Instructions to use entfane/math-professor-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use entfane/math-professor-3B with Transformers:

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

pipe = pipeline("text-generation", model="entfane/math-professor-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("entfane/math-professor-3B")
model = AutoModelForCausalLM.from_pretrained("entfane/math-professor-3B", 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-professor-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "entfane/math-professor-3B"
# 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-professor-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/entfane/math-professor-3B

SGLang

How to use entfane/math-professor-3B 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-professor-3B" \
    --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-professor-3B",
		"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-professor-3B" \
        --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-professor-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use entfane/math-professor-3B with Docker Model Runner:
```
docker model run hf.co/entfane/math-professor-3B
```

Improve language tag

by lbourdois - opened Apr 27, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+56

-44

Files changed (1) hide show

README.md +56 -44

README.md CHANGED Viewed

@@ -1,45 +1,57 @@
----
-library_name: transformers
-tags:
-- trl
-- sft
-datasets:
-- qwedsacf/grade-school-math-instructions
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-3B
----
-<img src="https://huggingface.co/entfane/math-professor-3B/resolve/main/math-professor-image.png" width="300" height="300"/>
-# Math Professor 3B
-This model is a math instruction fine-tuned version of Qwen2.5-3B model.
-### Fine-tuning dataset
-Model was fine-tuned on [qwedsacf/grade-school-math-instructions](https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions) instruction dataset.
-### Inference
-```python
-!pip install transformers accelerate
-from transformers import AutoTokenizer, AutoModelForCausalLM
-model_name = "entfane/math-professor-3B"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-messages = [
-    {"role": "user", "content": "What's the derivative of 2x^2?"}
-]
-input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-encoded_input = tokenizer(input, return_tensors = "pt").to(model.device)
-output = model.generate(**encoded_input, max_new_tokens=1024)
-print(tokenizer.decode(output[0], skip_special_tokens=False))
 ```

+---
+library_name: transformers
+tags:
+- trl
+- sft
+datasets:
+- qwedsacf/grade-school-math-instructions
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-3B
+---
+<img src="https://huggingface.co/entfane/math-professor-3B/resolve/main/math-professor-image.png" width="300" height="300"/>
+# Math Professor 3B
+This model is a math instruction fine-tuned version of Qwen2.5-3B model.
+### Fine-tuning dataset
+Model was fine-tuned on [qwedsacf/grade-school-math-instructions](https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions) instruction dataset.
+### Inference
+```python
+!pip install transformers accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_name = "entfane/math-professor-3B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+messages = [
+    {"role": "user", "content": "What's the derivative of 2x^2?"}
+]
+input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+encoded_input = tokenizer(input, return_tensors = "pt").to(model.device)
+output = model.generate(**encoded_input, max_new_tokens=1024)
+print(tokenizer.decode(output[0], skip_special_tokens=False))
 ```