The BLOOM model has been proposed with its various versions through the BigScience Workshop. BigScience is inspired by other open science initiatives where researchers have pooled their time and resources to collectively achieve a higher impact. The architecture of BLOOM is essentially similar to GPT3 (auto-regressive model for next token prediction), but has been trained on 46 different languages and 13 programming languages. Several smaller versions of the models have been trained on the same dataset. BLOOM is available in the following versions:
A list of official Hugging Face and community (indicated by 馃寧) resources to help you get started with BLOOM. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
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BloomForCausalLM] is supported by this causal language modeling example script and notebook.
See also:
- Causal language modeling task guide
- Text classification task guide
- Token classification task guide
- Question answering task guide
鈿★笍 Inference
- A blog on Optimization story: Bloom inference.
- A blog on Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate.
鈿欙笍 Training
- A blog on The Technology Behind BLOOM Training.
[[autodoc]] BloomConfig - all
[[autodoc]] BloomTokenizerFast - all
[[autodoc]] BloomModel - forward
[[autodoc]] BloomForCausalLM - forward
[[autodoc]] BloomForSequenceClassification - forward
[[autodoc]] BloomForTokenClassification - forward
[[autodoc]] BloomForQuestionAnswering - forward
[[autodoc]] FlaxBloomModel - call
[[autodoc]] FlaxBloomForCausalLM - call