._handle_event(e)
@ -610,7 +610,7 @@ class TrainingBackend:
checkpoint on disk. This is a no-op placeholder.
"""
logger.info(
"_transfer_to_inference_backend: subprocess training — "
"_transfer_to_inference_backend: subprocess training -- "
"model must be loaded from disk (output_dir=%s)",
self._output_dir,
)

68
studio/backend/core/training/worker.py
View file

@ -5,7 +5,7 @@
Training subprocess entry point.
Each training job runs in a fresh subprocess (mp.get_context("spawn")).
This gives us a clean Python interpreter with no stale module state —
This gives us a clean Python interpreter with no stale module state --
solving the transformers version-switching problem completely.
Pattern follows core/data_recipe/jobs/worker.py.
@ -65,7 +65,7 @@ def run_training_process(
stop_queue: Any,
config: dict,
) -> None:
"""Subprocess entrypoint. Fresh Python — no stale module state.
"""Subprocess entrypoint. Fresh Python -- no stale module state.
Args:
event_queue: mp.Queue for sending progress/status/error events to parent.
@ -90,7 +90,7 @@ def run_training_process(
model_name = config["model_name"]
# ── 1. Activate correct transformers version BEFORE any ML imports ──
# -- 1. Activate correct transformers version BEFORE any ML imports --
try:
_activate_transformers_version(model_name)
except Exception as exc:
@ -104,7 +104,7 @@ def run_training_process(
)
return
# ── 1a. Auto-enable trust_remote_code for unsloth/* transformers 5.x models ──
# -- 1a. Auto-enable trust_remote_code for unsloth/* transformers 5.x models --
# Some newer architectures (e.g. NemotronH) have config parsing bugs in
# transformers that require trust_remote_code=True as a workaround.
# Only auto-enable for unsloth/* prefixed models (trusted source).
@ -121,7 +121,7 @@ def run_training_process(
model_name,
)
# ── 1b. Auto-install mamba-ssm for SSM/hybrid models (NemotronH, Falcon-H1) ──
# -- 1b. Auto-install mamba-ssm for SSM/hybrid models (NemotronH, Falcon-H1) --
_SSM_MODEL_SUBSTRINGS = ("nemotron_h", "nemotron-3-nano", "falcon_h1", "falcon-h1")
if any(sub in model_name.lower() for sub in _SSM_MODEL_SUBSTRINGS):
try:
@ -130,7 +130,7 @@ def run_training_process(
logger.info("mamba-ssm already installed")
except ImportError:
logger.info(
"SSM model detected — installing mamba-ssm and causal-conv1d (this may take several minutes)..."
"SSM model detected -- installing mamba-ssm and causal-conv1d (this may take several minutes)..."
)
_send_status(
event_queue, "Installing mamba-ssm (first time only, ~7 min)..."
@ -161,7 +161,7 @@ def run_training_process(
logger.info("Installed %s successfully", _pkg)
logger.info("mamba-ssm installation complete")
# ── 1c. Set fork start method so dataset.map() can multiprocess ──
# -- 1c. Set fork start method so dataset.map() can multiprocess --
# The parent launched us via spawn (clean process), but the compiled
# SFTTrainer checks get_start_method() and disables num_proc if not "fork".
# Linux only: fork is the default start method and is safe here (no CUDA
@ -176,20 +176,20 @@ def run_training_process(
except RuntimeError:
pass # Already set
# ── 1c. On Windows, check Triton availability (must be before import torch) ──
# -- 1c. On Windows, check Triton availability (must be before import torch) --
if sys.platform == "win32":
try:
import triton # noqa: F401
logger.info("Triton available — torch.compile enabled")
logger.info("Triton available -- torch.compile enabled")
except ImportError:
os.environ["TORCHDYNAMO_DISABLE"] = "1"
logger.warning(
"Triton not found on Windows — torch.compile disabled. "
"Triton not found on Windows -- torch.compile disabled. "
'Install for better performance: pip install "triton-windows<3.7"'
)
# ── 2. Now import ML libraries (fresh in this clean process) ──
# -- 2. Now import ML libraries (fresh in this clean process) --
try:
_send_status(event_queue, "Importing Unsloth...")
@ -219,7 +219,7 @@ def run_training_process(
)
return
# ── 2b. EMBEDDING MODEL FAST-PATH ──
# -- 2b. EMBEDDING MODEL FAST-PATH --
# Embedding models use a completely different pipeline (FastSentenceTransformer
# + SentenceTransformerTrainer + MultipleNegativesRankingLoss) so we branch
# early and handle the entire flow in a self-contained function.
@ -237,7 +237,7 @@ def run_training_process(
)
return
# ── 3. Create a fresh trainer instance ──
# -- 3. Create a fresh trainer instance --
trainer = UnslothTrainer()
# Wire up progress callback → event_queue
@ -289,7 +289,7 @@ def run_training_process(
stop_thread = threading.Thread(target = _poll_stop, daemon = True)
stop_thread.start()
# ── 4. Execute the training pipeline ──
# -- 4. Execute the training pipeline --
# Order: detect → dataset → model → prepare → train
# Dataset processing (including LLM-assisted detection) runs BEFORE model
# loading so both never occupy VRAM at the same time.
@ -297,7 +297,7 @@ def run_training_process(
hf_token = config.get("hf_token", "")
hf_token = hf_token if hf_token and hf_token.strip() else None
# ── 4a. Lightweight detection + tokenizer (no VRAM) ──
# -- 4a. Lightweight detection + tokenizer (no VRAM) --
_send_status(event_queue, "Detecting model type...")
trainer.pre_detect_and_load_tokenizer(
model_name = model_name,
@ -311,7 +311,7 @@ def run_training_process(
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
return
# ── 4b. Load and format dataset (LLM helper may use VRAM briefly) ──
# -- 4b. Load and format dataset (LLM helper may use VRAM briefly) --
_send_status(event_queue, "Loading and formatting dataset...")
hf_dataset = config.get("hf_dataset", "")
dataset_result = trainer.load_and_format_dataset(
@ -385,7 +385,7 @@ def run_training_process(
)
return
# ── Start tqdm monitor early so it captures download + tokenization bars ──
# -- Start tqdm monitor early so it captures download + tokenization bars --
import threading as _th
_tqdm_stop = _th.Event()
@ -413,7 +413,7 @@ def run_training_process(
training_type = config.get("training_type", "LoRA/QLoRA")
use_lora = training_type == "LoRA/QLoRA"
# ── 4c. Load training model (uses VRAM — dataset already formatted) ──
# -- 4c. Load training model (uses VRAM -- dataset already formatted) --
_send_status(event_queue, "Loading model...")
success = trainer.load_model(
model_name = model_name,
@ -442,7 +442,7 @@ def run_training_process(
)
return
# ── 4d. Prepare model (LoRA or full finetuning) ──
# -- 4d. Prepare model (LoRA or full finetuning) --
if use_lora:
_send_status(event_queue, "Configuring LoRA adapters...")
success = trainer.prepare_model_for_training(
@ -510,7 +510,7 @@ def run_training_process(
tensorboard_dir = str(resolve_tensorboard_dir(tensorboard_dir))
ensure_dir(Path(tensorboard_dir))
# Start training (directly — no inner thread, we ARE the subprocess)
# Start training (directly -- no inner thread, we ARE the subprocess)
dataset_display = (
config.get("hf_dataset", "") or config.get("uploaded_file", "") or ""
)
@ -598,7 +598,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
"""Self-contained embedding model training pipeline.
Uses FastSentenceTransformer + SentenceTransformerTrainer +
MultipleNegativesRankingLoss — completely separate from the
MultipleNegativesRankingLoss -- completely separate from the
LLM/VLM/audio paths in UnslothTrainer.
Mirrors the pattern from the reference embedding notebooks:
@ -612,7 +612,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
model_name = config["model_name"]
training_start_time = time.time()
# ── 1. Import embedding-specific libraries ──
# -- 1. Import embedding-specific libraries --
_send_status(event_queue, "Importing embedding libraries...")
try:
from unsloth import FastSentenceTransformer, is_bfloat16_supported
@ -637,7 +637,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
)
return
# ── Stop signal handling ──
# -- Stop signal handling --
_should_stop = False
_save_on_stop = True
@ -662,7 +662,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
stop_thread = threading.Thread(target = _poll_stop, daemon = True)
stop_thread.start()
# ── 2. Load model ──
# -- 2. Load model --
_send_status(event_queue, "Loading embedding model...")
try:
hf_token = config.get("hf_token", "")
@ -692,7 +692,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
return
# ── 3. Apply LoRA ──
# -- 3. Apply LoRA --
if use_lora:
_send_status(event_queue, "Configuring LoRA adapters (FEATURE_EXTRACTION)...")
try:
@ -732,7 +732,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
return
# ── 4. Load dataset ──
# -- 4. Load dataset --
_send_status(event_queue, "Loading dataset...")
try:
hf_dataset = config.get("hf_dataset", "")
@ -750,7 +750,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
token = hf_token,
)
elif local_datasets:
# Load from local file(s) — mirrors the non-embedding pipeline's
# Load from local file(s) -- mirrors the non-embedding pipeline's
# directory handling so recipe outputs (parquet-files/) work.
all_files: list[str] = []
for dataset_file in local_datasets:
@ -833,10 +833,10 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
return
# ── 5. Create loss function ──
# -- 5. Create loss function --
loss = MultipleNegativesRankingLoss(model)
# ── 6. Build training arguments ──
# -- 6. Build training arguments --
_send_status(event_queue, "Configuring training...")
try:
lr_value = float(config.get("learning_rate", "2e-4"))
@ -902,7 +902,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
args = SentenceTransformerTrainingArguments(**training_args_kwargs)
# ── 7. Calculate total steps for progress tracking ──
# -- 7. Calculate total steps for progress tracking --
if max_steps_val and max_steps_val > 0:
total_steps = max_steps_val
else:
@ -911,7 +911,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
steps_per_epoch = max(len_dataloader // gradient_accumulation_steps, 1)
total_steps = steps_per_epoch * effective_epochs
# ── 8. Create progress callback ──
# -- 8. Create progress callback --
class _EmbeddingProgressCallback(TrainerCallback):
"""Sends training progress events to the parent process via event_queue."""
@ -952,7 +952,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
control.should_training_stop = True
return control
# ── 9. Create trainer and train ──
# -- 9. Create trainer and train --
_send_status(event_queue, "Starting embedding training...")
try:
trainer = SentenceTransformerTrainer(
@ -975,7 +975,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
)
return
# ── 10. Save model ──
# -- 10. Save model --
if _should_stop and not _save_on_stop:
event_queue.put(
{
@ -1004,7 +1004,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
)
return
# ── 11. Done ──
# -- 11. Done --
event_queue.put(
{
"type": "complete",

