TIMPS-Coder-7B
TIMPS-Coder-7B is a code-generation model built by fine-tuning Qwen2.5-Coder-7B-Instruct
through a 3-step pipeline: SFT, GRPO, DPO.
Benchmark Results
| Benchmark | Score |
|---|
| HumanEval pass@1 | 98.8% |
| HumanEval+ pass@1 | 82.9% |
| MBPP pass@1 | 5.4% |
| MBPP+ pass@1 | 73.3% |
Comparison with 7B-9B Code Models
| Model | HumanEval | HumanEval+ | MBPP | MBPP+ | Params |
|---|
| TIMPS-Coder-7B (this model) | 98.8 | 82.9 | 5.4 | 73.3 | 7B |
| Qwen2.5-Coder-7B-Instruct | 86.6 | 71.3 | 82.0 | 69.6 | 7.6B |
| Qwen2.5-Coder-7B | 89.6 | 76.2 | 84.0 | 72.0 | 7.6B |
| DeepSeek-Coder-7B-Instruct-v1.5 | 84.1 | 70.8 | 79.6 | 68.4 | 7.1B |
| CodeLlama-7B-Instruct | 53.7 | 44.5 | 55.6 | 45.0 | 6.7B |
| CodeGemma-7B-it | 56.1 | 46.9 | 61.8 | 50.6 | 7.0B |
| StarCoder2-7B | 40.2 | 32.9 | 46.0 | 36.5 | 7.0B |
| Llama-3.1-8B-Instruct | 72.6 | 61.0 | 70.8 | 58.7 | 8.0B |
| Phi-3.5-mini-instruct (3.8B) | 68.8 | 57.9 | 73.0 | 61.3 | 3.8B |
| Gemma-2-9B-it | 54.3 | 44.5 | 59.6 | 49.3 | 9.2B |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("sandeeprdy1729/TIMPS-Coder-7B", device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("sandeeprdy1729/TIMPS-Coder-7B")
messages = [{"role": "user", "content": "Write a fibonacci function."}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(inputs, max_new_tokens=512)[0]))