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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)
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