Update README.md

README.md

@@ -1,74 +1,74 @@
----
-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
-
-| Model | BigCodeBench (Hard) |
-|-------|---------------------|
-| Base Qwen 2.5 7B Coder | 40% |
-| **Bently Coder 7B** | **92%** |
-
-**+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)
+---
+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)