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

Libraries

How to use hemlang/Hemlock-Qwen3.5-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="hemlang/Hemlock-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("hemlang/Hemlock-Qwen3.5-9B")
model = AutoModelForMultimodalLM.from_pretrained("hemlang/Hemlock-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 hemlang/Hemlock-Qwen3.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hemlang/Hemlock-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": "hemlang/Hemlock-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/hemlang/Hemlock-Qwen3.5-9B

SGLang

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

Hemlock-Qwen3.5-9B

A 9B model that writes Hemlock — the 9B counterpart of Hemlock-Qwen3.6-27B.

Schierling-Qwen3.5-9B-LoRA (SFT on hemlang/Hemlock-SFT-combined) merged at 1.0 into Huihui-Qwen3.5-9B-abliterated-TIES, with the 15 mtp.* tensors restored from the base after the merge — peft merge_and_unload() drops them, and current llama.cpp refuses to load the model without them. 775 tensors total (426 language, 333 vision, 15 MTP, 1 lm_head), verified against the base's tensor inventory.

Results — hembench 57.1%

Every generated program is executed by the real interpreter and its stdout compared against expected output (Q8_0, greedy):

level		score
L1	syntax	7/9
L2	stdlib	1/5
L3	algorithms	7/7
L4	systems	4/7
L5	translation	2/5
L6	debugging	2/5
overall	weighted	57.1%

For scale: the 27B TIES scored 10.1% before its Hemlock training and 65.0% after. This 9B lands at 57.1% — most of the 27B's Hemlock capability at a third the parameters.

No measurable general-capability cost from the adapter: ARC 61.5→62.5 (n=299), wiki perplexity +1.9%.

Limitations

stdlib is the weak axis (1/5), same as the 27B family: standard-library work needs exact function names and signatures — memorised API knowledge rather than transferable syntax. Check calls against the docs.
Tool-use restraint measured slightly below the pre-SFT base (8/10 vs 10/10 on a 47-case bench) — the known cost of direct code-generation SFT on an operator model. Preference training repairs this; it is the next rung of this model's ladder.

Serving

llama-server -m Hemlock-Qwen3.5-9B-Q8_0.gguf -ngl 99 -c 8192 --jinja -fa on

The MTP head supports --spec-type draft-mtp speculative decoding.

Downloads last month: 20

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for hemlang/Hemlock-Qwen3.5-9B

hemlang/Schierling-Qwen3.5-9B-LoRA

nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES

Merge model

this model

Adapters

2 models

Finetunes

1 model

Quantizations