Instructions to use schneewolflabs/Wichtelchen-Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# 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=40) 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
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 —
argumentsas 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_statusorexecute_commandon 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
Related
schneewolflabs/Wichtel-Qwen3.6-27B— the big siblinghemlang/Hemlock-Qwen3.5-9B— this model before the behaviour rungshemlang/Schierling-Qwen3.5-9B-LoRA— the Hemlock adapter- egirl — the agent this serves
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Model tree for schneewolflabs/Wichtelchen-Qwen3.5-9B
Base model
hemlang/Hemlock-Qwen3.5-9B