Instructions to use schneewolflabs/Wichtelchen-Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schneewolflabs/Wichtelchen-Qwen3.5-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="schneewolflabs/Wichtelchen-Qwen3.5-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("schneewolflabs/Wichtelchen-Qwen3.5-9B")
model = AutoModelForMultimodalLM.from_pretrained("schneewolflabs/Wichtelchen-Qwen3.5-9B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use schneewolflabs/Wichtelchen-Qwen3.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schneewolflabs/Wichtelchen-Qwen3.5-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "schneewolflabs/Wichtelchen-Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/schneewolflabs/Wichtelchen-Qwen3.5-9B

SGLang

How to use schneewolflabs/Wichtelchen-Qwen3.5-9B 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 "schneewolflabs/Wichtelchen-Qwen3.5-9B" \
    --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": "schneewolflabs/Wichtelchen-Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "schneewolflabs/Wichtelchen-Qwen3.5-9B" \
        --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": "schneewolflabs/Wichtelchen-Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use schneewolflabs/Wichtelchen-Qwen3.5-9B with Docker Model Runner:
```
docker model run hf.co/schneewolflabs/Wichtelchen-Qwen3.5-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Wichtelchen-Qwen3.5-9B

Wichtelchen: the little Wichtel — same house-imp, smaller boots.

The Wichtel-Qwen3.6-27B recipe rebuilt on Qwen3.5-9B: a local operator model that delegates engineering work to a code agent 10/10 — parity with the 27B — and writes Hemlock at 56.1% on hembench, most of the 27B's 67.6% at a third the parameters. Every adapter was retrained on the 9B itself — LoRAs do not transfer across pretrains as behaviour.

Lineage

Qwen/Qwen3.5-9B-Base
  └─ huihui-ai/Huihui-Qwen3.5-9B-abliterated
      └─ nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES     (+ Grimoire SFT/ORPO, 3-way TIES)
          └─ + Schierling  (Hemlock SFT, Hemlock-SFT-combined)  = Hemlock-Qwen3.5-9B
              └─ + Bubba       (GreatFirewall ORPO — states facts it otherwise sanitises)
                  └─ + delegation  (hands engineering work to a code agent)
                      └─ + egirl       (delegation + Hemlock tool use, ORPO)   ← Wichtelchen

Results — the full ladder, every rung benched

	baseline	+Schierling	+Bubba	+delegation	Wichtelchen
hembench (weighted)	28.5%	57.1%	49.4%	50.6%	56.1%
egirl 47-case	41/47	37	36	36	37/47
— delegate axis	6/10	7	7	9	10/10
censorship (best-of-5)	28.8/29	29.0	28.8	28.6	29.0/29
ARC (deterministic)	61.54	62.54	62.54	62.54	61.87
wiki perplexity	6.656	6.782	6.739	6.717	6.720

The Bubba rung cost 7.7 hembench points at rung 2; the egirl rung's Hemlock preference pairs recovered nearly all of it (49.4 → 56.1) — the same interaction the 27B showed. General capability never moved: ARC stayed in a one-point band across all four rungs and final perplexity is within 1% of baseline.

Limitations

Call formatting wobble (9B-specific). On 3 of 47 bench cases the model emits a malformed tool call — arguments as a bare string, or an argument hoisted to the top level. The 27B never did this. A lenient parser (or one repair round of preference training) covers it.
Restraint is the weak behavioural axis (7/10): it sometimes reaches for git_status or execute_command on prompts that want a direct answer. Delegation itself never misfires.
stdlib is the weak Hemlock axis, as in every model of this family: exact function names are memorised API knowledge. Check calls against the docs.
Single-turn tool use, and delegation is bound to egirl's tool schema.

MTP head

The 15 mtp.* tensors are restored after every PEFT merge (merge_and_unload() silently drops them and current llama.cpp then refuses the model). 775 tensors — 426 language, 333 vision, 15 MTP, 1 lm_head — verified at every rung.

llama-server -m Wichtelchen-Q8_0.gguf -ngl 99 -c 8192 --jinja -fa on -np 1 \
    --spec-type draft-mtp --spec-draft-n-max 4

schneewolflabs/Wichtel-Qwen3.6-27B — the big sibling
hemlang/Hemlock-Qwen3.5-9B — this model before the behaviour rungs
hemlang/Schierling-Qwen3.5-9B-LoRA — the Hemlock adapter
egirl — the agent this serves

Downloads last month: 19

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for schneewolflabs/Wichtelchen-Qwen3.5-9B

Base model

hemlang/Hemlock-Qwen3.5-9B

Finetuned

(1)

this model

Finetunes

1 model

Quantizations

4 models

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Wichtelchen-Qwen3.5-9B