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jagalindo
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Model Details
Model Provider
jagalindo
Model Tree
Qwen/Qwen2.5-Coder-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Valid-UVL rate on a 62-prompt held-out test set (flamapy validator):
python
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "jagalindo/uvl-qwen2.5-coder-7b-sft-merged"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto") 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 ..."},]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)out = model.generate(**inputs, max_new_tokens=1536, do_sample=False)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))~16 GB VRAM in bf16; load with load_in_4bit=True (bitsandbytes) for ~6 GB.
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.
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.
Part of the fine-tunning-uvl
project — artifacts from the same training run, all based on
Qwen/Qwen2.5-Coder-7B-Instruct:
jagalindo/uvl-qwen2.5-coder-7b-sft-merged — this model: merged bf16 full model (CUDA-ready, transformers)jagalindo/uvl-qwen2.5-coder-7b-sft — fused MLX 4-bit model (Apple Silicon / mlx-lm)jagalindo/uvl-qwen2.5-coder-7b-sft-lora — SFT LoRA adapter (apply on the base)jagalindo/uvl-qwen2.5-coder-7b-dpo-lora — DPO LoRA adapter (experimental; regressed vs SFT)from transformers import AutoModelForCausalLM, AutoTokenizer repo = "jagalindo/uvl-qwen2.5-coder-7b-sft-merged"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto") 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 ..."},]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)out = model.generate(**inputs, max_new_tokens=1536, do_sample=False)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))