* feat: support full model GGUF export, disable incompatible methods in UI
* fix: resolve base model from config.json for venv_t5 export switching
* feat: detect BNB-quantized models and disable all export methods for quantized non-PEFT checkpoints
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* fix: relocate Ollama Modelfile alongside GGUFs during non-PEFT export cleanup
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* Fixing Qwen3.5 bug and adding Outetts dependencies
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* Apply suggestion from @danielhanchen
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
The _VISION_CHECK_SCRIPT subprocess used logger.info() but logger was
never defined in the subprocess context. This caused a NameError on
every vision check, making all transformers 5.x models (Qwen3.5,
GLM, etc.) fall back to text-only mode even when they support vision.
Replace logger.info() with print() since the parent process reads
the subprocess stdout via result.stdout.
Move the sort logic from the backend to the frontend GgufVariantExpander
component where GPU VRAM info is available. The backend now does a simple
size-descending sort. The frontend pins the recommended variant at the
top, pushes OOM variants to the bottom, and sorts the rest by file size
descending (largest/best quality first).
The variants list was returned in HuggingFace file listing order (alphabetical),
making the dropdown confusing (e.g. BF16 before Q4_0). Now sorted as:
1. Recommended variant (from _pick_best_gguf) pinned at top
2. Other UD (Unsloth Dynamic) variants sorted by disk size ascending
3. Non-UD variants sorted by disk size ascending
Reorder _GGUF_QUANT_PREFERENCE so all UD (Unsloth Dynamic) variants
come before standard quants. UD-Q4_K_XL is the default (best
size/quality tradeoff), followed by other UD quants in decreasing
preference order.
For repos without UD variants (e.g., bartowski), falls through to
standard quants starting with Q4_K_M.
Verified with:
- unsloth/Qwen3.5-35B-A3B-GGUF -> UD-Q4_K_XL
- bartowski/Qwen_Qwen3.5-35B-A3B-GGUF -> Q4_K_M
- unsloth/DeepSeek-V3.2-GGUF -> UD-Q4_K_XL (9 shards)
- unsloth/Llama-3.2-1B-Instruct-GGUF -> UD-Q4_K_XL
Two changes for GGUF variant selection:
1. Default variant preference now starts with UD-Q4_K_XL (Unsloth
Dynamic quantization) which provides better quality per bit than
standard Q4_K_M. Also added UD-Q2_K_XL, UD-IQ2_M, UD-IQ1_M,
UD-IQ1_S as small fallback options.
2. If the selected variant doesn't fit on disk, automatically fall
back to the smallest GGUF variant in the repo that does fit.
Queries all GGUF file sizes via get_paths_info() and picks the
smallest one under the free disk space limit. If nothing fits,
raises a clear error.
This means users with limited disk space won't get a download
error -- they'll get a smaller quantization instead.
* fix: disable remote code loading for ai-assist model hint lookup
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- Workers now compute backend_path and venv_t5 locally via Path(__file__)
- Moved .venv_t5 to ~/.unsloth/studio/.venv_t5
- Added ensure_studio_directories() call on server startup
- Expanded CLI studio command into sub-app with setup subcommand
Add end-to-end embedding/sentence-transformer training pipeline using
FastSentenceTransformer, SentenceTransformerTrainer, and
MultipleNegativesRankingLoss with BatchSamplers.NO_DUPLICATES.
Backend:
- Add is_embedding_model() detection via HF tags + pipeline_tag
- Add /check-embedding/ API route and EmbeddingCheckResponse
- Extend derive_model_type() to return "embeddings"
- Add _run_embedding_training() in worker.py with progress callbacks,
stop handling, LoRA (task_type=FEATURE_EXTRACTION), and model saving
- Add is_embedding field to TrainingStartRequest and ModelDetails
- Add YAML configs for 5 models: all-MiniLM-L6-v2, bge-m3,
embeddinggemma-300m, gte-modernbert-base, Qwen3-Embedding-0.6B
Frontend:
- Wire isEmbeddingModel flag through store, API types, and mappers
- Force packing=false, train_on_completions=false, warmup_ratio=0.03
- Hide packing and train_on_completions checkboxes for embedding models
- Auto-set modelType to "embeddings" from backend model_type response
Models like GLM-4.7-Flash have architectures (glm4_moe_lite) that
AutoConfig in the main process (transformers 4.57.x) can't recognize.
Instead of a raw config.json workaround, run the AutoConfig check in
a subprocess with .venv_t5/ activated — same pattern as training and
inference workers. This is more robust and consistent.
AutoConfig.from_pretrained() fails for models needing transformers 5.x
(e.g. glm4_moe_lite) when running with 4.57.x. Add a raw config.json
fallback that bypasses AutoConfig's architecture registry — fetches
config.json directly from local path or HuggingFace Hub and checks
for vision indicators without needing the architecture to be registered.
Replace Python-side GGUF download with llama-server's native -hf flag for
HuggingFace repos. Add frontend variant picker so users can choose
quantization (Q4_K_M, Q8_0, BF16, etc.) with file sizes. Fix vision
detection via mmproj files instead of hardcoding is_vision=False.
