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

Libraries

How to use OverwhelmingFit/SOPO-4B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OverwhelmingFit/SOPO-4B")
model = AutoModelForCausalLM.from_pretrained("OverwhelmingFit/SOPO-4B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OverwhelmingFit/SOPO-4B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OverwhelmingFit/SOPO-4B

SGLang

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

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

nielsr HF Staff commited on 9 days ago

Commit

f683011

verified ·

1 Parent(s): 31c2bb9

Add library name and paper link

Browse files

This PR improves the model card by:
- Adding `library_name: transformers` to the metadata, since the model is a `Qwen3ForCausalLM` and is fully compatible with the Transformers library.
- Adding a direct link to the paper page in the content, making it easier to find the associated research.

Please review and merge if this looks good.

Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -6,11 +6,12 @@ tags:
 - qwen3
 - hallucination-detection
 - sopo
 ---
 # SOPO-4B
-Official release of the 4B [SOPO (Socratic Policy Optimization)](https://github.com/patsnap/SOPO) model for span-level hallucination detection. See the [evaluation instructions](https://github.com/patsnap/SOPO#evaluation) for the RAGTruth test set.
 ## RoPE configuration and compatibility
@@ -22,4 +23,4 @@ The updated `config.json` sets **both** `rope_theta` and `rope_parameters.rope_t
 - [SOPO source code and evaluation script](https://github.com/patsnap/SOPO)
 - [RL training dataset](https://huggingface.co/datasets/OverwhelmingFit/SOPO-RL-RAGTruth10k)
-- [Reasoning-augmented SFT training dataset](https://huggingface.co/datasets/OverwhelmingFit/SOPO-SFT-RAGTruth5k-Reasoning)

 - qwen3
 - hallucination-detection
 - sopo
+library_name: transformers
 ---
 # SOPO-4B
+Official release of the 4B [SOPO (Socratic Policy Optimization)](https://github.com/patsnap/SOPO) model for span-level hallucination detection. This model is presented in the paper [SOPO: Socratic Guided Policy Optimization for Span-Level Hallucination Detection](https://huggingface.co/papers/2610.32212). See the [evaluation instructions](https://github.com/patsnap/SOPO#evaluation) for the RAGTruth test set.
 ## RoPE configuration and compatibility
 - [SOPO source code and evaluation script](https://github.com/patsnap/SOPO)
 - [RL training dataset](https://huggingface.co/datasets/OverwhelmingFit/SOPO-RL-RAGTruth10k)
+- [Reasoning-augmented SFT training dataset](https://huggingface.co/datasets/OverwhelmingFit/SOPO-SFT-RAGTruth5k-Reasoning)