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

Libraries

How to use iromu/Qwen3-0.6B-tools with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iromu/Qwen3-0.6B-tools")
model = AutoModelForCausalLM.from_pretrained("iromu/Qwen3-0.6B-tools", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iromu/Qwen3-0.6B-tools with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/iromu/Qwen3-0.6B-tools

SGLang

How to use iromu/Qwen3-0.6B-tools 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 "iromu/Qwen3-0.6B-tools" \
    --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": "iromu/Qwen3-0.6B-tools",
		"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 "iromu/Qwen3-0.6B-tools" \
        --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": "iromu/Qwen3-0.6B-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Qwen3-0.6B Tools

Qwen3-0.6B fine-tuned with LoRA for tool calling and agent-style interactions.

Base model

This model was fine-tuned from:

Qwen/Qwen3-0.6B

Training

Training was performed using NVIDIA NeMo AutoModel with LoRA/PEFT.

LoRA configuration

LoRA dimension: 32
LoRA alpha: 32
Dropout: 0.05
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Training configuration

Max sequence length: 4096
Learning rate: 5e-5
Weight decay: 0.01
Global batch size: 64 (micro batch 2 x 32 accumulation)
Training steps: 336
Mixed precision: bf16

Dataset

Training used the sft_tools split of the r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation dataset.

Intended use

Structured tool/function calling
Agent-style multi-step interactions
Small-footprint on-device or edge deployment

It is not intended to be a general replacement for larger Qwen models.

Usage

Serve the model:

trtllm-serve serve iromu/Qwen3-0.6B-tools --port 8000

Load it with llama.cpp:

llama-cli -hf iromu/Qwen3-0.6B-tools-GGUF:Q4_K_M

Downloads last month: -

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for iromu/Qwen3-0.6B-tools

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Adapter

(562)

this model

iromu
/

Qwen3-0.6B-tools

Qwen3-0.6B Tools

Base model

Training

LoRA configuration

Training configuration

Dataset

Intended use

Usage

Model tree for iromu/Qwen3-0.6B-tools

Dataset used to train iromu/Qwen3-0.6B-tools