Instructions to use horiuchinobuyuki/Qwick-3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use horiuchinobuyuki/Qwick-3.5-9B with Transformers:

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

pipe = pipeline("text-generation", model="horiuchinobuyuki/Qwick-3.5-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("horiuchinobuyuki/Qwick-3.5-9B")
model = AutoModelForMultimodalLM.from_pretrained("horiuchinobuyuki/Qwick-3.5-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use horiuchinobuyuki/Qwick-3.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "horiuchinobuyuki/Qwick-3.5-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "horiuchinobuyuki/Qwick-3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/horiuchinobuyuki/Qwick-3.5-9B

SGLang

How to use horiuchinobuyuki/Qwick-3.5-9B 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 "horiuchinobuyuki/Qwick-3.5-9B" \
    --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": "horiuchinobuyuki/Qwick-3.5-9B",
		"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 "horiuchinobuyuki/Qwick-3.5-9B" \
        --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": "horiuchinobuyuki/Qwick-3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use horiuchinobuyuki/Qwick-3.5-9B with Docker Model Runner:
```
docker model run hf.co/horiuchinobuyuki/Qwick-3.5-9B
```

Qwick-3.5-9B / TRAINING.md

horiuchinobuyuki

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Training lineage

A single rank-8 attention adapter was trained over Qwen/Qwen3.5-9B@c202236235762e1c871ad0ccb60c8ee5ba337b9a. After checkpoint selection and evaluation, it was merged with PEFT safe_merge and serialized as this BF16 checkpoint.

1. Difficulty-aware full-trace SFT

1,324 selected examples from 1,536 source problems; 212 unsolved problems excluded.
Languages: 630 English, 694 Japanese.
Graders/tasks: 416 exact, 377 numeric, 531 choice.
Teacher roles: 397 anchor, 662 exploratory, 265 narrow.
207 productive long traces retained without semantic rewriting.
One epoch, 83 optimizer steps, effective batch size 16, two workers.
Learning rate 5e-5, maximum sequence length 8,192, prompt loss masked.
LoRA rank 8, alpha 8, dropout 0.05, 104 attention matrices in the text backbone, 7,471,104 trainable parameters.
Selected-data manifest SHA-256: 623fa2f278c83d9c4a2423578fa4330d3d0aa252761663373a602cfa5a4ad150.

2. Composition-native Step-DPO

Continued from the SFT LoRA factors.
1,024 preference pairs: 768 strict and 256 ties.
One epoch, 64 optimizer steps, learning rate 5e-6, beta 0.05.
Maximum sequence length 8,704; anchor weight 0.05 every two local pairs.
Preference-data SHA-256: aa336a6dd6484d2a56b8c4570e32311965aa88b0f43ddc19a79e31e383beb3b1.

3. Checkpoint search

Seeded antithetic random search and later local searches were used to propose adapter checkpoints. Short 256/1,024-token runs served as screens; promoted candidates were evaluated with complete saved traces. Final selection used matched MMLU-Pro and GPQA-Diamond comparisons.

The search design draws on the observation of Gan and Isola, Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights (arXiv:2603.12228), that strong task-specific variants are densely distributed around pretrained weights. We adapt that idea to propose and screen low-rank adapter checkpoints; we do not apply the paper's ensemble-by-majority-vote procedure; a single searched checkpoint is selected and merged.

4. Final local NES update

The selected G7 adapter came from a six-direction, antithetic, block-separable local NES update at alpha 8.

Master seed: 2026081502
Selected direction/sign: 4 / positive
Effective step: 0.009458353243739753
Center tensor SHA-256: 699a961974399f391c2e40ddcd0a0bd3f8efe8de6e35fb63dbf05952ded355ca
Optimizer-state SHA-256: 70665decb8cbb3b64dfb345a170901b34a0e04a91efee4df4aecd86e5d205015
Final adapter tensor SHA-256: 2a25037cd9c7bbdacdbefba3b09424e74f7759cb024acfe56f3a412db0f92e0e

The reserved holdout was evaluated once after this checkpoint had been selected. Its separately frozen temperature-zero policy was an internal release gate, not the public benchmark measurement profile; the gate passed and no post-result retuning or rerun occurred. Detailed lineage and merge identities are in provenance.json.