| --- |
| 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) |
|
|