12
studio/backend/main.py
View file

@ -64,13 +64,13 @@ async def lifespan(app: FastAPI):
# Clean up any stale compiled cache from previous runs
clear_unsloth_compiled_cache()
# Remove stale .venv_overlay from previous versions — no longer used.
# Remove stale .venv_overlay from previous versions -- no longer used.
# Version switching now uses .venv_t5/ (pre-installed by setup.sh).
overlay_dir = Path(__file__).resolve().parent.parent.parent / ".venv_overlay"
if overlay_dir.is_dir():
shutil.rmtree(overlay_dir, ignore_errors = True)
# Detect hardware first — sets DEVICE global used everywhere
# Detect hardware first -- sets DEVICE global used everywhere
detect_hardware()
# Pre-cache the helper GGUF model for LLM-assisted dataset detection.
@ -177,7 +177,7 @@ async def get_system_info():
import psutil
from utils.hardware import get_device, get_gpu_memory_info, DeviceType
# GPU Info — query nvidia-smi for physical GPUs, filtered by
# GPU Info -- query nvidia-smi for physical GPUs, filtered by
# CUDA_VISIBLE_DEVICES when set (the frontend uses this for GGUF
# fit estimation and llama-server respects CVD too).
import os
@ -289,7 +289,7 @@ def _inject_bootstrap(html_bytes: bytes, app: FastAPI) -> bytes:
The script tag is only injected while the default admin account still
has ``must_change_password=True``. Once the user changes the password
the HTML is served clean — no credentials leak.
the HTML is served clean -- no credentials leak.
"""
import json as _json
@ -340,14 +340,14 @@ def setup_frontend(app: FastAPI, build_path: Path):
file_path = (build_path / full_path).resolve()
# Block path traversal — ensure resolved path stays inside build_path
# Block path traversal -- ensure resolved path stays inside build_path
if not file_path.is_relative_to(build_path.resolve()):
return Response(status_code = 403)
if file_path.is_file():
return FileResponse(file_path)
# Serve index.html as bytes — avoids Content-Length mismatch
# Serve index.html as bytes -- avoids Content-Length mismatch
content = (build_path / "index.html").read_bytes()
content = _strip_crossorigin(content)
content = _inject_bootstrap(content, app)

12
studio/backend/models/inference.py
View file

@ -203,7 +203,7 @@ class InferenceStatusResponse(BaseModel):
# =====================================================================
# ── Multimodal content parts (OpenAI vision format) ──────────────
# -- Multimodal content parts (OpenAI vision format) --------------
class TextContentPart(BaseModel):
@ -214,7 +214,7 @@ class TextContentPart(BaseModel):
class ImageUrl(BaseModel):
"""Image URL object — supports data URIs and remote URLs."""
"""Image URL object -- supports data URIs and remote URLs."""
url: str = Field(..., description = "data:image/png;base64,... or https://...")
detail: Optional[Literal["auto", "low", "high"]] = "auto"
@ -243,7 +243,7 @@ ContentPart = Annotated[
"""Union type for multimodal content parts, discriminated by the 'type' field."""
# ── Messages ─────────────────────────────────────────────────────
# -- Messages -----------------------------------------------------
class ChatMessage(BaseModel):
@ -282,7 +282,7 @@ class ChatCompletionRequest(BaseModel):
)
presence_penalty: float = Field(0.0, ge = 0.0, le = 2.0, description = "Presence penalty")
# ── Unsloth extensions (ignored by standard OpenAI clients) ──
# -- Unsloth extensions (ignored by standard OpenAI clients) --
top_k: int = Field(20, ge = -1, le = 100, description = "[x-unsloth] Top-k sampling")
min_p: float = Field(
0.01, ge = 0.0, le = 1.0, description = "[x-unsloth] Min-p sampling threshold"
@ -338,7 +338,7 @@ class ChatCompletionRequest(BaseModel):
)
# ── Streaming response chunks ────────────────────────────────────
# -- Streaming response chunks ------------------------------------
class ChoiceDelta(BaseModel):
@ -368,7 +368,7 @@ class ChatCompletionChunk(BaseModel):
timings: Optional[dict] = None
# ── Non-streaming response ───────────────────────────────────────
# -- Non-streaming response ---------------------------------------
class CompletionMessage(BaseModel):

2
studio/backend/models/training.py
View file

@ -167,7 +167,7 @@ class TrainingStatus(BaseModel):
metric_history: Optional[dict] = Field(
None,
description = "Full metric history arrays for chart recovery after SSE reconnection. "
"Keys: 'steps', 'loss', 'lr', 'grad_norm', 'grad_norm_steps' — each a list of numeric values.",
"Keys: 'steps', 'loss', 'lr', 'grad_norm', 'grad_norm_steps' -- each a list of numeric values.",
)

6
studio/backend/routes/datasets.py
View file

@ -339,7 +339,7 @@ def check_format(
total_rows = None
if dataset_path.exists():
# ── Local file ──────────────────────────────────────────
# -- Local file ------------------------------------------
train_split = request.train_split or "train"
preview_slice, total_rows = _load_local_preview_slice(
dataset_path = dataset_path,
@ -347,7 +347,7 @@ def check_format(
preview_size = PREVIEW_SIZE,
)
else:
# ── HuggingFace dataset ─────────────────────────────────
# -- HuggingFace dataset ---------------------------------
# Tier 1: list_repo_files → load only the first data file
preview_slice = None
@ -402,7 +402,7 @@ def check_format(
logger.warning(f"Tier 1 (single-file) failed: {e}")
if preview_slice is None:
# Tier 2: full streaming (resolves all files — slow for large repos)
# Tier 2: full streaming (resolves all files -- slow for large repos)
logger.info("Tier 2: falling back to full streaming load_dataset")
load_kwargs = {
"path": request.dataset_name,

2
studio/backend/routes/export.py
View file

@ -55,7 +55,7 @@ async def load_checkpoint(
"""
try:
# Version switching is handled automatically by the subprocess-based
# export backend — no need for ensure_transformers_version() here.
# export backend -- no need for ensure_transformers_version() here.
# Free GPU memory: shut down any running inference/training subprocesses
# before loading the export checkpoint (they'd compete for VRAM).

50
studio/backend/routes/inference.py
View file

@ -94,9 +94,9 @@ async def load_model(
"""
try:
# Version switching is handled automatically by the subprocess-based
# inference backend — no need for ensure_transformers_version() here.
# inference backend -- no need for ensure_transformers_version() here.
# ── Already-loaded check: skip reload if the exact model is active ──
# -- Already-loaded check: skip reload if the exact model is active --
backend = get_inference_backend()
llama_backend = get_llama_cpp_backend()
@ -183,7 +183,7 @@ async def load_model(
detail = f"Invalid model identifier: {request.model_path}",
)
# ── GGUF path: load via llama-server ──────────────────────
# -- GGUF path: load via llama-server ----------------------
if config.is_gguf:
llama_backend = get_llama_cpp_backend()
unsloth_backend = get_inference_backend()
@ -264,7 +264,7 @@ async def load_model(
chat_template = llama_backend.chat_template,
)
# ── Standard path: load via Unsloth/transformers ──────────
# -- Standard path: load via Unsloth/transformers ----------
backend = get_inference_backend()
# Unload any active GGUF model first
@ -305,13 +305,13 @@ async def load_model(
training_method = adapter_cfg.get("unsloth_training_method")
if training_method == "lora" and load_in_4bit:
logger.info(
f"adapter_config.json says unsloth_training_method='lora' — "
f"adapter_config.json says unsloth_training_method='lora' -- "
f"setting load_in_4bit=False to match 16-bit training"
)
load_in_4bit = False
elif training_method == "qlora" and not load_in_4bit:
logger.info(
f"adapter_config.json says unsloth_training_method='qlora' — "
f"adapter_config.json says unsloth_training_method='qlora' -- "
f"setting load_in_4bit=True to match QLoRA training"
)
load_in_4bit = True
@ -320,7 +320,7 @@ async def load_model(
f"Training method: {training_method}, load_in_4bit={load_in_4bit}"
)
else:
# No unsloth_training_method — fallback to base model name
# No unsloth_training_method -- fallback to base model name
if (
config.base_model
and "-bnb-4bit" not in config.base_model.lower()
@ -328,7 +328,7 @@ async def load_model(
):
logger.info(
f"No unsloth_training_method in adapter_config.json. "
f"Base model '{config.base_model}' has no -bnb-4bit suffix — "
f"Base model '{config.base_model}' has no -bnb-4bit suffix -- "
f"setting load_in_4bit=False"
)
load_in_4bit = False
@ -657,7 +657,7 @@ async def generate_audio(
raise HTTPException(status_code = 400, detail = "No user message found.")
text = last_user_msg["content"]
# Pick backend — both return (wav_bytes, sample_rate)
# Pick backend -- both return (wav_bytes, sample_rate)
llama_backend = get_llama_cpp_backend()
if llama_backend.is_loaded and getattr(llama_backend, "_is_audio", False):
model_name = llama_backend.model_identifier
@ -780,7 +780,7 @@ def _extract_content_parts(
first_image_b64: Optional[str] = None
for msg in messages:
# ── System messages → extract as system_prompt ────────
# -- System messages → extract as system_prompt --------
if msg.role == "system":
if isinstance(msg.content, str):
system_prompt = msg.content
@ -791,9 +791,9 @@ def _extract_content_parts(
)
continue
# ── User / assistant messages ─────────────────────────
# -- User / assistant messages -------------------------
if isinstance(msg.content, str):
# Plain string content — pass through
# Plain string content -- pass through
chat_messages.append({"role": msg.role, "content": msg.content})
elif isinstance(msg.content, list):
# Multimodal content parts
@ -838,7 +838,7 @@ async def openai_chat_completions(
llama_backend = get_llama_cpp_backend()
using_gguf = llama_backend.is_loaded
# ── Determine which backend is active ─────────────────────
# -- Determine which backend is active ---------------------
if using_gguf:
model_name = llama_backend.model_identifier or payload.model
if getattr(llama_backend, "_is_audio", False):
@ -852,20 +852,20 @@ async def openai_chat_completions(
)
model_name = backend.active_model_name or payload.model
# ── Audio TTS path: auto-route to audio generation ────
# (Whisper is ASR not TTS — handled below in audio input path)
# -- Audio TTS path: auto-route to audio generation ----
# (Whisper is ASR not TTS -- handled below in audio input path)
model_info = backend.models.get(backend.active_model_name, {})
if model_info.get("is_audio") and model_info.get("audio_type") != "whisper":
return await generate_audio(payload, request)
# ── Whisper without audio: return clear error ──
# -- Whisper without audio: return clear error --
if model_info.get("audio_type") == "whisper" and not payload.audio_base64:
raise HTTPException(
status_code = 400,
detail = "Whisper models require audio input. Please upload an audio file.",
)
# ── Audio INPUT path: decode WAV and route to audio input generation ──
# -- Audio INPUT path: decode WAV and route to audio input generation --
if payload.audio_base64 and model_info.get("has_audio_input"):
audio_array = _decode_audio_base64(payload.audio_base64)
system_prompt, chat_messages, _ = _extract_content_parts(payload.messages)
@ -970,7 +970,7 @@ async def openai_chat_completions(
)
return JSONResponse(content = response.model_dump())
# ── Parse messages (handles multimodal content parts) ─────
# -- Parse messages (handles multimodal content parts) -----
system_prompt, chat_messages, extracted_image_b64 = _extract_content_parts(
payload.messages
)
@ -981,7 +981,7 @@ async def openai_chat_completions(
detail = "At least one non-system message is required.",
)
# ── GGUF path: proxy to llama-server /v1/chat/completions ──
# -- GGUF path: proxy to llama-server /v1/chat/completions --
if using_gguf:
# Reject images if this GGUF model doesn't support vision
image_b64 = extracted_image_b64 or payload.image_base64
@ -1021,7 +1021,7 @@ async def openai_chat_completions(
completion_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
created = int(time.time())
# ── Tool-calling path (agentic loop) ──────────────────
# -- Tool-calling path (agentic loop) ------------------
use_tools = (
payload.enable_tools and llama_backend.supports_tools and not image_b64
)
@ -1192,7 +1192,7 @@ async def openai_chat_completions(
},
)
# ── Standard GGUF path (no tools) ─────────────────────
# -- Standard GGUF path (no tools) ---------------------
def gguf_generate():
return llama_backend.generate_chat_completion(
@ -1354,7 +1354,7 @@ async def openai_chat_completions(
logger.error(f"Error during GGUF completion: {e}", exc_info = True)
raise HTTPException(status_code = 500, detail = str(e))
# ── Standard Unsloth path ─────────────────────────────────
# -- Standard Unsloth path ---------------------------------
# Decode image (from content parts OR legacy field)
image_b64 = extracted_image_b64 or payload.image_base64
@ -1416,7 +1416,7 @@ async def openai_chat_completions(
completion_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
created = int(time.time())
# ── Streaming response ────────────────────────────────────────
# -- Streaming response ----------------------------------------
if payload.stream:
async def stream_chunks():
@ -1446,7 +1446,7 @@ async def openai_chat_completions(
gen = generate()
while True:
# next(gen, _DONE) returns _DONE instead of raising
# StopIteration — StopIteration cannot propagate
# StopIteration -- StopIteration cannot propagate
# through asyncio futures (Python limitation).
cumulative = await loop.run_in_executor(None, next, gen, _DONE)
if cumulative is _DONE:
@ -1511,7 +1511,7 @@ async def openai_chat_completions(
},
)
# ── Non-streaming response ────────────────────────────────────
# -- Non-streaming response ------------------------------------
else:
try:
full_text = ""

