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--- |
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library_name: transformers |
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tags: [] |
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extra_gated_heading: "Access Gemma on Hugging Face" |
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extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately." |
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extra_gated_button_content: "Acknowledge license" |
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license: other |
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license_name: gemma-terms-of-use |
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license_link: https://ai.google.dev/gemma/terms |
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--- |
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# Gemma Model Card |
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**Model Page**: [Gemma](https://ai.google.dev/gemma/docs) |
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This model card corresponds to the 7B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). |
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**Resources and Technical Documentation**: |
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* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) |
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* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) |
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* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335) |
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**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) |
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**Authors**: Google |
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## Model Information |
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Summary description and brief definition of inputs and outputs. |
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### Description |
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Gemma is a family of lightweight, state-of-the-art open models from Google, |
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built from the same research and technology used to create the Gemini models. |
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They are text-to-text, decoder-only large language models, available in English, |
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with open weights, pre-trained variants, and instruction-tuned variants. Gemma |
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models are well-suited for a variety of text generation tasks, including |
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question answering, summarization, and reasoning. Their relatively small size |
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makes it possible to deploy them in environments with limited resources such as |
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a laptop, desktop or your own cloud infrastructure, democratizing access to |
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state of the art AI models and helping foster innovation for everyone. |
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### Usage |
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Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. |
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#### Fine-tuning the model |
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You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-7b-it`. |
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In that repository, we provide: |
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* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA |
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* A script to perform SFT using FSDP on TPU devices |
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* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset |
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#### Running the model on a CPU |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") |
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model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it") |
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input_text = "Write me a poem about Machine Learning." |
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input_ids = tokenizer(**input_text, return_tensors="pt") |
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outputs = model.generate(input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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#### Running the model on a single / multi GPU |
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```python |
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# pip install accelerate |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") |
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model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", device_map="auto") |
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input_text = "Write me a poem about Machine Learning." |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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#### Running the model on a GPU using different precisions |
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* _Using `torch.float16`_ |
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```python |
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# pip install accelerate |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") |
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model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", device_map="auto", torch_dtype=torch.float16) |
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input_text = "Write me a poem about Machine Learning." |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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* _Using `torch.bfloat16`_ |
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```python |
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# pip install accelerate |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") |
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model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", device_map="auto", torch_dtype=torch.bfloat16) |
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input_text = "Write me a poem about Machine Learning." |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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#### Quantized Versions through `bitsandbytes` |
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* _Using 8-bit precision (int8)_ |
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```python |
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# pip install bitsandbytes accelerate |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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quantization_config = BitsAndBytesConfig(load_in_8bit=True) |
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") |
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model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", quantization_config=quantization_config) |
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input_text = "Write me a poem about Machine Learning." |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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* _Using 4-bit precision_ |
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```python |
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# pip install bitsandbytes accelerate |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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quantization_config = BitsAndBytesConfig(load_in_4bit=True) |
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") |
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model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", quantization_config=quantization_config) |
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input_text = "Write me a poem about Machine Learning." |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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#### Other optimizations |
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* _Flash Attention 2_ |
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First make sure to install `flash-attn` in your environment `pip install flash-attn` |
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```diff |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.float16, |
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+ attn_implementation="flash_attention_2" |
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).to(0) |
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``` |
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### Chat Template |
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The instruction-tuned models use a chat template that must be adhered to for conversational use. |
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The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. |
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Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: |
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```py |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import transformers |
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import torch |
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model_id = "gg-hf/gemma-7b-it" |
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dtype = torch.bfloat16 |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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device_map="cuda", |
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torch_dtype=dtype, |
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) |
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chat = [ |
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{ "role": "user", "content": "Write a hello world program" }, |
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] |
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) |
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``` |
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At this point, the prompt contains the following text: |
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``` |
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<start_of_turn>user |
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Write a hello world program<end_of_turn> |
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<start_of_turn>model |
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``` |
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As you can see, each turn is preceeded by a `<start_of_turn>` delimiter and then the role of the entity |
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(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with |
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the `<end_of_turn>` token. |
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You can follow this format to build the prompt manually, if you need to do it without the tokenizer's |
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chat template. |
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After the prompt is ready, generation can be performed like this: |
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```py |
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inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt") |
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outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) |
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``` |
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### Inputs and outputs |
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* **Input:** Text string, such as a question, a prompt, or a document to be |
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summarized. |
