Instructions to use EleutherAI/llemma_7b_muinstruct_camelmath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EleutherAI/llemma_7b_muinstruct_camelmath with Transformers:

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

pipe = pipeline("text-generation", model="EleutherAI/llemma_7b_muinstruct_camelmath")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/llemma_7b_muinstruct_camelmath")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/llemma_7b_muinstruct_camelmath", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EleutherAI/llemma_7b_muinstruct_camelmath with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EleutherAI/llemma_7b_muinstruct_camelmath"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EleutherAI/llemma_7b_muinstruct_camelmath",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EleutherAI/llemma_7b_muinstruct_camelmath

SGLang

How to use EleutherAI/llemma_7b_muinstruct_camelmath 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 "EleutherAI/llemma_7b_muinstruct_camelmath" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EleutherAI/llemma_7b_muinstruct_camelmath",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "EleutherAI/llemma_7b_muinstruct_camelmath" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EleutherAI/llemma_7b_muinstruct_camelmath",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EleutherAI/llemma_7b_muinstruct_camelmath with Docker Model Runner:
```
docker model run hf.co/EleutherAI/llemma_7b_muinstruct_camelmath
```

zhangirazerbayev commited on Mar 12, 2024

Commit

31a9f7b

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1 Parent(s): d67ebc9

Update README.md

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@@ -30,7 +30,7 @@ llemma_7b_muinstruct_camelmath` compares favorably to other 7B parameter models
 | [MAmmoTH 7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B)                      | 17\%       |
 | [MAmmoTH Coder 7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B)          | 11\%       |
 | [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) (few-shot)            | 23\%       |
-| [Llemma_7B_muinstruct_camelmath]                                               | 25\%       |
 | -                                                                              | -          |
 | [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) (few-shot)      | 22\%       |
 | [MetaMath Mistral 7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)    | 29\%       |

 | [MAmmoTH 7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B)                      | 17\%       |
 | [MAmmoTH Coder 7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B)          | 11\%       |
 | [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) (few-shot)            | 23\%       |
+| Llemma_7B_muinstruct_camelmath                                                 | 25\%       |
 | -                                                                              | -          |
 | [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) (few-shot)      | 22\%       |
 | [MetaMath Mistral 7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)    | 29\%       |