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

Libraries

How to use Qwen/Qwen3-0.6B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
AMD Developer Cloud
Local Apps Settings

vLLM

How to use Qwen/Qwen3-0.6B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Qwen/Qwen3-0.6B

SGLang

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

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

Reflex — a Rust/CUDA inference engine built for cold-start, with day-one Qwen3/Qwen3.5 support

#42

by Lateos - opened 18 days ago

Discussion

Lateos

18 days ago

Sharing an open-source engine we've been building that takes a different angle than llama.cpp/vLLM: instead of chasing steady-state throughput, it's optimized for cold-start latency and energy — process launch to first token. That's the metric that matters for serverless/scale-to-zero GPU deployments, where you're paying for a cold process spin-up on every invocation, not a long-lived server.

The core trick: every CUDA kernel is compiled ahead-of-time by nvcc at build time, not JIT'd via NVRTC at runtime. That avoids a real multi-second JIT tax that a naive runtime-compilation design pays on every single cold start.

Qwen coverage (all real-hardware-verified, byte-exact against llama.cpp):

Dense Qwen3
Qwen3-MoE (top-k expert routing)
Qwen3.5's hybrid Gated DeltaNet mixer architecture
DeepSeek-V2/V3 MLA, for comparison

Also supports LoRA adapters (--lora, including per-expert MoE adapters), a C-FFI + Python binding surface for embedding, and non-network IPC modes (stdio/UDS) plus an optional OpenAI-compatible HTTP sidecar for local use.

Numbers: on real GPU hardware (T4/A6000/A100), currently measuring ~1.3–1.4x faster cold-start than llama.cpp on Qwen3 dense/MoE. Our "System1" cold-start phase benchmark (reflex system1) runs against unsloth/Qwen3-0.6B-GGUF's Qwen3-0.6B-Q4_K_M.gguf specifically, so it's easy to pull the exact same quant and reproduce the numbers yourself. Every benchmark run in the repo's history is against real hardware, not simulated — including a couple of regressions we found and fixed along the way, disclosed rather than buried.

What it's not: no batching, no multi-tenant serving, no internal HTTP server in the core engine — those are explicitly out of scope; this is a single-process engine meant to sit behind an orchestrator, not replace one.

Repo: https://github.com/lateos-ai/reflex

Would love feedback from anyone running Qwen3/Qwen3.5 in serverless/cold-start-sensitive setups — especially bug reports against real checkpoints, since that's the whole verification methodology this project leans on.

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