Instructions to use rishiraj/zephyr-math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rishiraj/zephyr-math with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rishiraj/zephyr-math")
model = AutoModelForCausalLM.from_pretrained("rishiraj/zephyr-math")
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 rishiraj/zephyr-math with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rishiraj/zephyr-math

SGLang

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

Docker Model Runner
How to use rishiraj/zephyr-math with Docker Model Runner:
```
docker model run hf.co/rishiraj/zephyr-math
```

rishiraj commited on Oct 25, 2023

Commit

115cadb

1 Parent(s): 3958f37

Update README.md

Browse files

Files changed (1) hide show

README.md +14 -1

README.md CHANGED Viewed

@@ -104,4 +104,17 @@ print(outputs[0]["generated_text"])
 | Arithmo-Mistral-7B  | 74.7         | 25.3        |
 | MetaMath-7B         | 66.5         | 19.8        |
 | MetaMath-13B        | 72.3         | 22.4        |
-| 🔥 **Zephyr-Math-7B** | **??**     | **??**        |

 | Arithmo-Mistral-7B  | 74.7         | 25.3        |
 | MetaMath-7B         | 66.5         | 19.8        |
 | MetaMath-13B        | 72.3         | 22.4        |
+| 🔥 **Zephyr-Math-7B** | **??**     | **??**        |
+## Citation
+```bibtex
+@software{acharya2023zephyrmath
+  title = {Zephyr Math: Zephyr 7B Alpha Model Fine-tuned on MetaMathQA Dataset},
+  author = {Rishiraj Acharya and Soumik Rakshit},
+  year = {2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+  howpublished = {\url{https://huggingface.co/rishiraj/zephyr-math}},
+}
+```