Instructions to use omicverse/OmicAI-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omicverse/OmicAI-4B with Transformers:

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

pipe = pipeline("text-generation", model="omicverse/OmicAI-4B")
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("omicverse/OmicAI-4B")
model = AutoModelForMultimodalLM.from_pretrained("omicverse/OmicAI-4B", 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 omicverse/OmicAI-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omicverse/OmicAI-4B"
# 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-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/omicverse/OmicAI-4B

SGLang

How to use omicverse/OmicAI-4B 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-4B" \
    --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-4B",
		"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-4B" \
        --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-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use omicverse/OmicAI-4B with Docker Model Runner:
```
docker model run hf.co/omicverse/OmicAI-4B
```

OmicAI-4B / README.md

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metadata

license: apache-2.0
base_model: Qwen/Qwen3.5-4B
library_name: transformers
pipeline_tag: text-generation
language:
  - en
tags:
  - omics
  - bioinformatics
  - single-cell
  - spatial-transcriptomics
  - omicverse
  - agent
  - tool-calling
  - scientific-coding

OmicAI-4B

OmicAI is a family of language models optimized for omics data analysis and tool interaction — writing and running real bioinformatics pipelines over the omicverse toolkit. This card is the 4B member; 9B and 27B variants are in progress.

OmicAI is built on Qwen3.5 and fine-tuned with ms-swift using LoRA adapters merged into the final weights.

The OmicAI family

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

Training Data

The training trajectories are produced by OmicOS and OmicVerse through a paper-reproduction process over the last three years of publications: the systems retrieve recent omics papers, then attempt to reproduce their analyses end-to-end from the raw data. Each attempt yields a multi-turn agent trajectory — literature/context retrieval, code generation, omicverse tool calls, and recovery from execution feedback.

A trajectory is kept only when an automatic judge confirms the deliverable is legitimate (not a placeholder or answer-leak) and a scoreboard confirms the reproduced result matches or surpasses the paper's reported (SOTA) numbers. Runs that were rejected, contaminated, or degenerate are discarded. This yields a high-signal corpus grounded in real, verifiable scientific reproduction.

Key Details


Base model	Qwen/Qwen3.5-4B
Method	ms-swift 4.5.3 · LoRA (`target_modules=all-linear`, rank 16, α 32) → merged
Precision	bfloat16 (merged weights)
Chat template	`qwen3_5` — inference template must match training
Training data	agent trajectories from OmicOS/OmicVerse reproducing 3 years of omics papers; judge + scoreboard (MATCH / SURPASS-SOTA) validated
Epochs / seq len	3 epochs · max length 16384 · packing + padding-free

Evaluation

OmicAI is evaluated on two axes (results reported separately as the suite lands):

Domain / tool-use (the training target): an omics-analysis benchmark with a deterministic execution verifier, plus function-calling (BFCL) and multi-turn agentic (τ-bench) suites.
General-capability regression (catastrophic forgetting check): MMLU, GSM8K, IFEval — to ensure domain gains do not come at the cost of general ability.

Benchmark numbers are omitted here rather than estimated; they will be added once the evaluation harness is finalized.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("omicverse/OmicAI-4B")
model = AutoModelForCausalLM.from_pretrained(
    "omicverse/OmicAI-4B", torch_dtype="bfloat16", device_map="auto")

msgs = [
    {"role": "system", "content": "You are an omics bioinformatics assistant."},
    {"role": "user", "content": "Load pbmc3k, run QC, normalize, and cluster with omicverse."},
]
prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=512)
print(tok.decode(out[0], skip_special_tokens=True))

Also loadable with vLLM (use the qwen3_5 chat template).

Intended Use & Limitations

Intended: research assistant for single-cell / bulk / spatial omics analysis and omicverse pipeline generation.
Limitations: a research model — outputs (especially generated analysis code) should be executed and verified before use; it does not replace expert judgement, and may hallucinate APIs outside the omicverse/scanpy surface it was trained on.

Ecosystem

Part of the omicOS / omicAI effort and the omicverse ecosystem. OmicAI is also the student model of omicOS-RSI, an infrastructure-level recursive self-improvement pipeline in which the model, the omicverse toolkit, and the task benchmark co-evolve under an execution-grounded verifier.

Citation

If omicverse is useful in your work, please cite the omicverse paper. A dedicated OmicAI report is forthcoming.