6
studio/backend/routes/models.py
View file

@ -499,7 +499,7 @@ async def scan_loras(
)
)
# Scan exported models (merged, LoRA, base — skips GGUF)
# Scan exported models (merged, LoRA, base -- skips GGUF)
exported = scan_exported_models(exports_dir = resolved_exports_dir)
for display_name, model_path, export_type, base_model in exported:
lora_list.append(
@ -1003,7 +1003,7 @@ async def delete_cached_model(
if target_repo is None:
raise HTTPException(status_code = 404, detail = "Model not found in cache")
# ── Per-variant GGUF deletion ────────────────────────────
# -- Per-variant GGUF deletion ----------------------------
if variant:
deleted_bytes = 0
deleted_count = 0
@ -1041,7 +1041,7 @@ async def delete_cached_model(
)
return {"status": "deleted", "repo_id": repo_id, "variant": variant}
# ── Full repo deletion ───────────────────────────────────
# -- Full repo deletion -----------------------------------
revision_hashes = [rev.commit_hash for rev in target_repo.revisions]
if not revision_hashes:
raise HTTPException(status_code = 404, detail = "No revisions found for model")

16
studio/backend/routes/training.py
View file

@ -201,7 +201,7 @@ async def start_training(
"trust_remote_code": request.trust_remote_code,
}
# Training page has no trust_remote_code toggle — the value comes from
# Training page has no trust_remote_code toggle -- the value comes from
# YAML model defaults applied when the user selects a model. As a safety
# net, consult the YAML directly so models that need it always get it.
if not training_kwargs["trust_remote_code"]:
@ -323,7 +323,7 @@ async def reset_training(
if is_active:
if backend._cancel_requested:
# Cancel (save=False) was requested — force-terminate so we can reset immediately
# Cancel (save=False) was requested -- force-terminate so we can reset immediately
logger.info(
"Force-terminating subprocess for immediate reset (cancel path)"
)
@ -523,7 +523,7 @@ async def stream_training_progress(
backend = get_training_backend()
job_id: str = getattr(backend, "current_job_id", "") or ""
# ── Helpers ──────────────────────────────────────────────
# -- Helpers ----------------------------------------------
def build_progress(
step: int,
loss: float,
@ -585,11 +585,11 @@ async def stream_training_progress(
lines.append("") # double newline terminates the event
return "\n".join(lines)
# ── Retry directive ──────────────────────────────────────
# -- Retry directive --------------------------------------
# Tell the browser to reconnect after 3 seconds if the connection drops
yield "retry: 3000\n\n"
# ── Replay missed steps on reconnect ─────────────────────
# -- Replay missed steps on reconnect ---------------------
if resume_from_step is not None and backend.step_history:
replayed = 0
grad_norm_by_step = {
@ -636,7 +636,7 @@ async def stream_training_progress(
if replayed:
logger.info(f"SSE reconnect: replayed {replayed} missed steps")
# ── Initial status (only on fresh connections) ───────────
# -- Initial status (only on fresh connections) -----------
if resume_from_step is None:
is_active = backend.is_training_active()
tp = getattr(getattr(backend, "trainer", None), "training_progress", None)
@ -686,7 +686,7 @@ async def stream_training_progress(
)
return
# ── Live polling loop ────────────────────────────────────
# -- Live polling loop ------------------------------------
last_step = resume_from_step if resume_from_step is not None else -1
no_update_count = 0
max_no_updates = (
@ -805,7 +805,7 @@ async def stream_training_progress(
)
break
# ── Final "complete" event ───────────────────────────────
# -- Final "complete" event -------------------------------
final_step = backend.step_history[-1] if backend.step_history else last_step
final_loss = backend.loss_history[-1] if backend.loss_history else 0.0
final_lr = backend.lr_history[-1] if backend.lr_history else 0.0

2
studio/backend/run.py
View file

@ -165,7 +165,7 @@ def _graceful_shutdown(server = None):
before the parent exits. This is critical on Windows where atexit
handlers are unreliable after Ctrl+C.
"""
logger.info("Graceful shutdown initiated — cleaning up subprocesses...")
logger.info("Graceful shutdown initiated -- cleaning up subprocesses...")
# 1. Shut down uvicorn server (releases the listening socket)
if server is not None:

6
studio/backend/tests/test_transformers_version.py
View file

@ -37,7 +37,7 @@ from utils.transformers_version import (
# ---------------------------------------------------------------------------
# _resolve_base_model — config.json fallback
# _resolve_base_model -- config.json fallback
# ---------------------------------------------------------------------------
@ -102,7 +102,7 @@ class TestResolveBaseModel:
# ---------------------------------------------------------------------------
# _check_tokenizer_config_needs_v5 — local file check
# _check_tokenizer_config_needs_v5 -- local file check
# ---------------------------------------------------------------------------
@ -150,7 +150,7 @@ class TestCheckTokenizerConfigNeedsV5:
# ---------------------------------------------------------------------------
# needs_transformers_5 — integration-level
# needs_transformers_5 -- integration-level
# ---------------------------------------------------------------------------

6
studio/backend/tests/test_utils.py
View file

@ -2,7 +2,7 @@
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""
Tests for utils/hardware and utils/utils — device detection, GPU memory, error formatting.
Tests for utils/hardware and utils/utils -- device detection, GPU memory, error formatting.
These tests are designed to pass on ANY platform:
• NVIDIA GPU (CUDA backend, requires torch)
@ -75,7 +75,7 @@ def _reset_and_detect():
class TestGetDevice:
"""Tests for get_device() — should agree with the real hardware."""
"""Tests for get_device() -- should agree with the real hardware."""
def setup_method(self):
self._saved_device = _hw_module.DEVICE
@ -167,7 +167,7 @@ class TestClearGpuCache:
@needs_mlx
def test_mlx_does_not_raise(self):
"""MLX cache clear is a no-op — should just succeed."""
"""MLX cache clear is a no-op -- should just succeed."""
with patch("utils.hardware.hardware.get_device", return_value = DeviceType.MLX):
clear_gpu_cache()

4
studio/backend/utils/datasets/chat_templates.py
View file

@ -75,8 +75,8 @@ def get_tokenizer_chat_template(tokenizer, model_name):
if has_chat_template:
logger.info(f"📝 Using tokenizer's own chat template (no Unsloth template match)")
else:
# Base model with no chat template — apply default ChatML
logger.info(f"📝 No chat template found — applying default ChatML template (base model)")
# Base model with no chat template -- apply default ChatML
logger.info(f"📝 No chat template found -- applying default ChatML template (base model)")
try:
tokenizer = get_chat_template(
tokenizer,

18
studio/backend/utils/datasets/dataset_utils.py
View file

@ -120,7 +120,7 @@ def check_dataset_format(dataset, is_vlm: bool = False) -> dict:
}
if is_audio:
# Audio dataset — require manual mapping only when columns can't be auto-detected
# Audio dataset -- require manual mapping only when columns can't be auto-detected
detected_audio = multimodal_info.get("detected_audio_column")
detected_text = multimodal_info.get("detected_text_column")
needs_mapping = not detected_audio or not detected_text
@ -155,7 +155,7 @@ def check_dataset_format(dataset, is_vlm: bool = False) -> dict:
**audio_fields,
}
else:
# Heuristic failed — user must map manually (or use AI Assist)
# Heuristic failed -- user must map manually (or use AI Assist)
return {
"requires_manual_mapping": True,
"detected_format": "unknown",
@ -206,7 +206,7 @@ def _apply_user_mapping(dataset, mapping: dict, batch_size: int = 1000):
Apply user-provided column mapping to convert dataset to conversations format.
Accepts chatml (user/assistant/system), sharegpt (human/gpt/system), and
alpaca (instruction/input/output) role names — all normalised to chatml output.
alpaca (instruction/input/output) role names -- all normalised to chatml output.
If the mapping contains ``__``-prefixed metadata keys (from the conversion
advisor), routes to template-based conversion instead of simple role mapping.
@ -221,7 +221,7 @@ def _apply_user_mapping(dataset, mapping: dict, batch_size: int = 1000):
if meta:
return _apply_template_mapping(dataset, column_roles, meta, batch_size)
# ── Simple mode (original logic) ──
# -- Simple mode (original logic) --
# Pre-compute: group columns by canonical chatml role
role_groups: dict[str, list[str]] = {r: [] for r in _CHATML_ROLE_ORDER}
for col_name, role in column_roles.items():
@ -257,7 +257,7 @@ def _apply_user_mapping(dataset, mapping: dict, batch_size: int = 1000):
def _extract_column_value(val, col: str, label_mapping: dict) -> str:
"""Extract a string value from a column, handling complex types and label mapping."""
# Handle complex types (dicts, lists) — extract useful text instead of raw repr
# Handle complex types (dicts, lists) -- extract useful text instead of raw repr
if isinstance(val, dict):
# Common pattern: {"text": [...]} in QA datasets
if "text" in val:
@ -354,7 +354,7 @@ def _apply_user_mapping_alpaca(dataset, mapping: dict, batch_size: int = 1000):
"""
Apply user-provided column mapping to convert dataset to Alpaca format.
Accepts any format's role names — normalises via _TO_CHATML, then maps
Accepts any format's role names -- normalises via _TO_CHATML, then maps
user → instruction, system → input, assistant → output.
Returns:
@ -447,7 +447,7 @@ def format_dataset(
final_format = "alpaca"
chat_column = None
else:
# auto / chatml / sharegpt / conversational — all produce chatml conversations
# auto / chatml / sharegpt / conversational -- all produce chatml conversations
# (sharegpt is always standardized to role/content internally)
mapped_dataset = _apply_user_mapping(
dataset, custom_format_mapping, batch_size
@ -906,11 +906,11 @@ def format_and_template_dataset(
"errors": [],
}
except Exception as e:
# User mapping failed — fall back to auto-detection instead
# User mapping failed -- fall back to auto-detection instead
# of giving up (handles stale cached mappings gracefully)
warnings.append(
f"User VLM mapping (image='{user_vlm_image_column}', "
f"text='{user_vlm_text_column}') failed: {e} — "
f"text='{user_vlm_text_column}') failed: {e} -- "
f"falling back to auto-detection"
)
logger.info(

