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transformers/docs/source/en/model_doc/gemma.md
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transformers/docs/source/en/model_doc/gemma.md
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<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on 2024-03-13 and added to Hugging Face Transformers on 2024-02-21.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
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</div>
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</div>
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# Gemma
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[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.
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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.
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You can find all the original Gemma checkpoints under the [Gemma](https://huggingface.co/collections/google/gemma-release-65d5efbccdbb8c4202ec078b) release.
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> [!TIP]
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> Click on the Gemma models in the right sidebar for more examples of how to apply Gemma to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`] class, and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="text-generation",
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model="google/gemma-2b",
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dtype=torch.bfloat16,
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device_map="auto",
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)
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pipeline("LLMs generate text through a process known as", max_new_tokens=50)
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
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model = AutoModelForCausalLM.from_pretrained(
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"google/gemma-2b",
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dtype=torch.bfloat16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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input_text = "LLMs generate text through a process known as"
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input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
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outputs = model.generate(**input_ids, max_new_tokens=50, cache_implementation="static")
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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</hfoption>
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<hfoption id="transformers CLI">
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```bash
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echo -e "LLMs generate text through a process known as" | transformers run --task text-generation --model google/gemma-2b --device 0
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```
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</hfoption>
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</hfoptions>
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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.
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The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.
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```py
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#!pip install bitsandbytes
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_quant_type="nf4"
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)
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
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model = AutoModelForCausalLM.from_pretrained(
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"google/gemma-7b",
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quantization_config=quantization_config,
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device_map="auto",
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attn_implementation="sdpa"
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)
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input_text = "LLMs generate text through a process known as."
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input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**input_ids,
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max_new_tokens=50,
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cache_implementation="static"
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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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.
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```py
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from transformers.utils.attention_visualizer import AttentionMaskVisualizer
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visualizer = AttentionMaskVisualizer("google/gemma-2b")
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visualizer("LLMs generate text through a process known as")
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```
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<div class="flex justify-center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/gemma-attn-mask.png"/>
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</div>
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## Notes
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- 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.
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```py
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
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model = AutoModelForCausalLM.from_pretrained(
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"google/gemma-2b",
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dtype=torch.bfloat16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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input_text = "LLMs generate text through a process known as"
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input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
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past_key_values = DynamicCache(config=model.config)
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outputs = model.generate(**input_ids, max_new_tokens=50, past_key_values=past_key_values)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## GemmaConfig
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[[autodoc]] GemmaConfig
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## GemmaTokenizer
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[[autodoc]] GemmaTokenizer
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## GemmaTokenizerFast
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[[autodoc]] GemmaTokenizerFast
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## GemmaModel
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[[autodoc]] GemmaModel
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- forward
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## GemmaForCausalLM
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[[autodoc]] GemmaForCausalLM
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- forward
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## GemmaForSequenceClassification
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[[autodoc]] GemmaForSequenceClassification
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- forward
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## GemmaForTokenClassification
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[[autodoc]] GemmaForTokenClassification
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- forward
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