| --- |
| license: apache-2.0 |
| datasets: |
| - AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset |
| language: |
| - en |
| base_model: |
| - Qwen/Qwen3-0.6B |
| pipeline_tag: text-generation |
| tags: |
| - qwen |
| - qlora |
| - fine-tuning |
| - code-generation |
| - agentic-ai |
| - transformers |
| - peft |
| - pytorch |
| --- |
| |
| # Qwen3-Agentic-Coder-0.6B |
|
|
| A QLoRA fine-tuned version of Qwen3-0.6B specialized for structured agentic coding assistance and software architecture reasoning. |
|
|
| This model was fine-tuned locally on an RTX 3050 Laptop GPU using parameter-efficient fine-tuning (QLoRA). |
|
|
| --- |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| Qwen3-Agentic-Coder-0.6B is a lightweight coding-focused assistant designed to generate: |
|
|
| * structured engineering responses |
| * implementation plans |
| * architecture explanations |
| * coding assistant style outputs |
| * software system design guidance |
|
|
| The fine-tuning process focused on improving: |
|
|
| * response structure |
| * engineering-oriented reasoning |
| * copilot-like behavior |
| * concise technical explanations |
|
|
| --- |
|
|
| ## Training Details |
|
|
| | Component | Value | |
| | -------------------- | -------------------------------- | |
| | Base Model | Qwen/Qwen3-0.6B | |
| | Fine-Tuning Method | QLoRA | |
| | GPU | NVIDIA RTX 3050 Laptop GPU | |
| | Frameworks | Transformers, PEFT, bitsandbytes | |
| | Training Environment | Local Windows Setup | |
| | Dataset Type | Agentic Coding SFT | |
|
|
| --- |
|
|
| ## Dataset |
|
|
| Fine-tuned using a cleaned subset of: |
|
|
| AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset |
|
|
| Preprocessing steps included: |
|
|
| * removing excessive chain-of-thought traces |
| * removing verbose reasoning blocks |
| * truncating oversized responses |
| * formatting into chat-style conversations |
|
|
| This improved: |
|
|
| * training stability |
| * VRAM efficiency |
| * response quality |
| * inference speed |
|
|
| --- |
|
|
| ## Features |
|
|
| * Lightweight local inference |
| * Structured software engineering responses |
| * Architecture-oriented outputs |
| * Coding copilot style formatting |
| * QLoRA optimized deployment |
|
|
| --- |
|
|
| ## Example Usage |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| model_name = "Flare0p/Qwen3-Agentic-Coder-0.6B" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| |
| model = AutoModelForCausalLM.from_pretrained(model_name) |
| |
| prompt = "Design a scalable authentication system for microservices." |
| |
| inputs = tokenizer(prompt, return_tensors="pt") |
| |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=200 |
| ) |
| |
| print(tokenizer.decode(outputs[0])) |
| ``` |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| This model is intended for: |
|
|
| * educational AI engineering projects |
| * lightweight coding assistance |
| * local LLM experimentation |
| * software architecture guidance |
| * research into efficient fine-tuning |
|
|
| --- |
|
|
| ## Limitations |
|
|
| This is a small 0.6B parameter model and may: |
|
|
| * hallucinate technical details |
| * produce incomplete code |
| * struggle with highly complex reasoning |
| * require prompt engineering for best results |
|
|
| --- |
|
|
| ## Hardware Used |
|
|
| * NVIDIA RTX 3050 Laptop GPU |
| * Python 3.10 |
| * PyTorch CUDA 12.1 |
|
|
| --- |
|
|
| ## Notes |
|
|
| This project demonstrates: |
|
|
| * local LLM fine-tuning |
| * QLoRA workflows |
| * dataset preprocessing |
| * Hugging Face model publishing |
| * consumer GPU AI development |
|
|
| The entire workflow was completed locally using consumer hardware. |