26
studio/backend/utils/datasets/format_conversion.py
View file

@ -357,7 +357,7 @@ def convert_to_vlm_format(
else:
image_data = Image.open(image_data).convert("RGB")
# Get text (if list of strings, pick a random one — e.g. multiple captions)
# Get text (if list of strings, pick a random one -- e.g. multiple captions)
text_data = sample[text_column]
if isinstance(text_data, list) and len(text_data) > 0:
import random
@ -391,7 +391,7 @@ def convert_to_vlm_format(
("http://", "https://")
)
# ── Bare-filename detection: images stored as filenames (e.g. "img_001.png")
# -- Bare-filename detection: images stored as filenames (e.g. "img_001.png")
# that don't exist locally. Build a basename→repo_path lookup so we can
# resolve them via hf_hub_download during conversion.
_image_lookup = None
@ -407,7 +407,7 @@ def convert_to_vlm_format(
_notify("Resolving image filenames from HF repo...")
logger.info(
f"🔍 Image column contains bare filenames (e.g. '{first_image}') — building repo lookup..."
f"🔍 Image column contains bare filenames (e.g. '{first_image}') -- building repo lookup..."
)
repo_files = HfApi().list_repo_files(dataset_name, repo_type = "dataset")
_image_lookup = {
@ -421,14 +421,14 @@ def convert_to_vlm_format(
)
else:
logger.info(
f"⚠️ Built lookup with {len(_image_lookup)} images but '{first_image}' not found — falling back to local open"
f"⚠️ Built lookup with {len(_image_lookup)} images but '{first_image}' not found -- falling back to local open"
)
_image_lookup = None
except Exception as e:
logger.info(f"⚠️ Failed to build HF repo image lookup: {e}")
_image_lookup = None
# ── URL probe: 200 samples with parallel workers to estimate speed + failure rate ──
# -- URL probe: 200 samples with parallel workers to estimate speed + failure rate --
PROBE_SIZE = 200
MAX_FAIL_RATE = 0.3
@ -510,7 +510,7 @@ def convert_to_vlm_format(
logger.info(f"⏱️ Estimated time for {total:,} samples: ~{eta_str}")
_notify(info_msg)
# ── Full conversion with progress ──
# -- Full conversion with progress --
from tqdm import tqdm
logger.info(f"🔄 Converting {total} samples to VLM format...")
@ -617,7 +617,7 @@ def convert_to_vlm_format(
if len(converted_list) == 0:
issues = [
f"All {total} samples failed during VLM conversion — no usable images found",
f"All {total} samples failed during VLM conversion -- no usable images found",
f"Image column '{image_column}' may contain URLs that are no longer accessible, "
"or local file paths that don't exist",
]
@ -636,7 +636,7 @@ def convert_to_vlm_format(
raise ValueError(
friendly
or (
f"All {total} samples failed during VLM conversion — no usable images found. "
f"All {total} samples failed during VLM conversion -- no usable images found. "
"This dataset may contain only image URLs that are no longer accessible."
)
)
@ -687,7 +687,7 @@ def convert_sharegpt_with_images_to_vlm_format(
if progress_callback:
progress_callback(status_message = msg)
# ── Resolve image loading strategy (same 3-tier as convert_to_vlm_format) ──
# -- Resolve image loading strategy (same 3-tier as convert_to_vlm_format) --
total = len(dataset)
first_image = next(iter(dataset))[image_column]
@ -703,7 +703,7 @@ def convert_sharegpt_with_images_to_vlm_format(
_notify("Resolving image filenames from HF repo...")
logger.info(
f"🔍 Image column contains bare filenames (e.g. '{first_image}') — building repo lookup..."
f"🔍 Image column contains bare filenames (e.g. '{first_image}') -- building repo lookup..."
)
repo_files = HfApi().list_repo_files(dataset_name, repo_type = "dataset")
_image_lookup = {
@ -721,7 +721,7 @@ def convert_sharegpt_with_images_to_vlm_format(
)
else:
logger.info(
f"⚠️ Built lookup with {len(_image_lookup)} images but '{first_image}' not found — falling back to local open"
f"⚠️ Built lookup with {len(_image_lookup)} images but '{first_image}' not found -- falling back to local open"
)
_image_lookup = None
except Exception as e:
@ -792,7 +792,7 @@ def convert_sharegpt_with_images_to_vlm_format(
return {"messages": new_messages}
# ── Full conversion with progress ──
# -- Full conversion with progress --
logger.info(f"🔄 Converting {total} samples from ShareGPT+image format...")
converted_list = []
failed_count = 0
@ -815,7 +815,7 @@ def convert_sharegpt_with_images_to_vlm_format(
if len(converted_list) == 0:
raise ValueError(
f"All {total} samples failed during ShareGPT+image conversion — "
f"All {total} samples failed during ShareGPT+image conversion -- "
"no usable samples found."
)

20
studio/backend/utils/datasets/format_detection.py
View file

@ -433,7 +433,7 @@ def detect_multimodal_dataset(dataset):
audio_columns = []
modality_types = set()
# ── Image detection ─────────────────────────────────────
# -- Image detection -------------------------------------
# Pass 1: column-name heuristic (word-boundary match to avoid
# false positives like 'pic' in 'topic')
for col_name in column_names:
@ -453,7 +453,7 @@ def detect_multimodal_dataset(dataset):
multimodal_columns.append(col_name)
modality_types.add("image")
# ── Audio detection ─────────────────────────────────────
# -- Audio detection -------------------------------------
# Pass 1: column-name heuristic (word-boundary match)
for col_name in column_names:
for keyword in audio_keywords:
@ -812,17 +812,17 @@ def detect_vlm_dataset_structure(dataset):
Returns True if likely valid, False if definitely broken."""
import os
# PIL / dict — already loaded, always valid
# PIL / dict -- already loaded, always valid
if not isinstance(sample_value, str):
return True
# Local file — check it exists
# Local file -- check it exists
if not sample_value.startswith(("http://", "https://")):
return os.path.exists(
sample_value
) # bare filenames return False here, that's OK
# URL — quick HEAD request with short timeout
# URL -- quick HEAD request with short timeout
try:
import urllib.request
@ -845,7 +845,7 @@ def detect_vlm_dataset_structure(dataset):
if score > 0:
candidates.append((col, score))
# Pass 2: value-based fallback — find columns with image URLs/paths
# Pass 2: value-based fallback -- find columns with image URLs/paths
# even if the column name doesn't match image keywords
already = {c[0] for c in candidates}
for col in column_names:
@ -862,17 +862,17 @@ def detect_vlm_dataset_structure(dataset):
candidates.sort(key = lambda x: x[1], reverse = True)
# Single candidate or top candidate is PIL/dict — no probing needed
# Single candidate or top candidate is PIL/dict -- no probing needed
if len(candidates) == 1 or candidates[0][1] >= 75:
return candidates[0][0]
# Multiple string-based candidates — probe to find one that actually works
# Multiple string-based candidates -- probe to find one that actually works
for col, score in candidates:
sample_value = sample[col]
if _probe_image_candidate(col, sample_value):
return col
# Nothing probed successfully — return highest-scored anyway and let
# Nothing probed successfully -- return highest-scored anyway and let
# conversion handle the error (it may still resolve via hf_hub_download)
return candidates[0][0]
@ -899,7 +899,7 @@ def detect_vlm_dataset_structure(dataset):
and len(sample_value) > 0
and isinstance(sample_value[0], str)
):
# List of strings (e.g. captions list) — lower priority than plain strings
# List of strings (e.g. captions list) -- lower priority than plain strings
priority = min(len(sample_value[0]), 1000) // 2
candidates.append((col, priority))

42
studio/backend/utils/datasets/llm_assist.py
View file

@ -9,7 +9,7 @@ vlm_processing.py. Only invoked when heuristics are uncertain.
Architecture:
- Instantiates LlamaCppBackend, loads model, runs completion(s), unloads.
- Not kept warm — VRAM is freed immediately after use.
- Not kept warm -- VRAM is freed immediately after use.
- Gracefully degrades: returns None when unavailable (no binary, OOM, disabled).
"""
@ -43,12 +43,12 @@ def _strip_think_tags(text: str) -> str:
if "<think>" not in text:
return text
# Try stripping think blocks — keep content outside them
# Try stripping think blocks -- keep content outside them
stripped = re.sub(r"<think>.*?</think>\s*", "", text, flags = re.DOTALL).strip()
if stripped:
return stripped
# Everything was inside <think> tags — extract the inner content of the last block
# Everything was inside <think> tags -- extract the inner content of the last block
matches = re.findall(r"<think>(.*?)</think>", text, flags = re.DOTALL)
if matches:
return matches[-1].strip()
@ -158,7 +158,7 @@ def _run_with_helper(prompt: str, max_tokens: int = 256) -> Optional[str]:
):
if isinstance(chunk, dict):
continue # skip metadata events
cumulative = chunk # cumulative — last value is full text
cumulative = chunk # cumulative -- last value is full text
result = cumulative.strip()
result = _strip_think_tags(result)
@ -178,7 +178,7 @@ def _run_with_helper(prompt: str, max_tokens: int = 256) -> Optional[str]:
pass
# ─── Public API ───────────────────────────────────────────────────────
# --- Public API -------------------------------------------------------
def llm_generate_vlm_instruction(
@ -270,7 +270,7 @@ def llm_classify_columns(
"- user: The input/question/prompt from the human\n"
"- assistant: The expected output/answer/response from the AI\n"
"- system: Context, persona, or task description\n"
"- metadata: IDs, scores, labels, timestamps — not part of conversation\n\n"
"- metadata: IDs, scores, labels, timestamps -- not part of conversation\n\n"
f"Columns: {column_names}\n\n"
f"{formatted}"
"Respond with ONLY a JSON object mapping column names to roles.\n"
@ -383,7 +383,7 @@ def llm_generate_dataset_warning(
return warning
# ─── Dataset Conversion Advisor ──────────────────────────────────────
# --- Dataset Conversion Advisor --------------------------------------
def _parse_json_response(text: str) -> Optional[dict]:
@ -534,7 +534,7 @@ def _run_multi_pass_advisor(
return None
logger.info(f"Advisor model loaded in {time.monotonic() - t0:.1f}s")
# ── Format samples ──
# -- Format samples --
samples_text = ""
for i, row in enumerate(samples[:5], 1):
parts = [f" {col}: {str(row.get(col, ''))[:200]}" for col in columns]
@ -547,7 +547,7 @@ def _run_multi_pass_advisor(
)
card_excerpt = (dataset_card or "")[:1200] or "N/A"
# ── Target Model Hints ──
# -- Target Model Hints --
target_hints = ""
is_gemma_3n = False
if model_name:
@ -583,7 +583,7 @@ def _run_multi_pass_advisor(
"Ensure the dataset format mapped reflects these specialized tasks."
)
# ── Pass 1: Classify ──
# -- Pass 1: Classify --
logger.info("Pass 1: Classifying dataset...")
t1 = time.monotonic()
messages1 = [
@ -595,7 +595,7 @@ def _run_multi_pass_advisor(
"a conversational format suitable for LLM fine-tuning. A dataset is "
'"conversational" if it already has columns like "messages", "conversations", '
'or multiturn "user"/"assistant" pairs. Some datasets are NOT conversational '
"— they are things like summarization, question answering, translation, "
"-- they are things like summarization, question answering, translation, "
"classification, etc. Those need conversion. You must respond with ONLY a "
"valid JSON object. Do not write any explanation before or after the JSON."
f"{target_hints}"
@ -645,11 +645,11 @@ def _run_multi_pass_advisor(
"is_conversational": True,
"user_notification": (
"This dataset is already in conversational format. "
"No conversion needed — columns can be mapped directly."
"No conversion needed -- columns can be mapped directly."
),
}
# ── Pass 2: Map columns to roles ──
# -- Pass 2: Map columns to roles --
logger.info("Pass 2: Mapping columns to roles...")
t2 = time.monotonic()
@ -693,23 +693,23 @@ def _run_multi_pass_advisor(
Here are worked examples to guide you:
Example 1 — Summarization dataset with columns ["document", "summary"]:
Example 1 -- Summarization dataset with columns ["document", "summary"]:
"document" is the input text → "user"
"summary" is the output the model should generate → "assistant"
Result: {{"document": "user", "summary": "assistant"}}
Example 2 — Question answering dataset with columns ["context", "question", "answer"]:
Example 2 -- Question answering dataset with columns ["context", "question", "answer"]:
"context" is input → "user"
"question" is input → "user"
"answer" is what the model should generate → "assistant"
Result: {{"context": "user", "question": "user", "answer": "assistant"}}
Example 3 — Classification dataset with columns ["text", "label"]:
Example 3 -- Classification dataset with columns ["text", "label"]:
"text" is input → "user"
"label" is the output the model should predict → "assistant"
Result: {{"text": "user", "label": "assistant"}}
Example 4 — Translation dataset with columns ["en", "fr"]:
Example 4 -- Translation dataset with columns ["en", "fr"]:
"en" is the source language (input) → "user"
"fr" is the target language (output) → "assistant"
Result: {{"en": "user", "fr": "assistant"}}
@ -725,7 +725,7 @@ def _run_multi_pass_advisor(
"notes": "<brief explanation of why you assigned roles this way>"
}}
REMEMBER: There must be at least one "user" column AND at least one "assistant" column. If all columns are "user", you made a mistake — the output/target column should be "assistant".
REMEMBER: There must be at least one "user" column AND at least one "assistant" column. If all columns are "user", you made a mistake -- the output/target column should be "assistant".
Respond with ONLY the JSON object."""),
},
@ -738,7 +738,7 @@ def _run_multi_pass_advisor(
logger.warning(f"Advisor Pass 2 failed to produce JSON: {raw2[:200]}")
return None
# ── Extract and validate column roles from Pass 2 ──
# -- Extract and validate column roles from Pass 2 --
column_roles = pass2.get("column_roles", {})
label_map = pass2.get("label_mapping") or {} # may be null
@ -750,7 +750,7 @@ def _run_multi_pass_advisor(
)
return None # triggers fallback to simple classification
# ── Pass 3: System prompt (non-conversational datasets only) ──
# -- Pass 3: System prompt (non-conversational datasets only) --
sys_prompt = ""
dtype = pass1.get("dataset_type", "unknown")
is_conv = pass1.get("is_conversational", False)
@ -802,7 +802,7 @@ def _run_multi_pass_advisor(
)
if raw3:
# Pass 3 returns raw text, not JSON — clean it up
# Pass 3 returns raw text, not JSON -- clean it up
cleaned = raw3.strip().strip('"').strip("'").strip()
if len(cleaned) >= 20 and cleaned.lower() not in ("null", "none", ""):
sys_prompt = cleaned

