Instructions to use ybelkada/bloom-1b7-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ybelkada/bloom-1b7-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ybelkada/bloom-1b7-8bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ybelkada/bloom-1b7-8bit") model = AutoModelForCausalLM.from_pretrained("ybelkada/bloom-1b7-8bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ybelkada/bloom-1b7-8bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ybelkada/bloom-1b7-8bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ybelkada/bloom-1b7-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ybelkada/bloom-1b7-8bit
- SGLang
How to use ybelkada/bloom-1b7-8bit 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 "ybelkada/bloom-1b7-8bit" \ --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": "ybelkada/bloom-1b7-8bit", "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 "ybelkada/bloom-1b7-8bit" \ --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": "ybelkada/bloom-1b7-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ybelkada/bloom-1b7-8bit with Docker Model Runner:
docker model run hf.co/ybelkada/bloom-1b7-8bit
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README.md
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@@ -89,4 +89,11 @@ Then just call `push_to_hub` method or `save_pretrained` method if you want to s
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model.push_to_hub("{your_username}/bloom-1b7-8bit")
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```
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That's it!
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model.push_to_hub("{your_username}/bloom-1b7-8bit")
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```
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That's it!
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## What is inside the model's `state_dict`?
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Inside the state dict of the model (`pytorch_model.bin` file) you have
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- the quantized `int8` weights
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- the quantization statistics in `float16`
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