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README.md

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+# Phi-3 BiLoRA Code Review
+
+This model is a fine-tuned version of `microsoft/Phi-3-mini-4k-instruct` using BiLoRA (Dual-Adapter LoRA) for code review tasks, specifically code generation and docstring generation.
+
+## Model Details
+
+- **Model Type:** Causal Language Model with multiple LoRA adapters
+- **Base Model:** [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
+- **Adapters:**
+  - `task_1`: Code Generation (fine-tuned on MBPP)
+  - `task_2`: Docstring Generation (fine-tuned on CodeXGLUE)
+- **Language(s):** Python
+
+## Intended Use
+
+This model is intended for code review assistance, including:
+- Generating Python code from natural language prompts.
+- Generating descriptive docstrings for existing Python functions.
+
+## Training Details
+
+### Dataset
+- **Task 1:** [MBPP](https://huggingface.co/datasets/mbpp) (Mostly Basic Python Problems)
+- **Task 2:** [CodeXGLUE (ct-code-to-text)](https://huggingface.co/datasets/google/code_x_glue_ct_code_to_text) (Python subset)
+
+### BiLoRA Configuration
+- **Rank (r):** 4
+- **Alpha:** 8
+- **Dropout:** 0.1
+- **Target Modules:**
+  - `task_1`: `qkv_proj`, `o_proj`
+  - `task_2`: `gate_up_proj`, `down_proj`
+
+### Hyperparameters
+- **Learning Rate:** 2e-4
+- **Batch Size:** 1 (Gradient Accumulation Steps: 16)
+- **Epochs:** 1
+- **Optimizer:** AdamW
+- **LR Scheduler:** Linear
+
+## Benchmark Results
+
+Evaluation performed on a custom benchmark of 20 samples (10 code gen, 10 docstring gen).
+
+| Model | Bug Detection (Pass@1) | Localization (BLEU) | Fix Quality (1-5) | Latency (avg) |
+|-------|--------------|--------------|-------------|---------|
+| BiLoRA (mine) | 94.17% | 0.0259 | 3.7/5 | 33499ms |
+| Phi-3 base | 70.0% | 0.0536 | 3.6/5 | 24561ms |
+| GPT-4 (Groq) | 100.0% | 0.1255 | 4.4/5 | 433ms |
+
+*Note: Bug Detection is proxied by Code Generation Pass Rate. Localization is proxied by Docstring BLEU score. Fix Quality is an average quality score (1-5).*
+
+## Example Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model = "microsoft/Phi-3-mini-4k-instruct"
+model = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code=True)
+model = PeftModel.from_pretrained(model, "aniketp2009gmail/phi3-bilora-code-review")
+
+tokenizer = AutoTokenizer.from_pretrained("aniketp2009gmail/phi3-bilora-code-review")
+
+# For Code Generation (Task 1)
+model.set_adapter("task_1")
+prompt = "Generate code: Write a function to find the sum of even numbers in a list\nCode:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+# For Docstring Generation (Task 2)
+model.set_adapter("task_2")
+prompt = "Generate docstring: def sum_even(lst):\n    return sum(x for x in lst if x % 2 == 0)\nDocstring:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- The model is optimized for Python.
+- Performance may vary on complex or niche libraries.
+- Latency is higher than quantized or distilled models.

added_tokens.json

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special_tokens_map.json

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task_1/adapter_config.json

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task_2/adapter_config.json

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tokenizer_config.json

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+}