Instructions to use webbigdata/ALMA-7B-Ja-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbigdata/ALMA-7B-Ja-V2 with Transformers:

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

pipe = pipeline("text-generation", model="webbigdata/ALMA-7B-Ja-V2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("webbigdata/ALMA-7B-Ja-V2")
model = AutoModelForMultimodalLM.from_pretrained("webbigdata/ALMA-7B-Ja-V2")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbigdata/ALMA-7B-Ja-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webbigdata/ALMA-7B-Ja-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webbigdata/ALMA-7B-Ja-V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/webbigdata/ALMA-7B-Ja-V2

SGLang

How to use webbigdata/ALMA-7B-Ja-V2 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 "webbigdata/ALMA-7B-Ja-V2" \
    --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": "webbigdata/ALMA-7B-Ja-V2",
		"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 "webbigdata/ALMA-7B-Ja-V2" \
        --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": "webbigdata/ALMA-7B-Ja-V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use webbigdata/ALMA-7B-Ja-V2 with Docker Model Runner:
```
docker model run hf.co/webbigdata/ALMA-7B-Ja-V2
```

dahara1 commited on Nov 6, 2023

Commit

3cc36e1

1 Parent(s): 67a66cc

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -80,6 +80,7 @@ Here are the results of a comparison of various genres of writing with the actua
 | ALMA-7B-Ja-V2-GPTQ-Ja-En | 25.3       | 15.00   | 0.8848   | 60.3       | 26.82   | 0.6189   |
 | ALMA-Ja-V2               | 27.2       | 15.60   | 0.8868   | 58.5       | 29.27   | 0.6155   |
 | ALMA-7B-Ja-V2-Lora       | 24.5       | 13.58   | 0.8670   | 50.7       | 21.85   | 0.6196   |
 | gpt-3.5                  | 34.6       | 28.33   | 0.8895   | 74.5       | 49.20   | 0.6382   |
 | gpt-4.0                  | 36.5       | 28.07   | 0.9255   | 62.5       | 33.63   | 0.6320   |
 | google-translate         | 43.5       | 35.37   | 0.9181   | 62.7       | 29.22   | 0.6446   |
@@ -102,6 +103,7 @@ Here are the results of a comparison of various genres of writing with the actua
 | ALMA-7B-Ja-V2-GPTQ-Ja-En | 27.6       | 18.28   | 0.8643   | 52.1       | 24.58   | 0.6106   |
 | ALMA-Ja-V2               | 20.4       | 8.45    | 0.7870   | 48.7       | 23.06   | 0.6050   |
 | ALMA-7B-Ja-V2-Lora       | 23.9       | 18.55   | 0.8634   | 55.6       | 29.91   | 0.6093   |
 | gpt-3.5                  | 31.2       | 23.37   | 0.9001   | -          | -       | 0.5948   |
 | gpt-4.0                  | 30.7       | 24.31   | 0.8848   | 53.9       | 24.89   | 0.6163   |
 | google-translate         | 32.4       | 25.36   | 0.8968   | 58.5       | 29.88   | 0.6022   |

 | ALMA-7B-Ja-V2-GPTQ-Ja-En | 25.3       | 15.00   | 0.8848   | 60.3       | 26.82   | 0.6189   |
 | ALMA-Ja-V2               | 27.2       | 15.60   | 0.8868   | 58.5       | 29.27   | 0.6155   |
 | ALMA-7B-Ja-V2-Lora       | 24.5       | 13.58   | 0.8670   | 50.7       | 21.85   | 0.6196   |
+| SeamlessM4T              | 27.3       | 16.76   | 0.9070   | 54.2       | 25.76   | 0.5656   |
 | gpt-3.5                  | 34.6       | 28.33   | 0.8895   | 74.5       | 49.20   | 0.6382   |
 | gpt-4.0                  | 36.5       | 28.07   | 0.9255   | 62.5       | 33.63   | 0.6320   |
 | google-translate         | 43.5       | 35.37   | 0.9181   | 62.7       | 29.22   | 0.6446   |
 | ALMA-7B-Ja-V2-GPTQ-Ja-En | 27.6       | 18.28   | 0.8643   | 52.1       | 24.58   | 0.6106   |
 | ALMA-Ja-V2               | 20.4       | 8.45    | 0.7870   | 48.7       | 23.06   | 0.6050   |
 | ALMA-7B-Ja-V2-Lora       | 23.9       | 18.55   | 0.8634   | 55.6       | 29.91   | 0.6093   |
+| SeamlessM4T              | 25.5       | 19.97   | 0.8657   | 42.2       | 14.39   | 0.5554   |
 | gpt-3.5                  | 31.2       | 23.37   | 0.9001   | -          | -       | 0.5948   |
 | gpt-4.0                  | 30.7       | 24.31   | 0.8848   | 53.9       | 24.89   | 0.6163   |
 | google-translate         | 32.4       | 25.36   | 0.8968   | 58.5       | 29.88   | 0.6022   |