42
studio/backend/utils/hardware/hardware.py
View file

@ -2,7 +2,7 @@
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""
Hardware detection — run once at startup, read everywhere.
Hardware detection -- run once at startup, read everywhere.
Usage:
# At FastAPI lifespan startup:
@ -93,14 +93,14 @@ def detect_hardware() -> DeviceType:
DEVICE = DeviceType.CUDA
CHAT_ONLY = False
device_name = torch.cuda.get_device_properties(0).name
print(f"Hardware detected: CUDA — {device_name}")
print(f"Hardware detected: CUDA -- {device_name}")
return DEVICE
# --- MLX: Apple Silicon ---
if is_apple_silicon() and _has_mlx():
DEVICE = DeviceType.MLX
chip = platform.processor() or platform.machine()
print(f"Hardware detected: MLX — Apple Silicon ({chip})")
print(f"Hardware detected: MLX -- Apple Silicon ({chip})")
return DEVICE
# --- Fallback ---
@ -126,7 +126,7 @@ def get_device() -> DeviceType:
def clear_gpu_cache():
"""
Clear GPU memory cache for the current device.
Safe to call on any platform — no-ops gracefully.
Safe to call on any platform -- no-ops gracefully.
"""
import gc
@ -142,7 +142,7 @@ def clear_gpu_cache():
torch.cuda.ipc_collect()
elif device == DeviceType.MLX:
# MLX manages memory automatically; no explicit cache clear needed.
# mlx.core has no empty_cache equivalent — gc.collect() above is enough.
# mlx.core has no empty_cache equivalent -- gc.collect() above is enough.
pass
@ -186,7 +186,7 @@ def get_gpu_memory_info() -> Dict[str, Any]:
import mlx.core as mx
import psutil
# MLX uses unified memory — report system memory as the pool
# MLX uses unified memory -- report system memory as the pool
total = psutil.virtual_memory().total
# MLX doesn't expose per-process GPU allocation; report 0 as allocated
allocated = 0
@ -235,8 +235,8 @@ def get_gpu_summary() -> Dict[str, Any]:
Return a compact summary of the primary GPU.
Returns dict with keys:
gpu_name – e.g. "NVIDIA L4" (or None)
vram_total_gb – e.g. 22.17 (or None)
gpu_name - e.g. "NVIDIA L4" (or None)
vram_total_gb - e.g. 22.17 (or None)
"""
mem = get_gpu_memory_info()
if mem.get("available"):
@ -289,19 +289,19 @@ def get_gpu_utilization() -> Dict[str, Any]:
Designed to be polled by the frontend during training (not streaming).
Uses ``nvidia-smi --query-gpu`` which is the most accurate source for
utilization %, temperature, and power draw – stats that PyTorch does
utilization %, temperature, and power draw - stats that PyTorch does
not expose.
Returns dict with keys:
available – bool, whether stats could be retrieved
gpu_utilization_pct – GPU core utilization %
temperature_c – GPU temperature in °C
vram_used_gb – VRAM currently used (GiB)
vram_total_gb – VRAM total (GiB)
vram_utilization_pct – VRAM used / total * 100
power_draw_w – current power draw (W)
power_limit_w – power limit (W)
power_utilization_pct – power draw / limit * 100
available - bool, whether stats could be retrieved
gpu_utilization_pct - GPU core utilization %
temperature_c - GPU temperature in °C
vram_used_gb - VRAM currently used (GiB)
vram_total_gb - VRAM total (GiB)
vram_utilization_pct - VRAM used / total * 100
power_draw_w - current power draw (W)
power_limit_w - power limit (W)
power_utilization_pct - power draw / limit * 100
"""
device = get_device()
@ -318,7 +318,7 @@ def get_gpu_utilization() -> Dict[str, Any]:
except (ValueError, TypeError):
return None
# ── nvidia-smi (most complete source) ───────────────────────
# -- nvidia-smi (most complete source) -----------------------
smi_data = {}
try:
import subprocess
@ -354,7 +354,7 @@ def get_gpu_utilization() -> Dict[str, Any]:
except Exception as e:
logger.warning(f"nvidia-smi query failed: {e}")
# ── Backfill VRAM from torch.cuda if nvidia-smi returned [N/A] ──
# -- Backfill VRAM from torch.cuda if nvidia-smi returned [N/A] --
vram_used_mb = smi_data.get("vram_used_mb")
vram_total_mb = smi_data.get("vram_total_mb")
@ -371,7 +371,7 @@ def get_gpu_utilization() -> Dict[str, Any]:
except Exception as e:
logger.debug(f"torch.cuda VRAM backfill failed: {e}")
# ── Build response ──────────────────────────────────────────
# -- Build response ------------------------------------------
gpu_util = smi_data.get("gpu_util")
temp = smi_data.get("temp")
power_draw = smi_data.get("power_draw")

2
studio/backend/utils/inference/inference_config.py
View file

@ -20,7 +20,7 @@ from utils.models.model_config import load_model_defaults
logger = get_logger(__name__)
# ── Family-based inference defaults (loaded once, cached) ──────────────
# -- Family-based inference defaults (loaded once, cached) --------------
_FAMILY_DEFAULTS: Optional[Dict[str, Any]] = None
_FAMILY_PATTERNS: Optional[list] = None

2
studio/backend/utils/models/checkpoints.py
View file

@ -108,7 +108,7 @@ def scan_checkpoints(
# This is a valid training run
checkpoints = []
# Placeholder for the main adapter — loss filled from last checkpoint below
# Placeholder for the main adapter -- loss filled from last checkpoint below
checkpoints.append((item.name, str(item), None))
# Scan for intermediate checkpoints (checkpoint-N subdirs)

20
studio/backend/utils/models/model_config.py
View file

@ -35,7 +35,7 @@ logger = get_logger(__name__)
# Format: "canonical_model_name.yaml": [list of all equivalent model names]
# Based on the model mapper provided - canonical filename is based on the first model name in the mapper
MODEL_NAME_MAPPING = {
# ── Embedding models ──
# -- Embedding models --
"unsloth_all-MiniLM-L6-v2.yaml": [
"unsloth/all-MiniLM-L6-v2",
"sentence-transformers/all-MiniLM-L6-v2",
@ -58,7 +58,7 @@ MODEL_NAME_MAPPING = {
"unsloth/Qwen3-Embedding-4B",
"Qwen/Qwen3-Embedding-4B",
],
# ── Other models ──
# -- Other models --
"unsloth_answerdotai_ModernBERT-large.yaml": [
"answerdotai/ModernBERT-large",
],
@ -551,7 +551,7 @@ def is_vision_model(model_name: str, hf_token: Optional[str] = None) -> bool:
Works for fine-tuned models since they inherit the base architecture.
For models that require transformers 5.x (e.g. GLM-4.7-Flash), the check
runs in a subprocess with .venv_t5/ activated — same pattern as the
runs in a subprocess with .venv_t5/ activated -- same pattern as the
training and inference workers.
Args:
@ -565,7 +565,7 @@ def is_vision_model(model_name: str, hf_token: Optional[str] = None) -> bool:
if needs_transformers_5(model_name):
logger.info(
"Model '%s' needs transformers 5.x — checking vision via subprocess",
"Model '%s' needs transformers 5.x -- checking vision via subprocess",
model_name,
)
return _is_vision_model_subprocess(model_name, hf_token = hf_token)
@ -643,7 +643,7 @@ def detect_audio_type(model_name: str, hf_token: Optional[str] = None) -> Option
"""
Dynamically detect if a model is an audio model and return its type.
Fully dynamic — works for any model, not just known ones.
Fully dynamic -- works for any model, not just known ones.
Uses tokenizer_config.json special tokens to detect all 6 audio types.
Returns: audio_type string ('snac', 'csm', 'bicodec', 'dac', 'whisper', 'audio_vlm') or None.
@ -751,7 +751,7 @@ def detect_mmproj_file(path: str) -> Optional[str]:
Find the mmproj (vision projection) GGUF file in a directory.
Args:
path: Directory to search — or a .gguf file (uses its parent dir).
path: Directory to search -- or a .gguf file (uses its parent dir).
Returns:
Full path to the mmproj .gguf file, or None if not found.
@ -775,7 +775,7 @@ def detect_gguf_model(path: str) -> Optional[str]:
1. path is a direct .gguf file path
2. path is a directory containing .gguf files
Skips mmproj (vision projection) files — those must be passed via
Skips mmproj (vision projection) files -- those must be passed via
``--mmproj``, not ``-m``. Use :func:`detect_mmproj_file` instead.
Returns the full path to the .gguf file if found, None otherwise.
@ -1149,7 +1149,7 @@ def scan_exported_models(
continue
# Check for flat GGUF export (e.g. exports/gemma-3-4b-it-finetune-gguf/)
# Filter out mmproj (vision projection) files — they aren't loadable as main models
# Filter out mmproj (vision projection) files -- they aren't loadable as main models
gguf_files = [f for f in run_dir.glob("*.gguf") if not _is_mmproj(f.name)]
if gguf_files:
base_model = None
@ -1585,7 +1585,7 @@ class ModelConfig:
if not LlamaCppBackend._find_llama_server_binary():
raise RuntimeError(
"llama-server binary not found — cannot load GGUF models. "
"llama-server binary not found -- cannot load GGUF models. "
"Run setup.sh to build it, or set LLAMA_SERVER_PATH."
)
@ -1599,7 +1599,7 @@ class ModelConfig:
if best:
variant = _extract_quant_label(best)
else:
variant = "Q4_K_M" # Fallback — llama-server's own default
variant = "Q4_K_M" # Fallback -- llama-server's own default
display_name = f"{identifier.split('/')[-1]} ({variant})"
logger.info(

