---
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](https://github.com/omicverse/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](https://huggingface.co/omicverse/OmicAI-0.8B) | Qwen3.5-0.8B | Open — Apache-2.0 |
| **OmicAI-4B** *(this model)* | Qwen3.5-4B | Open — Apache-2.0 |
| [OmicAI-9B](https://huggingface.co/omicverse/OmicAI-9B) | MiMo-V2.6-Distill-Qwen-9B | Open — Apache-2.0 |
| [OmicAI-27B](https://huggingface.co/omicverse/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

```python
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.