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

License

Apache 2.0 (same as base Qwen model)