10
studio/backend/utils/transformers_version.py
View file

@ -38,7 +38,7 @@ logger = get_logger(__name__)
# Detection
# ---------------------------------------------------------------------------
# Lowercase substrings — if ANY appears anywhere in the lowered model name,
# Lowercase substrings -- if ANY appears anywhere in the lowered model name,
# we need transformers 5.x.
TRANSFORMERS_5_MODEL_SUBSTRINGS: tuple[str, ...] = (
"ministral-3-", # Ministral-3-{3,8,14}B-{Instruct,Reasoning,Base}-2512
@ -61,7 +61,7 @@ _tokenizer_class_cache: dict[str, bool] = {}
TRANSFORMERS_5_VERSION = "5.3.0"
TRANSFORMERS_DEFAULT_VERSION = "4.57.6"
# Pre-installed directory for transformers 5.x — created by setup.sh / setup.ps1
# Pre-installed directory for transformers 5.x -- created by setup.sh / setup.ps1
_VENV_T5_DIR = str(Path.home() / ".unsloth" / "studio" / ".venv_t5")
@ -205,7 +205,7 @@ def needs_transformers_5(model_name: str) -> bool:
# ---------------------------------------------------------------------------
# Version switching (in-process — used only by export)
# Version switching (in-process -- used only by export)
# ---------------------------------------------------------------------------
@ -227,7 +227,7 @@ _PURGE_PREFIXES = (
"trl",
"accelerate",
"auto_gptq",
# NOTE: bitsandbytes is intentionally EXCLUDED — it registers torch custom
# NOTE: bitsandbytes is intentionally EXCLUDED -- it registers torch custom
# operators at import time via torch.library.define(). Those registrations
# live in torch's global operator registry which survives module purge.
# Re-importing bitsandbytes after purge → duplicate registration → crash.
@ -442,7 +442,7 @@ def ensure_transformers_version(model_name: str) -> None:
in_memory_major = int(in_memory.split(".")[0])
if in_memory_major == target_major:
logger.info(
"transformers %s already loaded — correct for '%s'",
"transformers %s already loaded -- correct for '%s'",
in_memory,
model_name,
)

2
studio/frontend/src/components/assistant-ui/attachment.tsx
View file

@ -3,7 +3,7 @@
"use client";
// Avatar removed — caused circular crop on image thumbnails
// Avatar removed -- caused circular crop on image thumbnails
import { TooltipIconButton } from "@/components/assistant-ui/tooltip-icon-button";
import {
Dialog,

8
studio/frontend/src/components/assistant-ui/model-selector/pickers.tsx
View file

@ -152,7 +152,7 @@ function ModelRow({
return content;
}
// ── GGUF Variant Expander ────────────────────────────────────
// -- GGUF Variant Expander ------------------------------------
function GgufVariantExpander({
repoId,
@ -358,7 +358,7 @@ function GgufVariantExpander({
);
}
// ── Detect GGUF repos by naming convention ────────────────────
// -- Detect GGUF repos by naming convention --------------------
function isGgufRepo(id: string): boolean {
return id.toUpperCase().includes("-GGUF");
@ -376,7 +376,7 @@ function extractParamLabel(id: string): string | undefined {
let _cachedGgufCache: CachedGgufRepo[] = [];
let _cachedModelsCache: CachedModelRepo[] = [];
// ── Hub Model Picker ──────────────────────────────────────────
// -- Hub Model Picker ------------------------------------------
export function HubModelPicker({
models,
@ -583,7 +583,7 @@ export function HubModelPicker({
return () => { clearTimeout(timer); obs.disconnect(); };
}, [recommendedSentinel, hasMoreRecommended, recommendedPage, scrollRef]);
/** Handle clicking a model row — GGUF repos expand, others load directly. */
/** Handle clicking a model row -- GGUF repos expand, others load directly. */
const handleModelClick = useCallback(
(id: string) => {
if (isGgufRepo(id)) {

14
studio/frontend/src/components/assistant-ui/sources.tsx
View file

@ -18,7 +18,7 @@ import {
HoverCardContent,
} from "@/components/ui/hover-card";
// ── Helpers ──────────────────────────────────────────────────
// -- Helpers --------------------------------------------------
const extractDomain = (url: string): string => {
try {
@ -33,7 +33,7 @@ const getDomainInitial = (url: string): string => {
return domain.charAt(0).toUpperCase();
};
// ── Sub-components ───────────────────────────────────────────
// -- Sub-components -------------------------------------------
function SourceIcon({
url,
@ -116,7 +116,7 @@ function Source({
);
}
// ── Source badge with hover card ─────────────────────────────
// -- Source badge with hover card -----------------------------
interface SourceData {
url: string;
@ -158,7 +158,7 @@ const SourceBadge: FC<{ source: SourceData }> = ({ source }) => {
);
};
// ── Grouped sources with 2-row collapse ─────────────────────
// -- Grouped sources with 2-row collapse ---------------------
const SourcesGroup: FC = () => {
const message = useMessage();
@ -238,7 +238,7 @@ const SourcesGroup: FC = () => {
return (
<div className="relative mt-2">
{/* Hidden measurement container — renders all badges to measure row positions */}
{/* Hidden measurement container -- renders all badges to measure row positions */}
<div
ref={containerRef}
aria-hidden
@ -288,11 +288,11 @@ const SourcesGroup: FC = () => {
);
};
// ── Individual source (renders null — SourcesGroup handles all) ──
// -- Individual source (renders null -- SourcesGroup handles all) --
const SourcesNoop: FC<Record<string, unknown>> = () => null;
// ── Exports ──────────────────────────────────────────────────
// -- Exports --------------------------------------------------
const Sources = memo(SourcesNoop) as unknown as FC<Record<string, unknown>> & {
Root: typeof Source;

2
studio/frontend/src/components/assistant-ui/tool-group.tsx
View file

@ -210,7 +210,7 @@ const ToolGroupImpl: FC<
> = ({ children, startIndex, endIndex }) => {
const toolCount = endIndex - startIndex + 1;
// Single tool call — render directly without wrapper
// Single tool call -- render directly without wrapper
if (toolCount <= 1) {
return <>{children}</>;
}

2
studio/frontend/src/components/assistant-ui/tool-ui-python.tsx
View file

@ -55,7 +55,7 @@ function CopyBtn({ text }: { text: string }) {
);
}
/** Render code with syntax highlighting via Streamdown + shiki. No extra borders — inherits parent container. */
/** Render code with syntax highlighting via Streamdown + shiki. No extra borders -- inherits parent container. */
function HighlightedCode({ code: source, language }: { code: string; language: string }) {
const markdown = useMemo(
() => `\`\`\`${language}\n${truncate(source)}\n\`\`\``,

2
studio/frontend/src/config/env.ts
View file

@ -10,7 +10,7 @@ export const env = {
BASE_URL: import.meta.env.BASE_URL,
} as const;
// ── Platform / device type ──────────────────────────────────
// -- Platform / device type ----------------------------------
export type DeviceType = "mac" | "windows" | "linux" | string;

2
studio/frontend/src/features/auth/components/auth-form.tsx
View file

@ -92,7 +92,7 @@ export function AuthForm({ mode }: AuthFormProps): ReactElement | null {
let canceled = false;
async function initializeAuthForm(): Promise<void> {
// Always check the server first — localStorage flags can be stale
// Always check the server first -- localStorage flags can be stale
// (e.g. tokens from a previous install attempt). The server's
// /api/auth/status is the source of truth for requires_password_change.
try {

8
studio/frontend/src/features/chat/api/chat-adapter.ts
View file

@ -224,7 +224,7 @@ async function resolveUseAdapter(
if (!thread?.pairId) {
return undefined;
}
// model1/model2 threads don't use the adapter toggle — each side
// model1/model2 threads don't use the adapter toggle -- each side
// loads its own model via /api/inference/load before generation.
if (thread.modelType === "model1" || thread.modelType === "model2") {
return undefined;
@ -357,7 +357,7 @@ async function autoLoadSmallestModel(): Promise<boolean> {
}
}
// No cached models found — try downloading a small default GGUF
// No cached models found -- try downloading a small default GGUF
toast("Downloading a small model…", {
id: toastId,
description: "No downloaded models found. Fetching Qwen3.5-4B (UD-Q4_K_XL).",
@ -463,7 +463,7 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
}
const useAdapter = await resolveUseAdapter(unstable_threadId);
// ── Audio model path (non-streaming) ─────────────────────
// -- Audio model path (non-streaming) ---------------------
const activeModel = runtime.models.find(
(m) => m.id === params.checkpoint,
);
@ -552,7 +552,7 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
let cumulativeText = "";
let reasoningStartAt: number | null = null;
let reasoningDuration = 0;
// Tool call content parts — accumulated and yielded cumulatively.
// Tool call content parts -- accumulated and yielded cumulatively.
// result is set directly on the tool-call part when tool_end arrives.
const toolCallParts: ToolCallMessagePart[] = [];
let serverMetadata: { usage?: ServerUsage; timings?: ServerTimings } | null = null;

2
studio/frontend/src/features/chat/chat-page.tsx
View file

@ -137,7 +137,7 @@ type CompareModelSelection = {
/**
* Detect if this is a LoRA base-vs-fine-tuned compare.
* Returns true when the loaded checkpoint is a LoRA — in that case
* Returns true when the loaded checkpoint is a LoRA -- in that case
* we use the fast simultaneous base/lora adapter-toggle path.
*/
function useIsLoraCompare(): boolean {

2
studio/frontend/src/features/onboarding/components/wizard-layout.tsx
View file

@ -27,7 +27,7 @@ export function WizardLayout() {
const hasFiredRef = useRef(false);
const isFinalStep = currentStep === STEPS.length;
// Only redirect on initial mount — not on re-renders after markOnboardingDone()
// Only redirect on initial mount -- not on re-renders after markOnboardingDone()
// which would override explicit /chat navigation from skip buttons.
const checkedRef = useRef(false);
useEffect(() => {

6
studio/frontend/src/features/recipe-studio/recipe-studio-page.tsx
View file

@ -79,7 +79,7 @@ const TAB_SWITCH_FIT_DELAY_MS = 110;
/**
* Maximum RAF iterations to wait for React Flow's ResizeObserver to populate
* `node.measured` dimensions before calling fitView. ~20 frames ≈ 333 ms at
* 60 fps — more than enough for the render → layout → ResizeObserver cycle.
* 60 fps -- more than enough for the render → layout → ResizeObserver cycle.
*/
const MAX_FIT_VIEW_RETRIES = 20;
/**
@ -477,7 +477,7 @@ export function RecipeStudioPage({
return;
}
if (retries >= MAX_FIT_VIEW_RETRIES) {
// Timed out waiting — fit with whatever we have (graceful fallback).
// Timed out waiting -- fit with whatever we have (graceful fallback).
doFit();
return;
}
@ -491,7 +491,7 @@ export function RecipeStudioPage({
return;
}
} else {
// Measurements were reset (e.g. by updateNodeInternals) — restart
// Measurements were reset (e.g. by updateNodeInternals) -- restart
// the stability counter.
stableCount = 0;
}

2
studio/frontend/src/features/studio/sections/dataset-preview-dialog.tsx
View file

@ -97,7 +97,7 @@ export function DatasetPreviewDialog({
const availableRoles = getAvailableRoles(effectiveIsVlm, datasetFormat, effectiveIsAudio);
const isHfDataset = datasetSource === "huggingface";
// ── AI Assist ──────────────────────────────────────────────────────
// -- AI Assist ------------------------------------------------------
const [isAiLoading, setIsAiLoading] = useState(false);
const [aiError, setAiError] = useState<string | null>(null);

