A standalone, MLX 4-bit quantized model (Qwen2.5-Coder-7B-Instruct) fine-tuned to generate UVL (Universal Variability Language) feature models from a domain description and structural constraints (feature count, cross-tree constraints, depth, UVL level).

This is the fused model: the SFT LoRA adapter has been merged into the base, so it runs directly with mlx-lm on Apple Silicon — no separate adapter or base download needed. For a portable adapter you can apply to the full-precision base, use the LoRA repos linked below.

Trained with fine-tunning-uvl on the good/bad generations of the UVL_LLM_Guided_Generation benchmark, supervised on the valid (flamapy-parseable) outputs with prompt masking.

Results

Valid-UVL rate on a 62-prompt held-out test set (flamapy validator):

single-shot, raw: 77.42%
single-shot + dedup: 96.77%
SFT + retry-until-valid(5) + light post-processing: 96.77%

Usage

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("jagalindo/uvl-qwen2.5-coder-7b-sft")

messages = [
    {"role": "system", "content": "You are an expert in Software Product Lines ... generate valid UVL ..."},
    {"role": "user",   "content": "## Your task\nGenerate a valid UVL feature model for ..."},
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=1536, verbose=True))

For best results, sample a few candidates and keep the first that parses with flamapy (retry-until-valid), then apply the project's light post-processing.

Limitations

Small/medium model on a niche DSL; not every generation parses on the first try. Use retry-until-valid + the project's light post-processing (dedup feature names, drop dangling constraints, quote non-identifier namespaces) to reach the headline rate. The 4-bit quantization trades a little fidelity for size/speed; the full-precision LoRA adapter is available if you need it.

Related models

Part of the fine-tunning-uvl project — three artifacts from the same training run, all based on Qwen/Qwen2.5-Coder-7B-Instruct:

jagalindo/uvl-qwen2.5-coder-7b-sft — this model: fused full model (MLX 4-bit, runs standalone via mlx-lm)
jagalindo/uvl-qwen2.5-coder-7b-sft-merged — merged bf16 full model (CUDA-ready, transformers)
jagalindo/uvl-qwen2.5-coder-7b-sft-lora — SFT LoRA adapter (apply on the full-precision base; recommended checkpoint)
jagalindo/uvl-qwen2.5-coder-7b-dpo-lora — DPO LoRA adapter (experimental; regressed vs SFT)