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* **Output:** Generated English-language text in response to the input, such |
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as an answer to a question, or a summary of a document. |
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## Model Data |
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Data used for model training and how the data was processed. |
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### Training Dataset |
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These models were trained on a dataset of text data that includes a wide variety |
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of sources, totaling 6 trillion tokens. Here are the key components: |
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* Web Documents: A diverse collection of web text ensures the model is exposed |
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to a broad range of linguistic styles, topics, and vocabulary. Primarily |
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English-language content. |
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* Code: Exposing the model to code helps it to learn the syntax and patterns of |
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programming languages, which improves its ability to generate code or |
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understand code-related questions. |
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* Mathematics: Training on mathematical text helps the model learn logical |
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reasoning, symbolic representation, and to address mathematical queries. |
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The combination of these diverse data sources is crucial for training a powerful |
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language model that can handle a wide variety of different tasks and text |
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formats. |
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### Data Preprocessing |
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Here are the key data cleaning and filtering methods applied to the training |
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data: |
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* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was |
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applied at multiple stages in the data preparation process to ensure the |
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exclusion of harmful and illegal content |
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* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and |
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reliable, automated techniques were used to filter out certain personal |
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information and other sensitive data from training sets. |
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* Additional methods: Filtering based on content quality and safely in line with |
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[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). |
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## Implementation Information |
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Details about the model internals. |
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### Hardware |
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Gemma was trained using the latest generation of |
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[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). |
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Training large language models requires significant computational power. TPUs, |
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designed specifically for matrix operations common in machine learning, offer |
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several advantages in this domain: |
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* Performance: TPUs are specifically designed to handle the massive computations |
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involved in training LLMs. They can speed up training considerably compared to |
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CPUs. |
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* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing |
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for the handling of large models and batch sizes during training. This can |
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lead to better model quality. |
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* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for |
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handling the growing complexity of large foundation models. You can distribute |
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training across multiple TPU devices for faster and more efficient processing. |
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* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective |
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solution for training large models compared to CPU-based infrastructure, |
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especially when considering the time and resources saved due to faster |
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training. |
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* These advantages are aligned with |
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[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). |
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### Software |
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Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways). |
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JAX allows researchers to take advantage of the latest generation of hardware, |
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including TPUs, for faster and more efficient training of large models. |
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ML Pathways is Google's latest effort to build artificially intelligent systems |
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capable of generalizing across multiple tasks. This is specially suitable for |
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[foundation models](https://ai.google/discover/foundation-models/), including large language models like |
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these ones. |
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Together, JAX and ML Pathways are used as described in the |
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[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single |
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controller' programming model of Jax and Pathways allows a single Python |
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process to orchestrate the entire training run, dramatically simplifying the |
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development workflow." |
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## Evaluation |
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Model evaluation metrics and results. |
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### Benchmark Results |
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These models were evaluated against a large collection of different datasets and |
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metrics to cover different aspects of text generation: |
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| Benchmark | Metric | 2B Params | 7B Params | |
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| ------------------------------ | ------------- | ----------- | --------- | |
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| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 | |
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| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 | |
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| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 | |
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| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 59.7 | 51.8 | |
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| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 | |
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| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 | |
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| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 | |
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| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 | |
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| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 | |
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| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 | |
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| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 | |
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| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | - | 23 | |
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| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 | |
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| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 | |
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| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 | |
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| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 | |
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| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 | |
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| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 | |
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| ------------------------------ | ------------- | ----------- | --------- | |
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| **Average** | | **54.0** | **56.4** | |
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## Ethics and Safety |
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Ethics and safety evaluation approach and results. |
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### Evaluation Approach |
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Our evaluation methods include structured evaluations and internal red-teaming |
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testing of relevant content policies. Red-teaming was conducted by a number of |
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different teams, each with different goals and human evaluation metrics. These |
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models were evaluated against a number of different categories relevant to |
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ethics and safety, including: |
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* Text-to-Text Content Safety: Human evaluation on prompts covering safety |
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policies including child sexual abuse and exploitation, harassment, violence |
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and gore, and hate speech. |
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* Text-to-Text Representational Harms: Benchmark against relevant academic |
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datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). |
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* Memorization: Automated evaluation of memorization of training data, including |
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the risk of personally identifiable information exposure. |
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* Large-scale harm: Tests for "dangerous capabilities," such as chemical, |
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biological, radiological, and nuclear (CBRN) risks. |
