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--- |
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license: apache-2.0 |
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base_model: Qwen/Qwen2.5-Coder-14B |
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tags: |
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- code |
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- qwen2.5 |
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- lora-merged |
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- fine-tuned |
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library_name: transformers |
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--- |
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# PoPilot - Fine-tuned Qwen2.5-Coder-14B |
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This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B) with LoRA adapters merged. |
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## Model Details |
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- **Base Model**: Qwen/Qwen2.5-Coder-14B |
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- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) |
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- **Training**: Supervised Fine-Tuning (SFT) |
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- **Merged**: Full model weights (LoRA merged with base) |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained( |
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"Justin6657/PoPilot", |
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torch_dtype="auto", |
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device_map="auto", |
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trust_remote_code=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained( |
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"Justin6657/PoPilot", |
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trust_remote_code=True |
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) |
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# Example usage |
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prompt = "Write a Python function to calculate fibonacci numbers:" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=200, temperature=0.7) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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## Training Details |
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This model was fine-tuned using LoRA adapters and then merged back into the full model weights. |
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Original LoRA checkpoint path: `/net/projects/CLS/DSI_clinic/justin/checkpoint/augmented_train_Qwen2.5-Coder-14B_full-model_repair-synth_repair-simple-phase4` |
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