2
studio/frontend/src/features/studio/sections/model-section.tsx
View file

@ -177,7 +177,7 @@ export function ModelSection() {
return applyPriorityOrdering(ids);
}, [hfResults, selectedModel]);
// Filter out GGUF models — they can't be used for training
// Filter out GGUF models -- they can't be used for training
const trainableLocalModels = useMemo(
() =>
localModels.filter((m) => {

2
studio/frontend/src/features/training/api/datasets-api.ts
View file

@ -62,7 +62,7 @@ export async function uploadTrainingDataset(
return res.json();
}
// ── AI Assist ────────────────────────────────────────────────────────
// -- AI Assist --------------------------------------------------------
type AiAssistMappingArgs = {
columns: string[];

2
studio/frontend/src/hooks/use-gpu-utilization.ts
View file

@ -59,7 +59,7 @@ export function useGpuUtilization(
const json = (await res.json()) as GpuUtilization;
if (!cancelled) setData(json);
} catch {
// Silently ignore — next poll will retry
// Silently ignore -- next poll will retry
}
}

2
studio/frontend/src/hooks/use-hardware-info.ts
View file

@ -61,7 +61,7 @@ async function fetchOnce(): Promise<HardwareInfo> {
/**
* Fetch hardware info from `GET /api/system/hardware`.
*
* The result is cached at module level — only one network request is made
* The result is cached at module level -- only one network request is made
* regardless of how many components call this hook.
*/
export function useHardwareInfo(): HardwareInfo {

2
studio/frontend/src/hooks/use-hf-model-search.ts
View file

@ -27,7 +27,7 @@ const EXCLUDED_TAGS = new Set([
]);
// Embedding / sentence-transformer models ship with onnx/openvino as additional
// export formats — they should not be excluded by the tag check above.
// export formats -- they should not be excluded by the tag check above.
const EMBEDDING_TAGS = new Set([
"sentence-transformers",
"feature-extraction",

2
studio/frontend/src/index.css
View file

@ -341,7 +341,7 @@
}
}
/* Minimal scrollbar — thumb only, no track */
/* Minimal scrollbar -- thumb only, no track */
* {
scrollbar-width: thin;
scrollbar-color: transparent transparent;

20
studio/install_python_stack.py
View file

@ -22,20 +22,20 @@ from pathlib import Path
IS_WINDOWS = sys.platform == "win32"
# ── Verbosity control ──────────────────────────────────────────────────────────
# -- Verbosity control ----------------------------------------------------------
# By default the installer shows a minimal progress bar (one line, in-place).
# Set UNSLOTH_VERBOSE=1 in the environment to restore full per-step output:
# Linux/Mac: UNSLOTH_VERBOSE=1 ./studio/setup.sh
# Windows: $env:UNSLOTH_VERBOSE="1" ; .\studio\setup.ps1
VERBOSE: bool = os.environ.get("UNSLOTH_VERBOSE", "0") == "1"
# Progress bar state — updated by _progress() as each install step runs.
# Progress bar state -- updated by _progress() as each install step runs.
# _TOTAL counts: pip-upgrade + 7 shared steps + triton (non-Windows) + local-plugin + finalize
# Update _TOTAL here if you add or remove install steps in install_python_stack().
_STEP: int = 0
_TOTAL: int = 0 # set at runtime in install_python_stack() based on platform
# ── Paths ──────────────────────────────────────────────────────────────
# -- Paths --------------------------------------------------------------
SCRIPT_DIR = Path(__file__).resolve().parent
REQ_ROOT = SCRIPT_DIR / "backend" / "requirements"
SINGLE_ENV = REQ_ROOT / "single-env"
@ -44,7 +44,7 @@ LOCAL_DD_UNSTRUCTURED_PLUGIN = (
SCRIPT_DIR / "backend" / "plugins" / "data-designer-unstructured-seed"
)
# ── Color support ──────────────────────────────────────────────────────
# -- Color support ------------------------------------------------------
def _enable_colors() -> bool:
@ -72,7 +72,7 @@ def _enable_colors() -> bool:
return True # Unix terminals support ANSI by default
# Colors disabled — Colab and most CI runners render ANSI fine, but plain output
# Colors disabled -- Colab and most CI runners render ANSI fine, but plain output
# is cleaner in the notebook cell. Re-enable by setting _HAS_COLOR = _enable_colors()
_HAS_COLOR = False
@ -92,7 +92,7 @@ def _red(msg: str) -> str:
def _progress(label: str) -> None:
"""Print an in-place progress bar for the current install step.
Uses only stdlib (sys.stdout) — no extra packages required.
Uses only stdlib (sys.stdout) -- no extra packages required.
In VERBOSE mode this is a no-op; per-step labels are printed by run() instead.
"""
global _STEP
@ -129,7 +129,7 @@ def run(
# Packages to skip on Windows (require special build steps)
WINDOWS_SKIP_PACKAGES = {"open_spiel", "triton_kernels"}
# ── uv bootstrap ──────────────────────────────────────────────────────
# -- uv bootstrap ------------------------------------------------------
USE_UV = False # Set by _bootstrap_uv() at the start of install_python_stack()
UV_NEEDS_SYSTEM = False # Set by _bootstrap_uv() via probe
@ -285,7 +285,7 @@ def patch_package_file(package_name: str, relative_path: str, url: str) -> None:
download_file(url, dest)
# ── Main install sequence ─────────────────────────────────────────────
# -- Main install sequence ---------------------------------------------
def install_python_stack() -> int:
@ -316,7 +316,7 @@ def install_python_stack() -> int:
req = REQ_ROOT / "extras.txt",
)
# 3b. Extra dependencies (no-deps) — audio model support etc.
# 3b. Extra dependencies (no-deps) -- audio model support etc.
_progress("extra codecs")
pip_install(
"Installing extras (no-deps)",
@ -325,7 +325,7 @@ def install_python_stack() -> int:
req = REQ_ROOT / "extras-no-deps.txt",
)
# 4. Overrides (torchao, transformers) — force-reinstall
# 4. Overrides (torchao, transformers) -- force-reinstall
_progress("dependency overrides")
pip_install(
"Installing dependency overrides",

24
studio/setup.ps1
View file

@ -22,9 +22,9 @@ $FrontendDir = Join-Path $ScriptDir "frontend"
$OxcValidatorDir = Join-Path $ScriptDir "backend\core\data_recipe\oxc-validator"
$IsPipInstall = -not (Test-Path $FrontendDir)
# ─────────────────────────────────────────────
# ---------------------------------------------
# Helper functions
# ─────────────────────────────────────────────
# ---------------------------------------------
# Reload ALL environment variables from registry.
# Picks up changes made by installers (winget, msi, etc.) including
@ -78,7 +78,7 @@ function Find-Nvcc {
return $null
}
# Fallback: no version constraint — pick latest or whatever is available
# Fallback: no version constraint -- pick latest or whatever is available
# 1. Check nvcc on PATH
$cmd = Get-Command nvcc -ErrorAction SilentlyContinue
@ -133,7 +133,7 @@ function Get-CudaComputeCapability {
# Check if an nvcc binary supports a given sm_ architecture.
# Uses `nvcc --list-gpu-code` which outputs sm_* tokens (--list-gpu-arch
# outputs compute_* tokens instead). Available since CUDA 11.6.
# Returns $false if the flag isn't supported (old toolkit) — safer to reject
# Returns $false if the flag isn't supported (old toolkit) -- safer to reject
# and fall back to scanning/PTX than to assume support and fail later.
function Test-NvccArchSupport {
param([string]$NvccExe, [string]$Arch)
@ -247,9 +247,9 @@ function Find-VsBuildTools {
return $null
}
# ─────────────────────────────────────────────
# ---------------------------------------------
# Banner
# ─────────────────────────────────────────────
# ---------------------------------------------
Write-Host "+==============================================+" -ForegroundColor Green
Write-Host "| Unsloth Studio Setup (Windows) |" -ForegroundColor Green
Write-Host "+==============================================+" -ForegroundColor Green
@ -861,7 +861,7 @@ if ($NeedFrontendBuild -and -not $IsPipInstall) {
Write-Host ""
Write-Host "Building frontend..." -ForegroundColor Cyan
# ── Tailwind v4 .gitignore workaround ──
# -- Tailwind v4 .gitignore workaround --
# Tailwind v4's oxide scanner respects .gitignore in parent directories.
# Python venvs create a .gitignore with "*" (ignore everything), which
# prevents Tailwind from scanning .tsx source files for class names.
@ -907,12 +907,12 @@ if ($NeedFrontendBuild -and -not $IsPipInstall) {
Pop-Location
$ErrorActionPreference = $prevEAP_npm
# ── Restore hidden .gitignore files ──
# -- Restore hidden .gitignore files --
foreach ($gi in $HiddenGitignores) {
Rename-Item -Path "$gi._twbuild" -NewName (Split-Path $gi -Leaf) -Force -ErrorAction SilentlyContinue
}
# ── Validate CSS output ──
# -- Validate CSS output --
$CssFiles = Get-ChildItem (Join-Path $DistDir "assets") -Filter "*.css" -ErrorAction SilentlyContinue
$MaxCssSize = ($CssFiles | Measure-Object -Property Length -Maximum).Maximum
if ($MaxCssSize -lt 100000) {
@ -1214,7 +1214,7 @@ python "$PSScriptRoot\install_python_stack.py"
# Restore ErrorActionPreference after pip/python work
$ErrorActionPreference = $prevEAP
# ── Pre-install transformers 5.x into .venv_t5/ ──
# -- Pre-install transformers 5.x into .venv_t5/ --
# Models like GLM-4.7-Flash need transformers>=5.3.0. Instead of pip-installing
# at runtime (slow, ~10-15s), we pre-install into a separate directory.
# The training subprocess just prepends .venv_t5/ to sys.path -- instant switch.
@ -1291,7 +1291,7 @@ if ($OpenSslRoot) {
# ==========================================================================
# PHASE 4: Build llama.cpp with CUDA for GGUF inference + export
# ==========================================================================
# Builds at ~/.unsloth/llama.cpp — a single shared location under the user's
# Builds at ~/.unsloth/llama.cpp -- a single shared location under the user's
# home directory. This is used by both the inference server and the GGUF
# export pipeline (unsloth-zoo).
# We build:
@ -1360,7 +1360,7 @@ if ((Test-Path $LlamaServerBin) -and -not $NeedRebuild) {
$BuildOk = $true
$FailedStep = ""
# Re-sanitize CUDA_PATH_V* vars — Refresh-Environment (called during
# Re-sanitize CUDA_PATH_V* vars -- Refresh-Environment (called during
# Node/Python installs above) may have repopulated conflicting versioned
# vars from the Machine registry.
if ($HasNvidiaSmi -and $CudaToolkitRoot) {

