Text Generation
Transformers
PyTorch
Safetensors
multilingual
bloom
generation
question answering
instruction tuning
text-generation-inference
Instructions to use MaLA-LM/lucky52-bloom-7b1-no-28 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaLA-LM/lucky52-bloom-7b1-no-28 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MaLA-LM/lucky52-bloom-7b1-no-28")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-28") model = AutoModelForCausalLM.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-28") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MaLA-LM/lucky52-bloom-7b1-no-28 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaLA-LM/lucky52-bloom-7b1-no-28" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MaLA-LM/lucky52-bloom-7b1-no-28", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MaLA-LM/lucky52-bloom-7b1-no-28
- SGLang
How to use MaLA-LM/lucky52-bloom-7b1-no-28 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 "MaLA-LM/lucky52-bloom-7b1-no-28" \ --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": "MaLA-LM/lucky52-bloom-7b1-no-28", "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 "MaLA-LM/lucky52-bloom-7b1-no-28" \ --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": "MaLA-LM/lucky52-bloom-7b1-no-28", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MaLA-LM/lucky52-bloom-7b1-no-28 with Docker Model Runner:
docker model run hf.co/MaLA-LM/lucky52-bloom-7b1-no-28
- Xet hash:
- b57c1f51aa5a8c7d3021a930b1da7867da963b075820fefa0408acd2f8f5ae32
- Size of remote file:
- 6.27 kB
- SHA256:
- 96cf496637dc681261fad9293bdee59d69c9e7ea1a4d1ad14a6c9ab1721387a8
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