---
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.