48
studio/setup.sh
View file

@ -7,7 +7,7 @@ set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
# ── Helper: run command quietly, show output only on failure ──
# -- Helper: run command quietly, show output only on failure --
_run_quiet() {
local on_fail=$1
local label=$2
@ -48,19 +48,19 @@ echo "╔═══════════════════════
echo "║ Unsloth Studio Setup Script ║"
echo "╚══════════════════════════════════════╝"
# ── Clean up stale Unsloth compiled caches ──
# -- Clean up stale Unsloth compiled caches --
rm -rf "$REPO_ROOT/unsloth_compiled_cache"
rm -rf "$SCRIPT_DIR/backend/unsloth_compiled_cache"
rm -rf "$SCRIPT_DIR/tmp/unsloth_compiled_cache"
# ── Detect Colab (like unsloth does) ──
# -- Detect Colab (like unsloth does) --
IS_COLAB=false
keynames=$'\n'$(printenv | cut -d= -f1)
if [[ "$keynames" == *$'\nCOLAB_'* ]]; then
IS_COLAB=true
fi
# ── Detect whether frontend needs building ──
# -- Detect whether frontend needs building --
# Skip if dist/ exists AND no tracked input is newer than dist/.
# Checks top-level config/entry files and src/, public/ recursively.
# This handles: PyPI installs (dist/ bundled), repeat runs (no changes),
@ -107,7 +107,7 @@ else
fi
if [ "$NEED_NODE" = true ]; then
# ── 2. Install nvm ──
# -- 2. Install nvm --
export NODE_OPTIONS=--dns-result-order=ipv4first # or else fails on colab.
echo "Installing nvm..."
curl -so- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash > /dev/null 2>&1
@ -117,7 +117,7 @@ if [ "$NEED_NODE" = true ]; then
set +u
[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"
# ── Fix npmrc conflict with nvm ──
# -- Fix npmrc conflict with nvm --
# System npm (apt, conda, etc.) may have written `prefix` or `globalconfig`
# to ~/.npmrc, which is incompatible with nvm and causes "nvm use" to fail
# with: "has a `globalconfig` and/or a `prefix` setting, which are
@ -129,12 +129,12 @@ if [ "$NEED_NODE" = true ]; then
fi
fi
# ── 3. Install Node LTS ──
# -- 3. Install Node LTS --
echo "Installing Node LTS..."
run_quiet "nvm install" nvm install --lts
nvm use --lts > /dev/null 2>&1
set -u
# ── 4. Verify versions ──
# -- 4. Verify versions --
NODE_MAJOR=$(node -v | sed 's/v//' | cut -d. -f1)
NPM_MAJOR=$(npm -v | cut -d. -f1)
@ -150,7 +150,7 @@ fi
echo "✅ Node $(node -v) | npm $(npm -v)"
# ── 5. Build frontend ──
# -- 5. Build frontend --
cd "$SCRIPT_DIR/frontend"
# Tailwind v4's oxide scanner respects .gitignore in parent directories.
@ -194,16 +194,16 @@ echo "✅ Frontend built to frontend/dist"
fi # end frontend build check
# ── oxc-validator runtime (needs npm -- skip if not available) ──
# -- oxc-validator runtime (needs npm -- skip if not available) --
if [ -d "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator" ] && command -v npm &>/dev/null; then
cd "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator"
run_quiet "npm install (oxc validator runtime)" npm install
cd "$SCRIPT_DIR"
fi
# ── 6. Python venv + deps ──
# -- 6. Python venv + deps --
# ── 6a. Discover best Python >= 3.11 and < 3.14 (i.e. 3.11.x, 3.12.x, or 3.13.x) ──
# -- 6a. Discover best Python >= 3.11 and < 3.14 (i.e. 3.11.x, 3.12.x, or 3.13.x) --
MIN_PY_MINOR=11 # minimum minor version (>= 3.11)
MAX_PY_MINOR=13 # maximum minor version (< 3.14)
BEST_PY=""
@ -274,7 +274,7 @@ if [ -z "$BEST_PY" ]; then
fi
BEST_VER=$("$BEST_PY" --version 2>&1 | awk '{print $2}')
echo "✅ Using $BEST_PY ($BEST_VER) — compatible (3.${MIN_PY_MINOR}.x – 3.${MAX_PY_MINOR}.x)"
echo "✅ Using $BEST_PY ($BEST_VER) -- compatible (3.${MIN_PY_MINOR}.x - 3.${MAX_PY_MINOR}.x)"
REQ_ROOT="$SCRIPT_DIR/backend/requirements"
SINGLE_ENV_CONSTRAINTS="$REQ_ROOT/single-env/constraints.txt"
@ -311,7 +311,7 @@ else
source "$VENV_DIR/bin/activate"
fi
# ── Ensure uv is available (much faster than pip) ──
# -- Ensure uv is available (much faster than pip) --
USE_UV=false
if command -v uv &>/dev/null; then
USE_UV=true
@ -331,7 +331,7 @@ fast_install() {
cd "$SCRIPT_DIR"
install_python_stack
# ── 6b. Pre-install transformers 5.x into .venv_t5/ ──
# -- 6b. Pre-install transformers 5.x into .venv_t5/ --
# Models like GLM-4.7-Flash need transformers>=5.3.0. Instead of pip-installing
# at runtime (slow, ~10-15s), we pre-install into a separate directory.
# The training subprocess just prepends .venv_t5/ to sys.path -- instant switch.
@ -346,7 +346,7 @@ run_quiet "install hf_xet for t5" fast_install --target "$VENV_T5_DIR" --no-deps
run_quiet "install tiktoken for t5" fast_install --target "$VENV_T5_DIR" "tiktoken"
echo "✅ Transformers 5.x pre-installed to $VENV_T5_DIR/"
# ── 7. WSL: pre-install GGUF build dependencies ──
# -- 7. WSL: pre-install GGUF build dependencies --
# On WSL, sudo requires a password and can't be entered during GGUF export
# (runs in a non-interactive subprocess). Install build deps here instead.
if grep -qi microsoft /proc/version 2>/dev/null; then
@ -407,8 +407,8 @@ if grep -qi microsoft /proc/version 2>/dev/null; then
fi
fi
# ── 8. Build llama.cpp binaries for GGUF inference + export ──
# Builds at ~/.unsloth/llama.cpp — a single shared location under the user's
# -- 8. Build llama.cpp binaries for GGUF inference + export --
# Builds at ~/.unsloth/llama.cpp -- a single shared location under the user's
# home directory. This is used by both the inference server and the GGUF
# export pipeline (unsloth-zoo).
# - llama-server: for GGUF model inference
@ -427,11 +427,11 @@ rm -rf "$LLAMA_CPP_DIR"
# Check prerequisites
if ! command -v cmake &>/dev/null; then
echo ""
echo "⚠️ cmake not found — skipping llama-server build (GGUF inference won't be available)"
echo "⚠️ cmake not found -- skipping llama-server build (GGUF inference won't be available)"
echo " Install cmake and re-run setup.sh to enable GGUF inference."
elif ! command -v git &>/dev/null; then
echo ""
echo "⚠️ git not found — skipping llama-server build (GGUF inference won't be available)"
echo "⚠️ git not found -- skipping llama-server build (GGUF inference won't be available)"
else
echo ""
echo "Building llama-server for GGUF inference..."
@ -495,7 +495,7 @@ rm -rf "$LLAMA_CPP_DIR"
# Multi-threaded nvcc compilation (uses all CPU cores per .cu file)
CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_FLAGS=--threads=0"
elif [ -d /usr/local/cuda ] || nvidia-smi &>/dev/null; then
echo " CUDA driver detected but nvcc not found — building CPU-only"
echo " CUDA driver detected but nvcc not found -- building CPU-only"
echo " To enable GPU: install cuda-toolkit or add nvcc to PATH"
else
echo " Building CPU-only (no CUDA detected)..."
@ -519,7 +519,7 @@ rm -rf "$LLAMA_CPP_DIR"
# Also build llama-quantize (needed by unsloth-zoo's GGUF export pipeline)
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "build llama-quantize" cmake --build "$LLAMA_CPP_DIR/build" --config Release --target llama-quantize -j"$NCPU" || true
# Symlink to llama.cpp root — check_llama_cpp() looks for the binary there
# Symlink to llama.cpp root -- check_llama_cpp() looks for the binary there
QUANTIZE_BIN="$LLAMA_CPP_DIR/build/bin/llama-quantize"
if [ -f "$QUANTIZE_BIN" ]; then
ln -sf build/bin/llama-quantize "$LLAMA_CPP_DIR/llama-quantize"
@ -530,13 +530,13 @@ rm -rf "$LLAMA_CPP_DIR"
if [ -f "$LLAMA_SERVER_BIN" ]; then
echo "✅ llama-server built at $LLAMA_SERVER_BIN"
else
echo "⚠️ llama-server binary not found after build — GGUF inference won't be available"
echo "⚠️ llama-server binary not found after build -- GGUF inference won't be available"
fi
if [ -f "$LLAMA_CPP_DIR/llama-quantize" ]; then
echo "✅ llama-quantize available for GGUF export"
fi
else
echo "⚠️ llama-server build failed — GGUF inference won't be available, but everything else works"
echo "⚠️ llama-server build failed -- GGUF inference won't be available, but everything else works"
fi
fi
}

2
unsloth/models/rl_replacements.py
View file

@ -1373,7 +1373,7 @@ def openenv_vllm_reload_weights():
except (ImportError, NameError, Exception) as e:
logger.info(f"Unsloth: Failed to import trl openenv: {e}")
logger.info(
"Unsloth: trl.experimental.openenv not available — skipping RL openenv patches."
"Unsloth: trl.experimental.openenv not available -- skipping RL openenv patches."
)
return

6
unsloth/save.py
View file

@ -1537,7 +1537,7 @@ def create_huggingface_repo(
card.data.datasets = datasets
card.push_to_hub(save_directory, token = token)
except:
# Repo already exists — update datasets metadata separately
# Repo already exists -- update datasets metadata separately
if datasets:
try:
from huggingface_hub import metadata_update
@ -1593,7 +1593,7 @@ def upload_to_huggingface(
card.data.datasets = datasets
card.push_to_hub(save_directory, token = token)
except:
# Repo already exists — update datasets metadata separately
# Repo already exists -- update datasets metadata separately
if datasets:
try:
from huggingface_hub import metadata_update
@ -1991,7 +1991,7 @@ def unsloth_save_pretrained_gguf(
except Exception as e:
raise RuntimeError(f"Failed to save/merge model: {e}")
else:
# Non-PEFT model — checkpoint files already exist on disk.
# Non-PEFT model -- checkpoint files already exist on disk.
# Point save_to_gguf at the original checkpoint path instead of
# re-saving to a temporary "model" subdirectory.
original_path = getattr(self.config, "_name_or_path", None)

12
unsloth_cli/commands/studio.py
View file

@ -31,7 +31,7 @@ def _studio_venv_python() -> Optional[Path]:
def _find_run_py() -> Optional[Path]:
"""Find studio/backend/run.py.
No CWD dependency — works from any directory.
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 unsloth_cli/.
"""
@ -52,7 +52,7 @@ def _find_run_py() -> Optional[Path]:
def _find_setup_script() -> Optional[Path]:
"""Find studio/setup.sh or studio/setup.ps1.
No CWD dependency — works from any directory.
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)
@ -69,7 +69,7 @@ def _find_setup_script() -> Optional[Path]:
return None
# ── unsloth studio (server) ──────────────────────────────────────────
# -- unsloth studio (server) ------------------------------------------
@studio_app.callback(invoke_without_command = True)
@ -116,7 +116,7 @@ def studio_default(
try:
rc = proc.wait()
except KeyboardInterrupt:
# Child has its own signal handler — let it finish
# Child has its own signal handler -- let it finish
rc = proc.wait()
if rc != 0:
typer.echo(
@ -166,7 +166,7 @@ def studio_default(
typer.echo("\nShutting down...")
# ── unsloth studio setup ─────────────────────────────────────────────
# -- unsloth studio setup ---------------------------------------------
@studio_app.command()
@ -188,7 +188,7 @@ def setup():
raise typer.Exit(result.returncode)
# ── unsloth studio reset-password ────────────────────────────────────
# -- unsloth studio reset-password ------------------------------------
@studio_app.command("reset-password")

2
unsloth_cli/commands/ui.py
View file

@ -63,7 +63,7 @@ def ui(
try:
rc = proc.wait()
except KeyboardInterrupt:
# Child has its own signal handler — let it finish
# Child has its own signal handler -- let it finish
rc = proc.wait()
raise typer.Exit(rc)
else: