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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- code |
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- codeact |
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- python |
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- mlx |
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- lora |
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base_model: Qwen/Qwen2.5-3B |
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pipeline_tag: text-generation |
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--- |
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# CodeAct Fine-tuned Qwen2.5-3B |
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A fine-tuned version of Qwen2.5-3B for code generation with self-evaluation feedback. |
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## Model Description |
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This model was fine-tuned using the CodeAct approach with: |
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- **Base Model:** Qwen/Qwen2.5-3B |
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- **Training Method:** LoRA (Low-Rank Adaptation) |
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- **Training Data:** 100 curated Python programming examples |
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- **Categories:** Math, Strings, Lists, Algorithms, Data Structures |
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## Usage |
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### With MLX (Apple Silicon) |
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```python |
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from mlx_lm import load, generate |
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model, tokenizer = load("Phoenix21/codeact-qwen2.5-3b") |
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# Or with adapter: |
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# model, tokenizer = load("Qwen/Qwen2.5-3B", adapter_path="Phoenix21/codeact-qwen2.5-3b") |
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response = generate(model, tokenizer, prompt="Calculate factorial of 5", max_tokens=200) |
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print(response) |
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``` |
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### With PyTorch (CUDA/CPU) |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftModel |
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base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B", trust_remote_code=True) |
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model = PeftModel.from_pretrained(base_model, "Phoenix21/codeact-qwen2.5-3b") |
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B", trust_remote_code=True) |
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``` |
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### Interactive Demo |
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```bash |
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# Auto-detect backend (MLX/CUDA/CPU) |
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python interactive_universal.py |
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# Force specific backend |
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python interactive_universal.py --backend cuda |
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python interactive_universal.py --backend mlx |
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python interactive_universal.py --backend cpu |
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``` |
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## Training Details |
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- **Iterations:** 500 |
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- **Batch Size:** 1 |
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- **LoRA Layers:** 16 |
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- **Learning Rate:** 1e-5 |
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- **Platform:** Apple M3 (MLX) |
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## Response Format |
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The model uses structured tags: |
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- `<thought>reasoning</thought>` - Chain of thought |
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- `<execute>code</execute>` - Python code to execute |
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- `<solution>answer</solution>` - Final answer |
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- `<feedback>assessment</feedback>` - Self-evaluation |
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## Example |
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**Input:** "Calculate the sum of squares from 1 to 10" |
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**Output:** |
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``` |
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<thought>Sum of squares formula: n(n+1)(2n+1)/6</thought> |
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<execute> |
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n = 10 |
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result = n * (n + 1) * (2 * n + 1) // 6 |
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print(result) |
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</execute> |
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<solution>Sum of squares from 1 to 10 is 385</solution> |
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<feedback> |
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score: 10 |
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correctness: correct |
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efficiency: excellent |
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explanation: Used O(1) formula instead of O(n) loop |
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</feedback> |
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``` |
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## License |
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Apache 2.0 |
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