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# 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">

```py
import torch
from transformers import pipeline

pipeline = pipeline(
    task="text-generation",
    model="google/gemma-2b",
    torch_dtype=torch.bfloat16,
    device="cuda",
)

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

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

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

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2b",
    torch_dtype=torch.bfloat16,
    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("cuda")

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

</hfoption>
<hfoption id="transformers-cli">

```bash
echo -e "LLMs generate text through a process known as" | transformers-cli run --task text-generation --model google/gemma-2b --device 0
```

</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.

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

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    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("cuda")
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.

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

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

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    <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 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",
       torch_dtype=torch.bfloat16,
       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("cuda")
   past_key_values = DynamicCache()
   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

## FlaxGemmaModel

[[autodoc]] FlaxGemmaModel
    - __call__

## FlaxGemmaForCausalLM

[[autodoc]] FlaxGemmaForCausalLM
    - __call__
