Instructions to use hemlang/Hemlock-Qwen3.6-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hemlang/Hemlock-Qwen3.6-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="hemlang/Hemlock-Qwen3.6-27B")
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-Qwen3.6-27B")
model = AutoModelForMultimodalLM.from_pretrained("hemlang/Hemlock-Qwen3.6-27B", 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-Qwen3.6-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hemlang/Hemlock-Qwen3.6-27B"
# 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-Qwen3.6-27B",
		"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-Qwen3.6-27B

SGLang

How to use hemlang/Hemlock-Qwen3.6-27B 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-Qwen3.6-27B" \
    --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-Qwen3.6-27B",
		"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-Qwen3.6-27B" \
        --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-Qwen3.6-27B",
		"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-Qwen3.6-27B with Docker Model Runner:
```
docker model run hf.co/hemlang/Hemlock-Qwen3.6-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Hemlock-Qwen3.6-27B

Vorarbeiter with the Schierling adapter merged in — an operator model that also writes Hemlock.

The combination exists because no code agent understands Hemlock. Vorarbeiter's delegation policy hands engineering work to a coding agent, which is right for every language that agent knows and wrong for this one: if the operator does not write Hemlock itself, nothing does.

What each half contributes

Measured on the TIES base, the Schierling adapter takes hembench from 10.1% to 65.0% (4/38 → 26/38 tasks), where every generated program is executed by the real interpreter and its stdout compared against expected output:

level		TIES	+ Schierling
L1	syntax	1/9	8/9
L2	stdlib	0/5	1/5
L3	algorithms	0/7	5/7
L4	systems	0/7	5/7
L5	translation	0/5	3/5
L6	debugging	3/5	4/5
overall	weighted	10.1%	65.0%

22 tasks fixed, 0 broken. Generated code that fails to execute drops from 30 tasks to 8.

Vorarbeiter contributes the operator behaviour: delegation of non-Hemlock engineering work to a code agent, and factual answers on topics the base sanitises. See its card for those numbers.

Benchmarks

Measured on the merged weights. Tool use is the 47-case egirl bench at temperature 0; Hemlock is hembench, where every generated program is executed by the real interpreter and its stdout compared against expected output.

	TIES	Vorarbeiter	Hemlock	Wichtel
tool use	35/47	41/47	—	47/47
delegation	0/10	7/10	—	10/10
hembench	10.1%	—	—	67.6%

The concern flagged when this model was uploaded — that 5,562 rows of tool-free code generation might teach the operator to write code instead of delegating — did not materialise. Adding the Hemlock adapter to a base with 0/10 delegation left it at 1/10 and lifted total tool use 35/47 → 37/47; nothing was lost. Adding the egirl tool-use adapter on top then took delegation to 10/10, which is Wichtel.

If you want the operator model, use Wichtel — same lineage plus the tool-use adapter, and it is the configuration that was benchmarked end to end. This model is the useful intermediate: the Hemlock capability without the egirl-specific tool schema baked in, which is what you want if your agent uses different tools.

MTP head

peft merge_and_unload() silently drops the 15 mtp.* tensors and current llama.cpp then refuses to load the model. They are grafted back from the pre-merge base and verified bit-identical. This architecture has 1,199 tensors (850 language, 333 vision, 15 MTP, 1 lm_head).

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

Limitations

stdlib is the weak spot (1/5 on the adapter's own evaluation). Check API calls against docs.
Merge behaviour unbenchmarked at upload time — see above.
38 hembench tasks is a small sample; per-level scores carry wide error bars.
Everything Vorarbeiter carries: single-turn tool use, delegation bound to egirl's tool schema, and a persistent Chinese-language gap on the censorship axis.

Lineage

Qwen/Qwen3.6-27B
  └─ huihui-ai/Huihui-Qwen3.6-27B-abliterated
      └─ nbeerbower/Qwen3.6-27B-TIES
          └─ + Bubba-3ep (GreatFirewall ORPO)
              └─ + delegation LoRA          = Vorarbeiter
                  └─ + Schierling (Hemlock SFT)   ← this model

nbeerbower/Schierling-Qwen3.6-27B-LoRA — the adapter, with full training details
nbeerbower/Vorarbeiter-Qwen3.6-27B — the base
hemlock — the language and its benchmark

Downloads last month: -

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for hemlang/Hemlock-Qwen3.6-27B

nbeerbower/Schierling-Qwen3.6-27B-LoRA

nbeerbower/Vorarbeiter-Qwen3.6-27B

Merge model

this model

Merges

1 model

Quantizations