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
```

huangtao6 commited on Jul 9, 2024

Commit

77d70d5

1 Parent(s): 2039528

update readme

Browse files

Files changed (1) hide show

README.md +11 -8

README.md CHANGED Viewed

@@ -12,6 +12,17 @@
 🔥 LLaMAX2-7B-MetaMath demonstrates good multilingual math reasoning capability in all languages, improving the average accuracy by 6.2% across all languages in MGSM dataset.
 ### Model Usage
 Prompt template:
@@ -45,14 +56,6 @@ two weeks. To find out how many words are in each daily crossword puzzle, we nee
 the total number of words (1050) by the number of days in two weeks (14). So, there are
 1050/14 = 75 words in each daily crossword puzzle on average. #### The answer is: 75“
 ```
-### Experiments
-We evaluated LLaMAX2-7B-MetaMath on the MGSM dataset. Compared with MetaMath-7B, LLaMAX-7B-MetaMath achieves a leading on both high-resource languages (Hrl.) and low-resource languages (Lrl.).
-| MGSM                      | Avg.    | Lrl. | Hrl.   | Bn     | Th   | Sw | Ja    | Zh   | De | Fr | Ru   | Es | En |
-|---------------------------|---------|------|--------|--------|------|----|----|------|----|----|------|------|--------|
-| MetaMath-7B (official)    | 38.32   | 6.9  | 51.8   | 6.8	   | 7.2  |6.8| 36.4 | 38.4 | 55.2|54.4| 52.0 |57.2|68.8|
-| MetaMath-7B (Reproduced)  | 38.08   | 6.8  | 51.5   | 6.0    | 10.0 |4.4| 36.4 |42.8|52.8|56.0|48.8|58.8|64.8|
-| LLaMAX2-7B-MetaMath       | 44.28   | 25.6 | 52.3   | 26.8   | 24.0 |26.0| 35.6 |42.4|56.8|55.2|53.6|56.8|65.6|
 ### Citation
 if our model helps your work, please cite this paper:

 🔥 LLaMAX2-7B-MetaMath demonstrates good multilingual math reasoning capability in all languages, improving the average accuracy by 6.2% across all languages in MGSM dataset.
+### Experiments
+We evaluated LLaMAX2-7B-MetaMath on the MGSM dataset. Compared with MetaMath-7B, LLaMAX-7B-MetaMath achieves a leading on both high-resource languages (Hrl.) and low-resource languages (Lrl.).
+| MGSM                      | Avg.    | Lrl. | Hrl.   | Bn     | Th   | Sw | Ja    | Zh   | De | Fr | Ru   | Es | En |
+|---------------------------|---------|------|--------|--------|------|----|----|------|----|----|------|------|--------|
+| MetaMath-7B (official)    | 38.32   | 6.9  | 51.8   | 6.8	   | 7.2  |6.8| 36.4 | 38.4 | 55.2|54.4| 52.0 |57.2|68.8|
+| MetaMath-7B (Reproduced)  | 38.08   | 6.8  | 51.5   | 6.0    | 10.0 |4.4| 36.4 |42.8|52.8|56.0|48.8|58.8|64.8|
+| LLaMAX2-7B-MetaMath       | 44.28   | 25.6 | 52.3   | 26.8   | 24.0 |26.0| 35.6 |42.4|56.8|55.2|53.6|56.8|65.6|
 ### Model Usage
 Prompt template:
 the total number of words (1050) by the number of days in two weeks (14). So, there are
 1050/14 = 75 words in each daily crossword puzzle on average. #### The answer is: 75“
 ```
 ### Citation
 if our model helps your work, please cite this paper: