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, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("webbigdata/ALMA-7B-Ja") model = AutoModelForCausalLM.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
Browse files
README.md
CHANGED
|
@@ -50,6 +50,8 @@ ALMA-7B-Ja(Ours) | 26.41/83.13 | 34.39/83.50 | 24.77/81.12 | 20.60/78.54 | 15.57
|
|
| 50 |
mmnga made llama.cpp(gguf) version [webbigdata-ALMA-7B-Ja-gguf](https://huggingface.co/mmnga/webbigdata-ALMA-7B-Ja-gguf). Thank you!
|
| 51 |
llama.cpp is a tool used primarily on Macs, and gguf is its latest version format. It can be used without gpu.
|
| 52 |
|
|
|
|
|
|
|
| 53 |
|
| 54 |
### ALMA-7B-Ja-GPTQ-Ja-En
|
| 55 |
GPTQ is quantized(reduce the size of the model) method and ALMA-7B-Ja-GPTQ has GPTQ quantized version that reduces model size(3.9GB) and memory usage.
|
|
|
|
| 50 |
mmnga made llama.cpp(gguf) version [webbigdata-ALMA-7B-Ja-gguf](https://huggingface.co/mmnga/webbigdata-ALMA-7B-Ja-gguf). Thank you!
|
| 51 |
llama.cpp is a tool used primarily on Macs, and gguf is its latest version format. It can be used without gpu.
|
| 52 |
|
| 53 |
+
[ALMA-7B-Ja-gguf Free Colab sample](https://github.com/webbigdata-jp/python_sample/blob/main/ALMA_7B_Ja_gguf_Free_Colab_sample.ipynb)
|
| 54 |
+
|
| 55 |
|
| 56 |
### ALMA-7B-Ja-GPTQ-Ja-En
|
| 57 |
GPTQ is quantized(reduce the size of the model) method and ALMA-7B-Ja-GPTQ has GPTQ quantized version that reduces model size(3.9GB) and memory usage.
|