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
```

Add library name and paper link

by nielsr HF Staff - opened 9 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-2

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)