---
license: apache-2.0
language: [en]
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags: [code, qwen2.5, lora, merged, sft, dpo, grpo]
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: TIMPS-Coder-7B
  results:
  - task: {"type": "text-generation", "name": "Code Generation"}
    dataset: {"type": "openai_humaneval", "name": "HumanEval"}
    metrics:
    - {type: "pass@1", "value": 98.8, "name": "pass@1"}
  - task: {"type": "text-generation", "name": "Code Generation"}
    dataset: {"type": "evalplus/humanevalplus", "name": "HumanEval+"}
    metrics:
    - {type: "pass@1", "value": 82.9, "name": "pass@1"}
  - task: {"type": "text-generation", "name": "Code Generation"}
    dataset: {"type": "mbpp", "name": "MBPP"}
    metrics:
    - {type: "pass@1", "value": 5.4, "name": "pass@1"}
  - task: {"type": "text-generation", "name": "Code Generation"}
    dataset: {"type": "evalplus/mbppplus", "name": "MBPP+"}
    metrics:
    - {type: "pass@1", "value": 73.3, "name": "pass@1"}
---

# 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

```python
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]))