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

Libraries

How to use entfane/math-genius-7B with Transformers:

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

pipe = pipeline("text-generation", model="entfane/math-genius-7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("entfane/math-genius-7B")
model = AutoModelForCausalLM.from_pretrained("entfane/math-genius-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use entfane/math-genius-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "entfane/math-genius-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": "entfane/math-genius-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/entfane/math-genius-7B

SGLang

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

Docker Model Runner
How to use entfane/math-genius-7B with Docker Model Runner:
```
docker model run hf.co/entfane/math-genius-7B
```

entfane commited on Jul 16, 2025

Commit

ec071bb

verified ·

1 Parent(s): 625742c

Update README.md

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Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -14,7 +14,7 @@ pipeline_tag: text-generation
 <img src="https://huggingface.co/entfane/math_genious-7B/resolve/main/math-genious.png" width="400" height="400"/>
-# Math Genious 7B
 This model is a Math Chain-of-Thought fine-tuned version of Mistral 7B v0.3 Instruct model.
@@ -30,7 +30,7 @@ Model was fine-tuned on [entfane/Mixture-Of-Thoughts-Math-No-COT](https://huggin
 from transformers import AutoTokenizer, AutoModelForCausalLM
-model_name = "entfane/math-genious-7B"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 messages = [
@@ -48,5 +48,5 @@ The model was evaluated on a randomly sampled subset of 1,000 records from the t
 Math Genius 7B achieved an accuracy of 93.1% in producing the correct final answer under the pass@1 evaluation metric.
 #### AIME
-Math Genious 7B was evaluated on [90 problems from AIME 22, AIME 23, and AIME 24](https://huggingface.co/datasets/AI-MO/aimo-validation-aime).
 The model has successfully solved 3/90 of the problems.

 <img src="https://huggingface.co/entfane/math_genious-7B/resolve/main/math-genious.png" width="400" height="400"/>
+# Math Genius 7B
 This model is a Math Chain-of-Thought fine-tuned version of Mistral 7B v0.3 Instruct model.
 from transformers import AutoTokenizer, AutoModelForCausalLM
+model_name = "entfane/math-genius-7B"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 messages = [
 Math Genius 7B achieved an accuracy of 93.1% in producing the correct final answer under the pass@1 evaluation metric.
 #### AIME
+Math Genius 7B was evaluated on [90 problems from AIME 22, AIME 23, and AIME 24](https://huggingface.co/datasets/AI-MO/aimo-validation-aime).
 The model has successfully solved 3/90 of the problems.