Instructions to use LLaMAX/LLaMAX2-7B-MetaMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLaMAX/LLaMAX2-7B-MetaMath with Transformers:

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

pipe = pipeline("text-generation", model="LLaMAX/LLaMAX2-7B-MetaMath")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLaMAX/LLaMAX2-7B-MetaMath")
model = AutoModelForCausalLM.from_pretrained("LLaMAX/LLaMAX2-7B-MetaMath")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLaMAX/LLaMAX2-7B-MetaMath with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLaMAX/LLaMAX2-7B-MetaMath"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLaMAX/LLaMAX2-7B-MetaMath",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LLaMAX/LLaMAX2-7B-MetaMath

SGLang

How to use LLaMAX/LLaMAX2-7B-MetaMath 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 "LLaMAX/LLaMAX2-7B-MetaMath" \
    --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": "LLaMAX/LLaMAX2-7B-MetaMath",
		"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 "LLaMAX/LLaMAX2-7B-MetaMath" \
        --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": "LLaMAX/LLaMAX2-7B-MetaMath",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLaMAX/LLaMAX2-7B-MetaMath with Docker Model Runner:
```
docker model run hf.co/LLaMAX/LLaMAX2-7B-MetaMath
```

LLaMAX commited on Jul 16, 2024

Commit

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@@ -66,13 +66,10 @@ the total number of words (1050) by the number of days in two weeks (14). So, th
 if our model helps your work, please cite this paper:
 ```
-@misc{lu2024llamaxscalinglinguistichorizons,
-      title={LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages},
-      author={Yinquan Lu and Wenhao Zhu and Lei Li and Yu Qiao and Fei Yuan},
-      year={2024},
-      eprint={2407.05975},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2407.05975},
 }
 ```

 if our model helps your work, please cite this paper:
 ```
+@article{lu2024llamax,
+  title={LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages},
+  author={Lu, Yinquan and Zhu, Wenhao and Li, Lei and Qiao, Yu and Yuan, Fei},
+  journal={arXiv preprint arXiv:2407.05975},
+  year={2024}
 }
 ```