| --- |
| base_model: Qwen/Qwen2.5-Coder-7B-Instruct |
| library_name: transformers |
| model_name: deepwiki-coder-7b-v2 |
| tags: |
| - generated_from_trainer |
| - sft |
| - trl |
| - hf_jobs |
| - ml-intern |
| - deepwiki |
| - code-documentation |
| - technical-writing |
| license: apache-2.0 |
| --- |
| |
| # DeepWiki Coder 7B v2 |
|
|
| ## What? |
|
|
| This is a LoRA adapter fine-tuned from [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) to generate **DeepWiki-style codebase wiki pages** — structured technical documentation with source file references, Mermaid diagrams, Markdown tables, and inline citations. |
|
|
| Because this repository contains adapter weights rather than a standalone base model, it must be loaded with the Qwen base model and PEFT. A merged standalone version is available at [GhostScientist/semanticwiki-coder-7b-v2-merged](https://huggingface.co/GhostScientist/semanticwiki-coder-7b-v2-merged). |
|
|
| ## Why? |
|
|
| The adapter format is small and convenient for experimentation or further fine-tuning. Merging it with the base model makes deployment simpler and is the required preparation step for conversion to Apple-Silicon MLX format. |
|
|
| ## What It Does |
|
|
| Given source code files and a topic, this model generates a complete technical wiki page with: |
|
|
| - **`<details>` source file block** at the top listing all relevant files |
| - **H1/H2/H3 markdown sections** with introduction, detailed analysis, and conclusion |
| - **Mermaid diagrams** (flowchart TD, sequenceDiagram) showing architecture and data flow |
| - **Markdown tables** summarizing key information |
| - **Source citations** in `Sources: [path:line]()` format referencing specific files |
|
|
| ## Training Details |
|
|
| | Parameter | Value | |
| |---|---| |
| | Base model | Qwen/Qwen2.5-Coder-7B-Instruct | |
| | Method | LoRA SFT (Supervised Fine-Tuning) | |
| | LoRA rank | 64 | |
| | LoRA alpha | 128 | |
| | Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj | |
| | Epochs | 4 | |
| | Learning rate | 2e-4 | |
| | Effective batch size | 16 | |
| | Max sequence length | 12288 | |
| | Training examples | 28 | |
| | Training data | [GhostScientist/deepwiki-sft-v2](https://huggingface.co/datasets/GhostScientist/deepwiki-sft-v2) | |
| | Hardware | NVIDIA A100-SXM4-80GB | |
| | Final loss | 0.2488 | |
| | Token accuracy | 92.4% | |
| |
| ### v2 Improvements over v1 |
| |
| | Metric | v1 Training Data | v2 Training Data | |
| |---|---|---| |
| | Proper citation format | 19% | 100% | |
| | Mermaid diagrams | 52% | 100% | |
| | Markdown tables | 0% | 100% | |
| | Avg citations/example | 0.4 | 6.8 | |
| |
| The v2 training data was generated with few-shot prompting and post-processing to enforce DeepWiki format requirements (citations, diagrams, tables) that were missing or inconsistent in v1. |
| |
| ## Usage |
| |
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| import torch |
| |
| base_model_id = "Qwen/Qwen2.5-Coder-7B-Instruct" |
| adapter_model_id = "GhostScientist/semanticwiki-coder-7b-v2" |
| |
| tokenizer = AutoTokenizer.from_pretrained(base_model_id) |
| model = AutoModelForCausalLM.from_pretrained(base_model_id, dtype=torch.bfloat16, device_map="auto") |
| model = PeftModel.from_pretrained(model, adapter_model_id) |
| model = model.merge_and_unload() |
| |
| messages = [ |
| {"role": "system", "content": "You are an expert code analyst..."}, |
| {"role": "user", "content": "<START_OF_CONTEXT>\n[source code here]\n<END_OF_CONTEXT>\n\n<query>\nGenerate a wiki page about...\n</query>"}, |
| ] |
| |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| inputs = tokenizer(text, return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=4096, temperature=0.7, top_p=0.9, do_sample=True) |
| print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)) |
| ``` |
| |
| ## Training Infrastructure |
| |
| - **Training job**: [HF Jobs](https://huggingface.co/jobs/GhostScientist/6aa322ee5527934177ec2d8f) |
| - **Trackio dashboard**: [GhostScientist/ml-intern-d8f3a2b1](https://huggingface.co/spaces/GhostScientist/ml-intern-d8f3a2b1) |
| - **Training script**: TRL SFTTrainer with LoRA via PEFT |
| - **Monitoring**: Trackio with alert callbacks for divergence/NaN/overfitting detection |
| |
| ## Framework Versions |
| |
| - TRL: 1.13.0 |
| - Transformers: 5.17.0 |
| - PyTorch: 2.14.0 |
| - PEFT: 0.20.0 |
| - Datasets: 5.0.1 |
| - Tokenizers: 0.23.2 |
| |
| ## Related Resources |
| |
| - [v1 Model](https://huggingface.co/GhostScientist/deepwiki-coder-7b) (previous iteration) |
| - [v2 Training Dataset](https://huggingface.co/datasets/GhostScientist/deepwiki-sft-v2) |
| - [v1 Eval Results](https://huggingface.co/datasets/GhostScientist/deepwiki-eval-results) |
| |
| ## Citations |
| |
| ```bibtex |
| @software{vonwerra2020trl, |
| title = {{TRL: Transformers Reinforcement Learning}}, |
| author = {vonwerra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, |
| license = {Apache-2.0}, |
| url = {https://github.com/huggingface/trl}, |
| year = {2020} |
| } |
| ``` |
| |
| <!-- ml-intern-provenance --> |
| ## Generated by ML Intern |
| |
| This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub. |
| |
| - Try ML Intern: https://smolagents-ml-intern.hf.space |
| - Source code: https://github.com/huggingface/ml-intern |
| |
