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* toctree * not-doctested.txt * collapse sections * feedback * update * rewrite get started sections * fixes * fix * loading models * fix * customize models * share * fix link * contribute part 1 * contribute pt 2 * fix toctree * tokenization pt 1 * Add new model (#32615) * v1 - working version * fix * fix * fix * fix * rename to correct name * fix title * fixup * rename files * fix * add copied from on tests * rename to `FalconMamba` everywhere and fix bugs * fix quantization + accelerate * fix copies * add `torch.compile` support * fix tests * fix tests and add slow tests * copies on config * merge the latest changes * fix tests * add few lines about instruct * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * fix tests --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * "to be not" -> "not to be" (#32636) * "to be not" -> "not to be" * Update sam.md * Update trainer.py * Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * fix hfoption tag * tokenization pt. 2 * image processor * fix toctree * backbones * feature extractor * fix file name * processor * update not-doctested * update * make style * fix toctree * revision * make fixup * fix toctree * fix * make style * fix hfoption tag * pipeline * pipeline gradio * pipeline web server * add pipeline * fix toctree * not-doctested * prompting * llm optims * fix toctree * fixes * cache * text generation * fix * chat pipeline * chat stuff * xla * torch.compile * cpu inference * toctree * gpu inference * agents and tools * gguf/tiktoken * finetune * toctree * trainer * trainer pt 2 * optims * optimizers * accelerate * parallelism * fsdp * update * distributed cpu * hardware training * gpu training * gpu training 2 * peft * distrib debug * deepspeed 1 * deepspeed 2 * chat toctree * quant pt 1 * quant pt 2 * fix toctree * fix * fix * quant pt 3 * quant pt 4 * serialization * torchscript * scripts * tpu * review * model addition timeline * modular * more reviews * reviews * fix toctree * reviews reviews * continue reviews * more reviews * modular transformers * more review * zamba2 * fix * all frameworks * pytorch * supported model frameworks * flashattention * rm check_table * not-doctested.txt * rm check_support_list.py * feedback * updates/feedback * review * feedback * fix * update * feedback * updates * update --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
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Falcon
Overview
Falcon is a class of causal decoder-only models built by TII. The largest Falcon checkpoints have been trained on >=1T tokens of text, with a particular emphasis on the RefinedWeb corpus. They are made available under the Apache 2.0 license.
Falcon's architecture is modern and optimized for inference, with multi-query attention and support for efficient
attention variants like FlashAttention. Both 'base' models trained only as causal language models as well as
'instruct' models that have received further fine-tuning are available.
Falcon models are (as of 2023) some of the largest and most powerful open-source language models, and consistently rank highly in the OpenLLM leaderboard.
Converting custom checkpoints
Falcon models were initially added to the Hugging Face Hub as custom code checkpoints. However, Falcon is now fully
supported in the Transformers library. If you fine-tuned a model from a custom code checkpoint, we recommend converting
your checkpoint to the new in-library format, as this should give significant improvements to stability and
performance, especially for generation, as well as removing the need to use trust_remote_code=True!
You can convert custom code checkpoints to full Transformers checkpoints using the convert_custom_code_checkpoint.py
script located in the
Falcon model directory
of the Transformers library. To use this script, simply call it with
python convert_custom_code_checkpoint.py --checkpoint_dir my_model. This will convert your checkpoint in-place, and
you can immediately load it from the directory afterwards with e.g. from_pretrained(). If your model hasn't been
uploaded to the Hub, we recommend making a backup before attempting the conversion, just in case!
FalconConfig
autodoc FalconConfig - all
FalconModel
autodoc FalconModel - forward
FalconForCausalLM
autodoc FalconForCausalLM - forward
FalconForSequenceClassification
autodoc FalconForSequenceClassification - forward
FalconForTokenClassification
autodoc FalconForTokenClassification - forward
FalconForQuestionAnswering
autodoc FalconForQuestionAnswering - forward