Instructions to use omicverse/OmicAI-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omicverse/OmicAI-9B with Transformers:

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

pipe = pipeline("text-generation", model="omicverse/OmicAI-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("omicverse/OmicAI-9B")
model = AutoModelForMultimodalLM.from_pretrained("omicverse/OmicAI-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 omicverse/OmicAI-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omicverse/OmicAI-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": "omicverse/OmicAI-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/omicverse/OmicAI-9B

SGLang

How to use omicverse/OmicAI-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 "omicverse/OmicAI-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": "omicverse/OmicAI-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "omicverse/OmicAI-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": "omicverse/OmicAI-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use omicverse/OmicAI-9B with Docker Model Runner:
```
docker model run hf.co/omicverse/OmicAI-9B
```

OmicAI-9B / README.md

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metadata

license: apache-2.0
base_model: XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B
library_name: transformers
pipeline_tag: text-generation
language:
  - en
tags:
  - omics
  - bioinformatics
  - single-cell
  - spatial-transcriptomics
  - omicverse
  - agent
  - tool-calling
  - scientific-coding

OmicAI-9B

The 9B member of OmicAI, a family optimized for omics data analysis and tool interaction over omicverse. Fine-tuned with ms-swift LoRA (all-linear, r16, α32) merged on a Qwen3.5-9B-architecture base (MiMo-V2.6-Distill-Qwen-9B).

The OmicAI family

Model	Base	Access
OmicAI-0.8B	Qwen3.5-0.8B	Open — Apache-2.0
OmicAI-4B	Qwen3.5-4B	Open — Apache-2.0
OmicAI-9B (this model)	MiMo-V2.6-Distill-Qwen-9B	Open — Apache-2.0
OmicAI-27B	Qwen3.8-27B	By application

Training Data

Trajectories produced by OmicOS and OmicVerse through a paper-reproduction process over the last three years of publications (retrieve recent omics papers → reproduce their analyses end-to-end → multi-turn agent trajectories of retrieval, code, omicverse tool calls, execution-feedback recovery). Kept only when a judge confirms legitimacy and a scoreboard confirms the result matches or surpasses the paper's reported (SOTA) numbers.

Key Details


Base model	XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B (Qwen3.5-9B architecture)
Method	ms-swift LoRA (all-linear, r16, α32) → merged
Chat template	`qwen3_5` — inference template must match training
Precision	bfloat16 (4 shards)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("omicverse/OmicAI-9B")
model = AutoModelForCausalLM.from_pretrained("omicverse/OmicAI-9B", torch_dtype="bfloat16", device_map="auto")

Ecosystem

Part of omicOS / omicAI. Siblings: OmicAI-4B · OmicAI-27B · OmicAI-0.8B.