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
```

zhoujun commited on Jun 19, 2025

Commit

7e8a9a4

verified ·

1 Parent(s): a28a79c

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -18,7 +18,7 @@ The leaderboard is evaluated with our evaluation [code](https://github.com/LLM36
 | **Science**    | GPQA-diamond (avg@4)           |       40.78 |                   38.64  |       37.63 |          35.98 |        50.63 |         55.67 |            46.46 |
 |                | SuperGPQA                      |       31.80 |                   30.64  |       29.75 |          27.29 |        43.60 |         46.05 |            37.73 |
 | **Logic**      | ARC-AGI (avg@4)                |        3.31 |                    0.75  |        0.00 |           0.50 |         7.63 |          2.31 |             5.25 |
-|                | Zebra Puzzle♡ (avg@4)          |       39.40 |                    0.07  |        1.00 |           0.62 |        45.21 |          0.54 |             1.16 |
 | **Simulation** | CodeI/O (avg@4)                |       15.63 |                    7.13  |        5.13 |           6.63 |        12.63 |          3.75 |             9.75 |
 |                | CruxEval-I                     |       61.72 |                   63.63  |       69.38 |          56.25 |        80.63 |         71.13 |            72.63 |
 |                | CruxEval-O                     |       71.28 |                   56.50  |       65.88 |          58.31 |        88.75 |         82.38 |            67.75 |
@@ -26,7 +26,7 @@ The leaderboard is evaluated with our evaluation [code](https://github.com/LLM36
 |                | HiTab                          |       74.20 |                   54.40  |       54.10 |          50.40 |        82.00 |         63.30 |            69.00 |
 |                | MultiHiertt (avg@4)            |       44.94 |                   31.62  |       38.10 |          37.57 |        55.28 |         52.83 |            52.83 |
 | **Others**     | IFEval                         |       35.81 |                   39.56  |       32.72 |          36.69 |        55.45 |         38.26 |            55.27 |
-|                | 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**        |
@@ -35,7 +35,7 @@ Example usage:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-model = "LLM360/Guru-32B"
 tokenizer = AutoTokenizer.from_pretrained(model)
 model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

 | **Science**    | GPQA-diamond (avg@4)           |       40.78 |                   38.64  |       37.63 |          35.98 |        50.63 |         55.67 |            46.46 |
 |                | SuperGPQA                      |       31.80 |                   30.64  |       29.75 |          27.29 |        43.60 |         46.05 |            37.73 |
 | **Logic**      | ARC-AGI (avg@4)                |        3.31 |                    0.75  |        0.00 |           0.50 |         7.63 |          2.31 |             5.25 |
+|                | Zebra Puzzle (avg@4)          |       39.40 |                    0.07  |        1.00 |           0.62 |        45.21 |          0.54 |             1.16 |
 | **Simulation** | CodeI/O (avg@4)                |       15.63 |                    7.13  |        5.13 |           6.63 |        12.63 |          3.75 |             9.75 |
 |                | CruxEval-I                     |       61.72 |                   63.63  |       69.38 |          56.25 |        80.63 |         71.13 |            72.63 |
 |                | CruxEval-O                     |       71.28 |                   56.50  |       65.88 |          58.31 |        88.75 |         82.38 |            67.75 |
 |                | HiTab                          |       74.20 |                   54.40  |       54.10 |          50.40 |        82.00 |         63.30 |            69.00 |
 |                | MultiHiertt (avg@4)            |       44.94 |                   31.62  |       38.10 |          37.57 |        55.28 |         52.83 |            52.83 |
 | **Others**     | IFEval                         |       35.81 |                   39.56  |       32.72 |          36.69 |        55.45 |         38.26 |            55.27 |
+|                | 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**        |
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+model = "LLM360/Guru-7B"
 tokenizer = AutoTokenizer.from_pretrained(model)
 model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")