How to use from
SGLangUse 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
}'Quick Links
llemma_7b_muinstruct_camelmath is an instruction-following finetune of Llemma 7B, trained on the μInstruct and camel-ai/math datasets.
Input Formatting
Format input queries as follows:
input_text = f"Input:{input}\n\nResponse:"
Note that due to an error during training, this model's end-of-sequence token ID is 0 instead of the 2 which is standard for Llama-2 based models. Inference APIs should handle this automatically by reading this repo's config.json, but be aware of this difference if you are doing token surgery.
Evals
llemma_7b_muinstruct_camelmath compares favorably to other 7B parameter models on the Hungarian Math Exam. It surpasses the few-shot performance of Llemma 7B whilst being the strongest Llama-2 7B based model.
| Model | Exam Score |
|---|---|
| Code Llama 7B (few-shot) | 8% |
| MetaMath 7B | 20% |
| MAmmoTH 7B | 17% |
| MAmmoTH Coder 7B | 11% |
| Llemma 7B (few-shot) | 23% |
| Llemma_7B_muinstruct_camelmath | 25% |
| - | - |
| Mistral 7B (few-shot) | 22% |
| MetaMath Mistral 7B | 29% |
| OpenChat 3.5 | 37% |
- Downloads last month
- 16
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 }'