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

Libraries

How to use IFM/guru-7B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IFM/guru-7B")
model = AutoModelForCausalLM.from_pretrained("IFM/guru-7B", 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 IFM/guru-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/IFM/guru-7B

SGLang

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

Docker Model Runner
How to use IFM/guru-7B with Docker Model Runner:
```
docker model run hf.co/IFM/guru-7B
```

nielsr HF Staff commited on Jun 21, 2025

Commit

3c092d6

verified ·

1 Parent(s): 8783142

Add Github repository link and ensure Transformers library is recognized

Browse files

This PR adds the link to the Github repository and ensures the proper library is recognized for your model.

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
 library_name: transformers
-pipeline_tag: text-generation
 license: cc-by-nc-4.0
 ---
 This repository contains the Guru-7B (base Qwen2.5-7B) model presented in [Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective](https://huggingface.co/papers/2506.14965).
@@ -29,8 +29,6 @@ The leaderboard is evaluated with our evaluation [code](https://github.com/LLM36
 |                | LiveBench                     |       18.57 |                   19.76  |       12.64 |          15.20 |        34.30 |         28.78 |            28.33 |
 |                | **Average Score**              | **43.29**   | **33.76**               | **35.42**   | **33.97**      | **54.24**    | **47.53**     | **46.25**        |
 Example usage:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -46,3 +44,5 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 Please refer to the [paper](https://arxiv.org/abs/2506.14965) for more details.

 ---
 library_name: transformers
 license: cc-by-nc-4.0
+pipeline_tag: text-generation
 ---
 This repository contains the Guru-7B (base Qwen2.5-7B) model presented in [Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective](https://huggingface.co/papers/2506.14965).
 |                | LiveBench                     |       18.57 |                   19.76  |       12.64 |          15.20 |        34.30 |         28.78 |            28.33 |
 |                | **Average Score**              | **43.29**   | **33.76**               | **35.42**   | **33.97**      | **54.24**    | **47.53**     | **46.25**        |
 Example usage:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 ```
 Please refer to the [paper](https://arxiv.org/abs/2506.14965) for more details.
+Github repository: https://github.com/LLM360/Reasoning360