Instructions to use webbigdata/ALMA-7B-Ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use webbigdata/ALMA-7B-Ja with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="webbigdata/ALMA-7B-Ja")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("webbigdata/ALMA-7B-Ja") model = AutoModelForMultimodalLM.from_pretrained("webbigdata/ALMA-7B-Ja") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use webbigdata/ALMA-7B-Ja 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" # 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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/webbigdata/ALMA-7B-Ja
- SGLang
How to use webbigdata/ALMA-7B-Ja 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" \ --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", "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" \ --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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use webbigdata/ALMA-7B-Ja with Docker Model Runner:
docker model run hf.co/webbigdata/ALMA-7B-Ja
Update README.md
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README.md
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Icelandic and English
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Czech and English
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[Sample Code For Free Colab](https://github.com/webbigdata-jp/python_sample/blob/main/ALMA_7B_Ja_Free_Colab_sample.ipynb)
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Icelandic and English
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Czech and English
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Models | de | cs | is | zh | ru/jp | Avg. |
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|----------------|--------|--------|--------|--------|--------|--------|
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Translating from English (en→xx) BLEU/COMET
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NLLB-54B | 34.50/86.45 | 37.60/90.15 | 24.15/81.76 | 27.38/78.91 | 30.96/87.92 | 30.92/85.04 |
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GPT-3.5-D | 31.80/85.61 | 31.30/88.57 | 15.90/76.28 | 38.30/85.76 | 27.50/86.74 | 28.96/84.59 |
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ALMA-7B(Original)| 30.31/85.59 | 29.88/89.10 | 25.71/85.52 | 36.87/85.11 | 27.13/86.98 | 29.89/86.49 |
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ALMA-7B-Ja(Ours) | 23.70/82.04 | 18.58/81.36 | 12.20/71.59 | 29.06/82.45 | 14.82/85.40 | 19.67/80.57 |
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Translating to English (xx→en) BLEU/COMET
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NLLB-54B | 26.89/78.94 | 39.11/80.13 | 23.09/71.66 | 16.56/70.70 | 39.11/81.88 | 28.95/76.66 |
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GPT-3.5-D | 30.90/84.79 | 44.50/86.16 | 31.90/82.13 | 25.00/81.62 | 38.50/84.80 | 34.16/83.90 |
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ALMA-7B(Original)| 30.26/84.00 | 43.91/85.86 | 35.97/86.03 | 23.75/79.85 | 39.37/84.58 | 34.55/84.02 |
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ALMA-7B-Ja(Ours) | 26.41/83.13 | 34.39/83.50 | 24.77/81.12 | 20.60/78.54 | 15.57/78.61 | 24.35/81.76 |
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[Sample Code For Free Colab](https://github.com/webbigdata-jp/python_sample/blob/main/ALMA_7B_Ja_Free_Colab_sample.ipynb)
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