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*This model was published in HF papers on 2024-03-13 and contributed to Hugging Face Transformers on 2024-02-21.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
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</div>

# Gemma

[Gemma](https://huggingface.co/papers/2403.08295) is a family of lightweight language models with pretrained and instruction-tuned variants, available in 2B and 7B parameters. The architecture is based on a transformer decoder-only design. It features Multi-Query Attention, rotary positional embeddings (RoPE), GeGLU activation functions, and RMSNorm layer normalization.

The instruction-tuned variant was fine-tuned with supervised learning on instruction-following data, followed by reinforcement learning from human feedback (RLHF) to align the model outputs with human preferences.

You can find all the original Gemma checkpoints under the [Gemma](https://huggingface.co/collections/google/gemma-release-65d5efbccdbb8c4202ec078b) release.

> [!TIP]
> Click on the Gemma models in the right sidebar for more examples of how to apply Gemma to different language tasks.

The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`] class, and from the command line.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


pipeline = pipeline(
    task="text-generation",
    model="google/gemma-2b",
    device_map="auto",
)

pipeline("LLMs generate text through a process known as", max_new_tokens=50)
```

</hfoption>
<hfoption id="AutoModel">

```python
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2b",
    device_map="auto",
    attn_implementation="sdpa"
)

input_text = "LLMs generate text through a process known as"
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)

outputs = model.generate(**input_ids, max_new_tokens=50, cache_implementation="static")
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.

```python
#!pip install bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig


quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4"
)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-7b",
    quantization_config=quantization_config,
    device_map="auto",
    attn_implementation="sdpa"
)

input_text = "LLMs generate text through a process known as."
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **input_ids,
    max_new_tokens=50,
    cache_implementation="static"
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.

```python
from transformers.utils.attention_visualizer import AttentionMaskVisualizer


visualizer = AttentionMaskVisualizer("google/gemma-2b")
visualizer("LLMs generate text through a process known as")
```

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/gemma-attn-mask.png"/>
</div>

## Notes

- The original Gemma models support standard kv-caching used in many transformer-based language models. You can use the default [`DynamicCache`] instance or a tuple of tensors for past key values during generation. This makes it compatible with typical autoregressive generation workflows.

   ```py
   import torch
   from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache

   tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
   model = AutoModelForCausalLM.from_pretrained(
       "google/gemma-2b",
          device_map="auto",
       attn_implementation="sdpa"
   )
   input_text = "LLMs generate text through a process known as"
   input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
   past_key_values = DynamicCache(config=model.config)
   outputs = model.generate(**input_ids, max_new_tokens=50, past_key_values=past_key_values)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   ```

## GemmaConfig

[[autodoc]] GemmaConfig

## GemmaTokenizer

[[autodoc]] GemmaTokenizer

## GemmaTokenizerFast

[[autodoc]] GemmaTokenizerFast

## GemmaModel

[[autodoc]] GemmaModel
    - forward

## GemmaForCausalLM

[[autodoc]] GemmaForCausalLM
    - forward

## GemmaForSequenceClassification

[[autodoc]] GemmaForSequenceClassification
    - forward

## GemmaForTokenClassification

[[autodoc]] GemmaForTokenClassification
    - forward
