Instructions to use MichaelAnthony/qwen-3.5-0.8b-Hedgehog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MichaelAnthony/qwen-3.5-0.8b-Hedgehog with Transformers:

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

pipe = pipeline("text-generation", model="MichaelAnthony/qwen-3.5-0.8b-Hedgehog")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MichaelAnthony/qwen-3.5-0.8b-Hedgehog")
model = AutoModelForCausalLM.from_pretrained("MichaelAnthony/qwen-3.5-0.8b-Hedgehog", 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 MichaelAnthony/qwen-3.5-0.8b-Hedgehog with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MichaelAnthony/qwen-3.5-0.8b-Hedgehog"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MichaelAnthony/qwen-3.5-0.8b-Hedgehog",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MichaelAnthony/qwen-3.5-0.8b-Hedgehog

SGLang

How to use MichaelAnthony/qwen-3.5-0.8b-Hedgehog 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 "MichaelAnthony/qwen-3.5-0.8b-Hedgehog" \
    --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": "MichaelAnthony/qwen-3.5-0.8b-Hedgehog",
		"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 "MichaelAnthony/qwen-3.5-0.8b-Hedgehog" \
        --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": "MichaelAnthony/qwen-3.5-0.8b-Hedgehog",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MichaelAnthony/qwen-3.5-0.8b-Hedgehog with Docker Model Runner:
```
docker model run hf.co/MichaelAnthony/qwen-3.5-0.8b-Hedgehog
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

qwen-3.5-0.8b-Hedgehog

qwen-3.5-0.8b-Hedgehog is a 752M-parameter Qwen 3.5 text model tuned for SimpleRAG document extraction and bounded thinking. This repository contains the merged r5 FP16 Transformers checkpoint; no LoRA adapter is required.

Recommended extraction mode

Use temperature 0, disable thinking, provide the retrieved document evidence, and specify exact JSON leaf paths and scalar types. Thinking-enabled chat and thinking-disabled extraction should be evaluated separately.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MichaelAnthony/qwen-3.5-0.8b-Hedgehog"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": "Use only the supplied document. Return exact JSON only.",
    },
    {
        "role": "user",
        "content": (
            "Document: Invoice INV-8042 is due 2026-09-15. Total: $1,284.50. "
            "Vendor: Northwind Tools. Extract invoice_number, due_date, total, "
            "and vendor."
        ),
    },
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)

r5 release evaluation

Evaluation	Result
BenchLocal DataExtract-15	71
BenchLocal Complex Documents	88
Q8 loop-control detected loops	0 / 24
Q8 loop-control length caps	0 / 24
Q8 loop-control final answers	23 / 24
Original validation JSON valid	99.375%
Original validation required fields	67.248%
Complex/implicit validation JSON valid	100%
Complex/implicit exact records	82.377%

The prior r3 GGUF scored 72 overall and 94 in Complex Documents, so r5 trades one overall BenchLocal point and six Complex Documents points for much better loop termination. It is intentionally published as a starter model, not as a 95%-accurate general extractor.

Known limitations

Multi-entity extraction and synthetic complex invoices are the primary known weaknesses.
One loop-control planning case ended after reasoning without a final answer.
Small models are sensitive to prompt wording and schema ambiguity.
The GGUF and MLX packages are published in separate repositories and must be evaluated independently after conversion or quantization.

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Safetensors

Model size

0.8B params

Tensor type

BF16

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