---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
  - code
  - qwen
  - fine-tuned
  - qlora
language:
  - en
pipeline_tag: text-generation
---

# Bently Coder 7B

A fine-tuned coding model based on [Qwen 2.5 Coder 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct), trained on personal GitHub repositories using QLoRA.

## Results

| Benchmark | Base Qwen 2.5 7B | Bently Coder v1 | Improvement |
|-----------|------------------|-----------------|-------------|
| BigCodeBench Hard | 40% | **92%** | +52pp |
| HumanEval | 50% | **86%** | +36pp |

**+52 percentage points over base model.**

## Key Findings

- **Your code only works better** — Training exclusively on personal repos outperformed mixed datasets with popular open source
- **2 epochs is optimal** — More epochs caused overfitting (4 epochs dropped to 66%)
- **Quality > quantity** — 7k samples from personal repos beat 15k mixed samples

## Usage

### Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Bentlybro/bently-coder-7b", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Bentlybro/bently-coder-7b")

prompt = "### Instruction:\nWrite a Python function to reverse a linked list\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Ollama

Convert to GGUF and create a Modelfile, or download quantized versions (if available).

## Training Details

- **Base model:** Qwen/Qwen2.5-Coder-7B-Instruct
- **Method:** QLoRA (4-bit quantization)
- **Epochs:** 2
- **Hardware:** RTX 3060 12GB
- **Dataset:** ~7,000 instruction-code pairs from personal GitHub repos
- **Task distribution:** write (~51%), complete (~17%), explain (~15%), refactor (~10%), document (~4%)

## Limitations

This model is fine-tuned on a single developer's coding style. It may:
- Prefer certain patterns, naming conventions, or structures specific to that style
- Perform differently on codebases with vastly different conventions

## Training Code

Full training pipeline available at: [github.com/Bentlybro/bently-coder-llm](https://github.com/Bentlybro/bently-coder-llm)

## License

Apache 2.0 (same as base Qwen model)