Instructions to use Flare0p/Qwen3-Agentic-Coder-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Flare0p/Qwen3-Agentic-Coder-0.6B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Flare0p/Qwen3-Agentic-Coder-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Flare0p/Qwen3-Agentic-Coder-0.6B", dtype="auto")

PEFT
How to use Flare0p/Qwen3-Agentic-Coder-0.6B with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Flare0p/Qwen3-Agentic-Coder-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Flare0p/Qwen3-Agentic-Coder-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Flare0p/Qwen3-Agentic-Coder-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Flare0p/Qwen3-Agentic-Coder-0.6B

SGLang

How to use Flare0p/Qwen3-Agentic-Coder-0.6B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Flare0p/Qwen3-Agentic-Coder-0.6B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Flare0p/Qwen3-Agentic-Coder-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Flare0p/Qwen3-Agentic-Coder-0.6B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Flare0p/Qwen3-Agentic-Coder-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Flare0p/Qwen3-Agentic-Coder-0.6B with Docker Model Runner:
```
docker model run hf.co/Flare0p/Qwen3-Agentic-Coder-0.6B
```

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

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.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for Flare0p/Qwen3-Agentic-Coder-0.6B

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B