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

Libraries

How to use FutureMa/Eva-4B with Transformers:

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

pipe = pipeline("text-generation", model="FutureMa/Eva-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("FutureMa/Eva-4B")
model = AutoModelForMultimodalLM.from_pretrained("FutureMa/Eva-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FutureMa/Eva-4B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FutureMa/Eva-4B

SGLang

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

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

FutureMa commited on Jan 15

Commit

83303c9

verified ·

1 Parent(s): 2a523d7

Update README.md

Browse files

update paper info

Files changed (1) hide show

README.md +10 -3

README.md CHANGED Viewed

@@ -214,12 +214,19 @@ Eva-4B is a research artifact and **not financial advice**. Outputs should be us
 If you use this model, please cite the accompanying paper:
 ```bibtex
-@article{ma_evasionbench,
-  title={EvasionBench: Detecting Evasive Answers in Financial Q\&A via Multi-Model Consensus and LLM-as-Judge},
-  author={Ma, Shijian}
 }
 ```
 ## Author
 - Shijian Ma (mas8069@foxmail.com)

 If you use this model, please cite the accompanying paper:
 ```bibtex
+@misc{ma2026evasionbenchdetectingevasiveanswers,
+      title={EvasionBench: Detecting Evasive Answers in Financial Q&A via Multi-Model Consensus and LLM-as-Judge},
+      author={Shijian Ma and Yan Lin and Yi Yang},
+      year={2026},
+      eprint={2601.09142},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2601.09142},
 }
 ```
+Paper: [https://arxiv.org/abs/2601.09142](https://arxiv.org/abs/2601.09142)
 ## Author
 - Shijian Ma (mas8069@foxmail.com)