Instructions to use gair-prox/math-doc-refining-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gair-prox/math-doc-refining-lm with Transformers:

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

pipe = pipeline("text-generation", model="gair-prox/math-doc-refining-lm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gair-prox/math-doc-refining-lm")
model = AutoModelForCausalLM.from_pretrained("gair-prox/math-doc-refining-lm", 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 gair-prox/math-doc-refining-lm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gair-prox/math-doc-refining-lm

SGLang

How to use gair-prox/math-doc-refining-lm 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 "gair-prox/math-doc-refining-lm" \
    --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": "gair-prox/math-doc-refining-lm",
		"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 "gair-prox/math-doc-refining-lm" \
        --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": "gair-prox/math-doc-refining-lm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gair-prox/math-doc-refining-lm with Docker Model Runner:
```
docker model run hf.co/gair-prox/math-doc-refining-lm
```

koalazf99 commited on Oct 10, 2024

Commit

ff506d2

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1 Parent(s): 7d31129

Update README.md

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@@ -5,7 +5,7 @@ datasets:
 language:
 - en
 base_model:
-- gair-prox/RedPJ-ProX-0.3B
 pipeline_tag: text-generation
 library_name: transformers
 tags:
@@ -21,7 +21,7 @@ tags:
 [ArXiv](http://arxiv.org/abs/2409.17115) | [Code](https://github.com/GAIR-NLP/program-every-example)
-**Math-chunk-refining-lm** is an adapted [0.3B-ProX](https://huggingface.co/gair-prox/RedPJ-ProX-0.3B) model, fine-tuned for chunk level refining via program generation, and can be applied over math pre-training corpus such as open-web-math.
 <p align="center">
   <img src="func_design.png">

 language:
 - en
 base_model:
+- gair-prox/RedPJ-ProX-0.7B
 pipeline_tag: text-generation
 library_name: transformers
 tags:
 [ArXiv](http://arxiv.org/abs/2409.17115) | [Code](https://github.com/GAIR-NLP/program-every-example)
+**Math-doc-refining-lm** is an adapted [0.7B-ProX](https://huggingface.co/gair-prox/RedPJ-ProX-0.7B) model, fine-tuned for doc level refining via program generation, and can be applied over math pre-training corpus such as open-web-math.
 <p align="center">
   <img src="func_design.png">