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Upload fine-tuned model

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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ language: code
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+ tags:
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+ - code-generation
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+ - python
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+ - fine-tuned
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+ - qlora
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+ base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
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+ datasets:
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+ - iamtarun/python_code_instructions_18k_alpaca
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+ license: mit
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+ ---
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+
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+ # Qwen2.5-Coder-0.5B Python Fine-tuned
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+
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+ Fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) for Python code generation.
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+
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+ ## Model Details
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+
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+ - **Base Model**: Qwen/Qwen2.5-Coder-0.5B-Instruct
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+ - **Fine-tuning Method**: QLoRA (4-bit quantization + LoRA adapters)
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+ - **Dataset**: iamtarun/python_code_instructions_18k_alpaca
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+ - **Task**: Python code generation from natural language instructions
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+
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+ ## Training Details
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+
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+
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+ - **Training Samples**: 16000
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+ - **Validation Samples**: 1000
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+ - **Epochs**: 3
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+ - **Training Time**: N/A
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+ - **Final Loss**: N/A
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+
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+ ## Performance
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+
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+ - **Syntax Validity**: 95.2%
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+ - **Pass@1**: 54.4%
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+ - **Verbosity Reduction**: 95%
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model = AutoModelForCausalLM.from_pretrained("KpRT/qwen-python-finetuned")
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+ tokenizer = AutoTokenizer.from_pretrained("KpRT/qwen-python-finetuned")
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+
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+ prompt = "Write a function to reverse a string"
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+ outputs = model.generate(**inputs, max_new_tokens=256)
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+ code = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ print(code)
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+ ```
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+
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+ ## Citation
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+
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+ If you use this model, please cite:
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+
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+ ```bibtex
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+ @misc{qwen-python-finetuned,
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+ author = {K R T},
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+ title = {Qwen2.5-Coder Python Fine-tuned},
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+ year = {2026},
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+ publisher = {HuggingFace},
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+ url = {https://huggingface.co/KpRT/qwen-python-finetuned}
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+ }
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+ ```
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