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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
The DPO checkpoint underperformed SFT on the 62-prompt held-out test set (flamapy validator). See the SFT repos below for the headline numbers (77.42% raw / 96.77% after dedup).
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = "Qwen/Qwen2.5-Coder-7B-Instruct"tok = AutoTokenizer.from_pretrained("jagalindo/uvl-qwen2.5-coder-7b-dpo-lora")model = AutoModelForCausalLM.from_pretrained(base, dtype="bfloat16", device_map="auto")model = PeftModel.from_pretrained(model, "jagalindo/uvl-qwen2.5-coder-7b-dpo-lora") 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))For best results, sample a few candidates and keep the first that parses with flamapy (retry-until-valid).
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. As noted above, prefer the SFT checkpoint for actual generation.
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-dpo-lora — this adapter: DPO LoRA (experimental; regressed vs SFT)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-sft-merged — merged bf16 full model (CUDA-ready, transformers)jagalindo/uvl-qwen2.5-coder-7b-sft — fused MLX 4-bit model (Apple Silicon / mlx-lm)from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = "Qwen/Qwen2.5-Coder-7B-Instruct"tok = AutoTokenizer.from_pretrained("jagalindo/uvl-qwen2.5-coder-7b-dpo-lora")model = AutoModelForCausalLM.from_pretrained(base, dtype="bfloat16", device_map="auto")model = PeftModel.from_pretrained(model, "jagalindo/uvl-qwen2.5-coder-7b-dpo-lora") 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))