Instructions to use bigscience/bloom-560m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloom-560m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloom-560m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m") model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigscience/bloom-560m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloom-560m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/bloom-560m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloom-560m
- SGLang
How to use bigscience/bloom-560m 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 "bigscience/bloom-560m" \ --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": "bigscience/bloom-560m", "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 "bigscience/bloom-560m" \ --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": "bigscience/bloom-560m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloom-560m with Docker Model Runner:
docker model run hf.co/bigscience/bloom-560m
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@@ -122,13 +122,15 @@ Please see [the BLOOM training README](https://github.com/bigscience-workshop/bi
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* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
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* 24 layers, 16 attention heads
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* Hidden layers are 1024-dimensional
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* Sequence length of 2048 tokens
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**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
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#### **Training**
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_In progress._
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Current training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11-176B-ml-logs/)
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- Checkpoint size:
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- Bf16 weights: 329GB
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- Full checkpoint with optimizer states: 2.3TB
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- Training throughput: About 150
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- Number of epochs: 1 (*current target*)
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- Started 11th March, 2022 11:42am PST
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- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
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- Server training location: Île-de-France, France
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* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
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* 559,214,592 parameters:
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* 256,901,120 embedding parameters
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* 24 layers, 16 attention heads
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* Hidden layers are 1024-dimensional
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* Sequence length of 2048 tokens (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
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**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
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#### **Training**
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Training logs: [Tensorboard link](https://huggingface.co/bigscience/tr11e-350M-logs)
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- Training throughput: About 150 TFLOPs per GPU per second
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- Number of epochs: 1 (*current target*)
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- Started 11th March, 2022 11:42am PST
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- Ended 5th July, 2022
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- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)
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- Server training location: Île-de-France, France
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