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### Evaluation Results |
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The results of ethics and safety evaluations are within acceptable thresholds |
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for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child |
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safety, content safety, representational harms, memorization, large-scale harms. |
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On top of robust internal evaluations, the results of well known safety |
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benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA |
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are shown here. |
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| Benchmark | Metric | 2B Params | 7B Params | |
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| ------------------------------ | ------------- | ----------- | --------- | |
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| [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 | |
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| [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 | |
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| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 | |
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| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 | |
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| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 | |
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| [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 | |
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| [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 | |
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| [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 | |
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| [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 | |
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| [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 | |
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| ------------------------------ | ------------- | ----------- | --------- | |
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## Usage and Limitations |
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These models have certain limitations that users should be aware of. |
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### Intended Usage |
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Open Large Language Models (LLMs) have a wide range of applications across |
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various industries and domains. The following list of potential uses is not |
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comprehensive. The purpose of this list is to provide contextual information |
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about the possible use-cases that the model creators considered as part of model |
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training and development. |
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* Content Creation and Communication |
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* Text Generation: These models can be used to generate creative text formats |
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such as poems, scripts, code, marketing copy, and email drafts. |
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* Chatbots and Conversational AI: Power conversational interfaces for customer |
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service, virtual assistants, or interactive applications. |
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* Text Summarization: Generate concise summaries of a text corpus, research |
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papers, or reports. |
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* Research and Education |
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* Natural Language Processing (NLP) Research: These models can serve as a |
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foundation for researchers to experiment with NLP techniques, develop |
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algorithms, and contribute to the advancement of the field. |
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* Language Learning Tools: Support interactive language learning experiences, |
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aiding in grammar correction or providing writing practice. |
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* Knowledge Exploration: Assist researchers in exploring large bodies of text |
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by generating summaries or answering questions about specific topics. |
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### Limitations |
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* Training Data |
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* The quality and diversity of the training data significantly influence the |
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model's capabilities. Biases or gaps in the training data can lead to |
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limitations in the model's responses. |
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* The scope of the training dataset determines the subject areas the model can |
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handle effectively. |
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* Context and Task Complexity |
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* LLMs are better at tasks that can be framed with clear prompts and |
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instructions. Open-ended or highly complex tasks might be challenging. |
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* A model's performance can be influenced by the amount of context provided |
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(longer context generally leads to better outputs, up to a certain point). |
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* Language Ambiguity and Nuance |
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* Natural language is inherently complex. LLMs might struggle to grasp subtle |
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nuances, sarcasm, or figurative language. |
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* Factual Accuracy |
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* LLMs generate responses based on information they learned from their |
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training datasets, but they are not knowledge bases. They may generate |
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incorrect or outdated factual statements. |
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* Common Sense |
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* LLMs rely on statistical patterns in language. They might lack the ability |
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to apply common sense reasoning in certain situations. |
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### Ethical Considerations and Risks |
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The development of large language models (LLMs) raises several ethical concerns. |
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In creating an open model, we have carefully considered the following: |
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* Bias and Fairness |
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* LLMs trained on large-scale, real-world text data can reflect socio-cultural |
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biases embedded in the training material. These models underwent careful |
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scrutiny, input data pre-processing described and posterior evaluations |
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reported in this card. |
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* Misinformation and Misuse |
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* LLMs can be misused to generate text that is false, misleading, or harmful. |
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* Guidelines are provided for responsible use with the model, see the |
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[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). |
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* Transparency and Accountability: |
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* This model card summarizes details on the models' architecture, |
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capabilities, limitations, and evaluation processes. |
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* A responsibly developed open model offers the opportunity to share |
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innovation by making LLM technology accessible to developers and researchers |
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across the AI ecosystem. |
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Risks identified and mitigations: |
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|
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* Perpetuation of biases: It's encouraged to perform continuous monitoring |
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(using evaluation metrics, human review) and the exploration of de-biasing |
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techniques during model training, fine-tuning, and other use cases. |
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* Generation of harmful content: Mechanisms and guidelines for content safety |
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are essential. Developers are encouraged to exercise caution and implement |
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appropriate content safety safeguards based on their specific product policies |
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and application use cases. |
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* Misuse for malicious purposes: Technical limitations and developer and |
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end-user education can help mitigate against malicious applications of LLMs. |
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Educational resources and reporting mechanisms for users to flag misuse are |
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provided. Prohibited uses of Gemma models are outlined in the |
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[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). |
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* Privacy violations: Models were trained on data filtered for removal of PII |
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(Personally Identifiable Information). Developers are encouraged to adhere to |
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privacy regulations with privacy-preserving techniques. |
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### Benefits |
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At the time of release, this family of models provides high-performance open |
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large language model implementations designed from the ground up for Responsible |
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AI development compared to similarly sized models. |
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Using the benchmark evaluation metrics described in this document, these models |
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have shown to provide superior performance to other, comparably-sized open model |
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alternatives. |
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