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devanshty
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Model Details
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devanshty
Model Tree
Qwen/Qwen2.5-Coder-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Code-Autopsy is a specialized code intelligence model fine-tuned on top of Qwen2.5-Coder-7B-Instruct using 4-bit QLoRA. It operates like an autonomous forensic compiler: given buggy, defective, or vulnerable code snippets across Python, JavaScript, and other languages, it outputs a clean, structured diagnostic report:
The model was trained for 3 full epochs (246 steps) on a curated dataset of code bugs and algorithmic repairs.
| Metric | Initial (Epoch 0.06) | Final (Epoch 3.0) | Delta |
|---|---|---|---|
| Training Loss | 2.162 | 0.255 | -88.2% 📉 |
Validation Loss (eval_loss) | 1.397 | 0.2442 | -82.5% 📉 |
| Token Accuracy | 60.29% | 93.20% | +32.91% 📈 |
| Gradient Norm | 0.27 | 0.39 | Stable |
🌐 Interactive Training Logs & Loss Curves:
View the live dashboard, loss charts, and hardware telemetry on Weights & Biases.
Qwen/Qwen2.5-Coder-7B-Instructbitsandbytes double quant)bfloat161632q_proj, v_projadamw_8bit2e-4 (with Cosine Decay and 5% Warmup)8 (Per-device 1, Gradient Accumulation 8)python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModel BASE_MODEL = "Qwen/Qwen2.5-Coder-7B-Instruct"ADAPTER_REPO = "devanshty/Code-Autopsy" # 1. Load Tokenizer & 4-bit Base Modeltokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True) base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, quantization_config=bnb_config, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) # 2. Load Fine-Tuned Code-Autopsy Adaptermodel = PeftModel.from_pretrained(base_model, ADAPTER_REPO)model.eval() # 3. Format Diagnostic Promptcode_snippet = '''def append_item(val, lst=[]): lst.append(val) return lst''' prompt = f"""<|im_start|>systemYou are a code review expert. Analyze the provided code, identify any bugs or issues, explain the root cause, and provide a corrected version.<|im_end|><|im_start|>userLanguage: python ```python{code_snippet}```<|im_end|><|im_start|>assistant""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, do_sample=False, pad_token_id=tokenizer.eos_token_id ) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))The model generates responses structured in Markdown:
markdown
## Bug IdentifiedMutable default argument `lst=[]` used in function definition. ## Root CauseIn Python, default arguments are evaluated once when the function is defined, not each time it is called. Modifying `lst` mutates the single shared list object across subsequent calls. ## Fixed Code```pythondef append_item(val, lst=None): if lst is None: lst = [] lst.append(val) return lsttext
--- ## 📜 Citation & Credits * **Author:** Devansh Tyagi ([devanshty](https://huggingface.co/devanshty))* **Base Architecture:** Alibaba Cloud Qwen Team (`Qwen2.5-Coder-7B-Instruct`)* **Frameworks:** 🤗 Hugging Face `transformers`, `peft`, `trl`, and Weights & Biases `wandb`.import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModel BASE_MODEL = "Qwen/Qwen2.5-Coder-7B-Instruct"ADAPTER_REPO = "devanshty/Code-Autopsy" # 1. Load Tokenizer & 4-bit Base Modeltokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True) base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, quantization_config=bnb_config, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) # 2. Load Fine-Tuned Code-Autopsy Adaptermodel = PeftModel.from_pretrained(base_model, ADAPTER_REPO)model.eval() # 3. Format Diagnostic Promptcode_snippet = '''def append_item(val, lst=[]): lst.append(val) return lst''' prompt = f"""<|im_start|>systemYou are a code review expert. Analyze the provided code, identify any bugs or issues, explain the root cause, and provide a corrected version.<|im_end|><|im_start|>userLanguage: python ```python{code_snippet}```<|im_end|><|im_start|>assistant""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, do_sample=False, pad_token_id=tokenizer.eos_token_id ) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))## Bug IdentifiedMutable default argument `lst=[]` used in function definition. ## Root CauseIn Python, default arguments are evaluated once when the function is defined, not each time it is called. Modifying `lst` mutates the single shared list object across subsequent calls. ## Fixed Code```pythondef append_item(val, lst=None): if lst is None: lst = [] lst.append(val) return lst---## 📜 Citation & Credits* **Author:** Devansh Tyagi ([devanshty](https://huggingface.co/devanshty))* **Base Architecture:** Alibaba Cloud Qwen Team (`Qwen2.5-Coder-7B-Instruct`)* **Frameworks:** 🤗 Hugging Face `transformers`, `peft`, `trl`, and Weights & Biases `wandb`.