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