| --- |
| language: |
| - es |
| - en |
| license: apache-2.0 |
| tags: |
| - code |
| - quantum |
| - qwen2 |
| - python |
| - distillation |
| base_model: Qwen/Qwen2.5-Coder-0.5B |
| --- |
| |
| # QuantumCoder-0.5B |
|
|
| Modelo de generación de código entrenado mediante **destilación cuántica**, |
| usando IBM Quantum para optimizar hiperparámetros y Qwen3-Coder-480B |
| como modelo profesor. |
|
|
| ## Proceso |
|
|
| - **Profesor**: Qwen3-Coder-480B (via OpenRouter) |
| - **Optimización**: IBM Quantum (hiperparámetros óptimos) |
| - **Base**: Qwen2.5-Coder-0.5B |
| - **Técnica**: LoRA fine-tuning + Quantum hyperparameter optimization |
|
|
| ## Uso |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| |
| tokenizer = AutoTokenizer.from_pretrained("rod123/QuantumCoder-0.5B") |
| model = AutoModelForCausalLM.from_pretrained( |
| "rod123/QuantumCoder-0.5B", |
| torch_dtype=torch.float16, |
| device_map="auto" |
| ) |
| |
| prompt = """### Instrucción: |
| Escribe una función Python que calcule fibonacci |
| |
| ### Respuesta: |
| """ |
| |
| inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
| outputs = model.generate(**inputs, max_new_tokens=200) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| ``` |
|
|
| ## Roadmap |
|
|
| - [ ] QuantumCoder-7B |
| - [ ] QuantumCoder-32B → 6B |
| - [ ] Benchmarks HumanEval |