Instructions to use Satori-reasoning/Satori-7B-Round2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Satori-reasoning/Satori-7B-Round2 with Transformers:

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

pipe = pipeline("text-generation", model="Satori-reasoning/Satori-7B-Round2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Satori-reasoning/Satori-7B-Round2")
model = AutoModelForCausalLM.from_pretrained("Satori-reasoning/Satori-7B-Round2", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Satori-reasoning/Satori-7B-Round2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Satori-reasoning/Satori-7B-Round2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Satori-reasoning/Satori-7B-Round2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Satori-reasoning/Satori-7B-Round2

SGLang

How to use Satori-reasoning/Satori-7B-Round2 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 "Satori-reasoning/Satori-7B-Round2" \
    --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": "Satori-reasoning/Satori-7B-Round2",
		"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 "Satori-reasoning/Satori-7B-Round2" \
        --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": "Satori-reasoning/Satori-7B-Round2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Satori-reasoning/Satori-7B-Round2 with Docker Model Runner:
```
docker model run hf.co/Satori-reasoning/Satori-7B-Round2
```

chaoscodes commited on May 31, 2025

Commit

8e848d6

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@@ -9,16 +9,7 @@ base_model:
 - Qwen/Qwen2.5-Math-7B
 ---
----
-license: apache-2.0
-library_name: transformers
-pipeline_tag: text-generation
-datasets:
-- Satori-reasoning/Satori_FT_data
-- Satori-reasoning/Satori_RL_data
-base_model:
-- Qwen/Qwen2.5-Math-7B
----
 **Satori-7B-Round2** is a 7B LLM trained on open-source model (Qwen-2.5-Math-7B) and open-source data (OpenMathInstruct-2 and NuminaMath). **Satori-7B-Round2** is capable of autoregressive search, i.e., self-reflection and self-exploration without external guidance.
 This is achieved through our proposed Chain-of-Action-Thought (COAT) reasoning and a two-stage post-training paradigm.

 - Qwen/Qwen2.5-Math-7B
 ---
 **Satori-7B-Round2** is a 7B LLM trained on open-source model (Qwen-2.5-Math-7B) and open-source data (OpenMathInstruct-2 and NuminaMath). **Satori-7B-Round2** is capable of autoregressive search, i.e., self-reflection and self-exploration without external guidance.
 This is achieved through our proposed Chain-of-Action-Thought (COAT) reasoning and a two-stage post-training paradigm.