Instructions to use MISA-core/linne-oak-1-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MISA-core/linne-oak-1-0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="MISA-core/linne-oak-1-0")
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("MISA-core/linne-oak-1-0")
model = AutoModelForMultimodalLM.from_pretrained("MISA-core/linne-oak-1-0", 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 MISA-core/linne-oak-1-0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MISA-core/linne-oak-1-0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MISA-core/linne-oak-1-0",
		"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/MISA-core/linne-oak-1-0

SGLang

How to use MISA-core/linne-oak-1-0 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 "MISA-core/linne-oak-1-0" \
    --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": "MISA-core/linne-oak-1-0",
		"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 "MISA-core/linne-oak-1-0" \
        --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": "MISA-core/linne-oak-1-0",
		"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 MISA-core/linne-oak-1-0 with Docker Model Runner:
```
docker model run hf.co/MISA-core/linne-oak-1-0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

linne-oak-1.0

linne-oak-1.0 is a 31B decision model. Give it a state (text, a document, JSON, a conversation or an agent trajectory) and a question with a set of options, and it returns one option together with a probability for every option, in a single forward pass.

It is the larger sibling of linne-fern-1.0: linne-fern is a 27B causal model that also chats, linne-oak is a dedicated decision model.

Highlights

Decision Index public score 63.94, measured with the official kit (edition 0.3, all 37 public benchmarks, 140,620 requests, complete).
Reads the whole prompt at once: the full-attention layers attend in both directions over the prompt (sliding-window layers keep their native behavior), so the decision sees the state, the question and every option together.
Strong across the board: knowledge, language understanding, retrieval and classification, tools and automation.
Agent-safe: a strong step-level guard for agents with a low false-block rate.
Fast: one forward pass per decision; median 63 ms per request on one H200.

linne-oak-1.0 is a decision-only model. Because its full-attention layers are bidirectional, it does not generate text: use it to pick options and read probabilities, not as a chat model. For chat, use the base model or linne-fern-1.0.

Decision Index (public part, edition 0.3)

Chance-corrected skill, higher is better. linne rows measured by us with the official kit; the other rows are the public-part scores published on the leaderboard.

Model	Overall	Knowledge	Language	Retrieval	Tools	Arts
linne-oak-1.0 (31B)	63.94	54.3	66.6	69.1	78.5	45.1
linne-fern-1.0 (27B)	58.92	44.9	62.8	62.4	79.0	41.5
Perplexity Decider v1.1 (27B), leaderboard	62.25
Quyet-1.0-Large (31B), leaderboard	60.79

Only the public part (20% of the full leaderboard score) can be measured outside the leaderboard.

Usage

The prompt is one user message (chat template, thinking off): the state (compact JSON when not a string), a blank line, Question: <instructions>, one line <label>: <name> - <description> per option, and Answer with the letter of one option only. The answer is the softmax over the next-token logits of the option letters (with the model's final logit soft-capping), read at the last prompt position, with the full-attention layers bidirectional.

linne_engine.py (with nc_hook.py) in this repository implements exactly this, as a Decision Index engine and as a library:

import sys
from huggingface_hub import snapshot_download

path = snapshot_download("MISA-core/linne-oak-1-0")
sys.path.insert(0, path)
from linne_engine import LinneEngine

model = LinneEngine(model=path, device="cuda:0", noncausal=True)
result, _ = model(
    "My Stripe integration keeps failing. Please help ASAP.",
    {"team": {"type": "choice", "instructions": "Which team should handle this request?",
              "criteria": {"billing": "Charges and refunds", "technical_support": "Integration errors",
                           "sales": "Questions about buying a product"}}},
)
print(result["answers"]["team"])  # chosen option and a probability for every option

Run the Decision Index public suite:

PYTHONPATH=<path> python -m decision_index pipeline --engine linne_engine:LinneEngine \
  --option model=<path> --option device=cuda:0 --option noncausal=1 --out runs/linne-oak-1-0

BF16 weights are about 62 GB and fit one 80 GB GPU.