mirror of https://github.com/huggingface/transformers.git synced 2025-07-05 13:50:13 +06:00

* 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>

2025-03-03 10:33:46 -08:00

3.0 KiB

Raw Blame History

Gemma2

Overview

The Gemma2 model was proposed in Gemma2: Open Models Based on Gemini Technology and Research by Gemma2 Team, Google. Two Gemma2 models are released, with parameters sizes of 9 billion (9B) and 27 billion (27B).

The abstract from the blog post is the following:

Now we’re officially releasing Gemma 2 to researchers and developers globally. Available in both 9 billion (9B) and 27 billion (27B) parameter sizes, Gemma 2 is higher-performing and more efficient at inference than the first generation, with significant safety advancements built in. In fact, at 27B, it offers competitive alternatives to models more than twice its size, delivering the kind of performance that was only possible with proprietary models as recently as December.

Tips:

The original checkpoints can be converted using the conversion script src/transformers/models/Gemma2/convert_Gemma2_weights_to_hf.py

Gemma2 uses sliding window attention every second layer, which makes it unsuitable for typical kv caching with [~DynamicCache] or tuples of tensors. To enable caching in Gemma2 forward call, you must initialize a [~HybridCache] instance and pass it as past_key_values to the forward call. Note, that you also have to prepare cache_position if the past_key_values already contains previous keys and values.

This model was contributed by Arthur Zucker, Pedro Cuenca and Tom Arsen.

Gemma2Config

autodoc Gemma2Config

Gemma2Model

autodoc Gemma2Model - forward

Gemma2ForCausalLM

autodoc Gemma2ForCausalLM - forward

Gemma2ForSequenceClassification

autodoc Gemma2ForSequenceClassification - forward

Gemma2ForTokenClassification

autodoc Gemma2ForTokenClassification - forward

3.0 KiB Raw Blame History Unescape Escape