Instructions to use CodeXomics/CodeXomics-ToolAgent-4B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeXomics/CodeXomics-ToolAgent-4B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="CodeXomics/CodeXomics-ToolAgent-4B-v1")
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("CodeXomics/CodeXomics-ToolAgent-4B-v1")
model = AutoModelForMultimodalLM.from_pretrained("CodeXomics/CodeXomics-ToolAgent-4B-v1", 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 CodeXomics/CodeXomics-ToolAgent-4B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeXomics/CodeXomics-ToolAgent-4B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeXomics/CodeXomics-ToolAgent-4B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CodeXomics/CodeXomics-ToolAgent-4B-v1

SGLang

How to use CodeXomics/CodeXomics-ToolAgent-4B-v1 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 "CodeXomics/CodeXomics-ToolAgent-4B-v1" \
    --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": "CodeXomics/CodeXomics-ToolAgent-4B-v1",
		"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 "CodeXomics/CodeXomics-ToolAgent-4B-v1" \
        --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": "CodeXomics/CodeXomics-ToolAgent-4B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CodeXomics/CodeXomics-ToolAgent-4B-v1 with Docker Model Runner:
```
docker model run hf.co/CodeXomics/CodeXomics-ToolAgent-4B-v1
```

CodeXomics-ToolAgent-4B-v1

File size: 3,024 Bytes

---
license: apache-2.0
language:
  - en
library_name: transformers
tags:
  - genomics
  - tool-calling
  - function-calling
  - qwen3.5
  - qwen
pipeline_tag: text-generation
base_model: Qwen/Qwen3.5-4B
pretty_name: CodeXomics-ToolAgent-4B-v1
---

# CodeXomics-ToolAgent-4B-v1

**CodeXomics-ToolAgent-4B-v1** (internally `qwen3.5:4b-codexomics-tools-v5`) is a 4.2B-parameter
tool-calling model fine-tuned from `Qwen/Qwen3.5-4B` for the CodeXomics genomics workbench. It
performs native function calling against the CodeXomics tool registry (file loading, navigation,
sequence analysis, annotation, track control, export, BLAST, primer design, database/protein
retrieval, task management, and UI control).

CodeXomics is an AI-native genome browser: a cross-platform desktop application in which
conversational AI agents drive genome visualization and run real biological analyses, with built-in
tool execution, a dynamic tool registry, plugin development, and Model Context Protocol (MCP)
integration. Source code and documentation:
[github.com/Scilence2022/CodeXomics](https://github.com/Scilence2022/CodeXomics) and
[scilence2022.github.io/CodeXomics](https://scilence2022.github.io/CodeXomics/).

## Fine-tuning

- Method: QLoRA (rank 16, scale 32.0, dropout 0.05, 4 layers) with MLX-LM 0.31.2 / MLX 0.32.0
- Trainable parameters: 4.058M (0.096%)
- Optimizer: AdamW, learning rate 1.0e-5, effective batch size 4, 200 iterations
- Maximum sequence length: 3,072 tokens; prompt masking enabled
- Hardware: Apple M3 Max; peak memory 191 GB (including swap)
- Checkpoint selection: iteration 75 (validation loss 0.020); test loss 0.074 (perplexity 1.077)
- Training data: CodeXomics-ToolCalling-v1 (373/123/30 train/validation/test examples)

## Evaluation

On the CodeXomics Benchmark (172 automatic tests: 143 single-operation, 29 multi-step), evaluated
in the real application loop with task-completion scoring plus execution evidence:

| Suite | Passed / Total |
| --- | --- |
| Simple | 143/143 |
| Complex | 29/29 |
| **Total** | **172/172 (100%)** |

100% was achieved in multiple independent complete sessions (simple: 2026-08-04 and 2026-08-07;
complex: 2026-08-04 and 2026-08-10). Inference settings: temperature 0, thinking enabled.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("CodeXomics-ToolAgent-4B-v1")
tokenizer = AutoTokenizer.from_pretrained("CodeXomics-ToolAgent-4B-v1")
```

For deployment in CodeXomics, the model is served through Ollama as `qwen3.5:4b-codexomics-tools-v5`
(Q4_K_M, 2.7 GB) with native tool calling and thinking enabled.

## Limitations

- The model is specialized for CodeXomics genomic workflows; generalization to other tool-calling
  domains was not evaluated.

## Citation

```bibtex
@software{codexomics-toolagent-v1,
  title = {CodeXomics-ToolAgent-4B-v1},
  author = {Song, Lifu},
  year = {2026},
  license = {Apache-2.0},
  publisher = {Hugging Face},
  base_model = {Qwen/Qwen3.5-4B}
}
```