| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - text-generation |
| - technical-documentation |
| - readme |
| - qwen |
| - qlora |
| pipeline_tag: text-generation |
| base_model: Qwen/Qwen2.5-Coder-7B-Instruct |
| model-index: |
| - name: Tech-Scribe-v1 |
| results: |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: collected_data_external |
| type: tech-docs |
| metrics: |
| - type: loss |
| value: 1.1258 |
| --- |
| |
| # Tech Scribe (Qwen 2.5 7B Fine-tune) |
|
|
| **Tech Scribe** is a specialized language model fine-tuned to generate high-quality, structured technical documentation (READMEs, Model Cards) from simple project descriptions. It is built on top of `Qwen/Qwen2.5-Coder-7B-Instruct` using QLoRA. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from peft import PeftModel |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig |
| |
| # Config for 4-bit loading |
| bnb_config = BitsAndBytesConfig( |
| load_in_4bit=True, |
| bnb_4bit_compute_dtype=torch.float16, |
| bnb_4bit_quant_type="nf4" |
| ) |
| |
| # Load Base Model |
| base_model_name = "Qwen/Qwen2.5-Coder-7B-Instruct" |
| model = AutoModelForCausalLM.from_pretrained( |
| base_model_name, |
| quantization_config=bnb_config, |
| device_map="auto" |
| ) |
| |
| # Load Tech Scribe Adapter |
| adapter_name = "Darmm/tech-scribe-v1" # Example path |
| model = PeftModel.from_pretrained(model, adapter_name) |
| tokenizer = AutoTokenizer.from_pretrained(base_model_name) |
| |
| # Generate |
| project_idea = "A Python library for real-time sentiment analysis using websockets" |
| prompt = f"### Instruction:\nWrite a high-quality technical README or Model Card for the project \"{project_idea}\".\n\n### Response:\n" |
| |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response:")[1]) |
| ``` |
|
|
| ## Model Description |
|
|
| - **Developed by:** Darmm Lab |
| - **Base Model:** `Qwen/Qwen2.5-Coder-7B-Instruct` |
| - **Fine-tuning Method:** QLoRA (4-bit quantization with LoRA adapters) |
| - **Task:** Technical Documentation Generation |
| - **Language:** English |
|
|
| ## Training (summary) |
|
|
| The model was fine-tuned on a curated dataset of high-quality READMEs from top open-source repositories (e.g., PyTorch, FastAPI, React, HuggingFace Transformers). |
|
|
| - **Epochs:** 1 (Prototype run) |
| - **Batch size:** 1 (Gradient Accumulation: 8) |
| - **Learning rate:** 2e-4 |
| - **Optimizer:** AdamW |
| - **Hardware:** NVIDIA A100 80GB |
|
|
| ## Metrics |
|
|
| ```json |
| { |
| "eval_loss": 1.1258, |
| "train_loss": 1.2937, |
| "epoch": 0.73 |
| } |
| ``` |
|
|
| ## Intended Use |
|
|
| - Rapidly generating boilerplate documentation for new software projects. |
| - converting rough notes into structured Markdown documentation. |
| - Learning best practices for technical writing structure. |
|
|
| ## Limitations |
|
|
| - **Prototype Status:** This model was trained on a small subset of data for demonstration purposes. |
| - **Hallucination:** Like all LLMs, it may generate plausible-sounding but incorrect installation instructions or API calls. Always verify the generated code. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{techscribe2026, |
| author = {Darmm Lab}, |
| title = {Tech Scribe: Automated Technical Documentation Generator}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| journal = {Hugging Face Repository}, |
| howpublished = {\url{https://huggingface.co/Darmm/tech-scribe-v1}} |
| } |
| ``` |
|
|
