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	---
	license: gemma
	library_name: transformers
	tags:
	- GGUF
	extra_gated_heading: Access Gemma on Hugging Face
	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.
	extra_gated_button_content: Acknowledge license
	quantized_by: andrijdavid
	---
	# gemma-7b-GGUF
	- Original model: [gemma-7b](https://huggingface.co/google/gemma-7b)

	<!-- description start -->
	## Description

	This repo contains GGUF format model files for [gemma-7b](https://huggingface.co/google/gemma-7b).

	<!-- description end -->
	<!-- README_GGUF.md-about-gguf start -->
	### About GGUF
	GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
	Here is an incomplete list of clients and libraries that are known to support GGUF:
	* [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
	* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
	* [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications
	* [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
	* [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
	* [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
	* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
	* [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
	* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
	* [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
	* [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
	* [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents.
	<!-- README_GGUF.md-about-gguf end -->

	<!-- compatibility_gguf start -->
	## Explanation of quantisation methods
	<details>
	<summary>Click to see details</summary>
	The new methods available are:

	* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
	* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
	* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
	* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
	* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.
	</details>
	<!-- compatibility_gguf end -->

	<!-- README_GGUF.md-how-to-download start -->
	## How to download GGUF files

	Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

	The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

	* LM Studio
	* LoLLMS Web UI
	* Faraday.dev

	### In `text-generation-webui`

	Under Download Model, you can enter the model repo: LiteLLMs/gemma-7b-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf.

	Then click Download.

	### On the command line, including multiple files at once

	I recommend using the `huggingface-hub` Python library:

	```shell
	pip3 install huggingface-hub
	```

	Then you can download any individual model file to the current directory, at high speed, with a command like this:

	```shell
	huggingface-cli download LiteLLMs/gemma-7b-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
	```

	<details>
	<summary>More advanced huggingface-cli download usage (click to read)</summary>

	You can also download multiple files at once with a pattern:

	```shell
	huggingface-cli download LiteLLMs/gemma-7b-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf'
	```

	For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).

	To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:

	```shell
	pip3 install huggingface_hub[hf_transfer]
	```

	And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:

	```shell
	HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/gemma-7b-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
	```

	Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
	</details>
	<!-- README_GGUF.md-how-to-download end -->
	<!-- README_GGUF.md-how-to-run start -->
	## Example `llama.cpp` command

	Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.

	```shell
	./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
	```

	Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

	Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

	If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

	For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

	## How to run in `text-generation-webui`

	Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

	## How to run from Python code

	You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

	### How to load this model in Python code, using llama-cpp-python

	For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).

	#### First install the package

	Run one of the following commands, according to your system:

	```shell
	# Base ctransformers with no GPU acceleration
	pip install llama-cpp-python
	# With NVidia CUDA acceleration
	CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
	# Or with OpenBLAS acceleration
	CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
	# Or with CLBLast acceleration
	CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
	# Or with AMD ROCm GPU acceleration (Linux only)
	CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
	# Or with Metal GPU acceleration for macOS systems only
	CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
	# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
	$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
	pip install llama-cpp-python
	```

	#### Simple llama-cpp-python example code

	```python
	from llama_cpp import Llama
	# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
	llm = Llama(
	model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first
	n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
	n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
	n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
	)
	# Simple inference example
	output = llm(
	"<PROMPT>", # Prompt
	max_tokens=512, # Generate up to 512 tokens
	stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
	echo=True # Whether to echo the prompt
	)
	# Chat Completion API
	llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
	llm.create_chat_completion(
	messages = [
	{"role": "system", "content": "You are a story writing assistant."},
	{
	"role": "user",
	"content": "Write a story about llamas."
	}
	]
	)
	```

	## How to use with LangChain

	Here are guides on using llama-cpp-python and ctransformers with LangChain:

	* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
	* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)

	<!-- README_GGUF.md-how-to-run end -->

	<!-- footer end -->

	<!-- original-model-card start -->
	# Original model card: gemma-7b


	# Gemma Model Card

	Model Page: [Gemma](https://ai.google.dev/gemma/docs)

	This model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it).

	Resources and Technical Documentation:

	* [Gemma Technical Report](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf)
	* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
	* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
	* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-7b-gg-hf)

	Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)

	Authors: Google

	## Model Information

	Summary description and brief definition of inputs and outputs.

	### Description

	Gemma is a family of lightweight, state-of-the-art open models from Google,
	built from the same research and technology used to create the Gemini models.
	They are text-to-text, decoder-only large language models, available in English,
	with open weights, pre-trained variants, and instruction-tuned variants. Gemma
	models are well-suited for a variety of text generation tasks, including
	question answering, summarization, and reasoning. Their relatively small size
	makes it possible to deploy them in environments with limited resources such as
	a laptop, desktop or your own cloud infrastructure, democratizing access to
	state of the art AI models and helping foster innovation for everyone.

	### Context Length
	Models are trained on a context length of 8192 tokens.

	### Usage

	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.

	#### Fine-tuning examples

	You can find fine-tuning notebooks under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples). We provide:

	* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using [QLoRA](https://huggingface.co/papers/2305.14314)
	* A script to perform SFT using FSDP on TPU devices
	* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset. You can also find the copy of the notebook [here](https://github.com/huggingface/notebooks/blob/main/peft/gemma_7b_english_quotes.ipynb).

	#### Running the model on a CPU


	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
	model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")

	input_text = "Write me a poem about Machine Learning."
	input_ids = tokenizer(input_text, return_tensors="pt")

	outputs = model.generate(**input_ids)
	print(tokenizer.decode(outputs[0]))
	```


	#### Running the model on a single / multi GPU


	```python
	# pip install accelerate
	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
	model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto")

	input_text = "Write me a poem about Machine Learning."
	input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

	outputs = model.generate(**input_ids)
	print(tokenizer.decode(outputs[0]))
	```


	#### Running the model on a GPU using different precisions

	* _Using `torch.float16`_

	```python
	# pip install accelerate
	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
	model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", revision="float16")

	input_text = "Write me a poem about Machine Learning."
	input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

	outputs = model.generate(**input_ids)
	print(tokenizer.decode(outputs[0]))
	```

	* _Using `torch.bfloat16`_

	```python
	# pip install accelerate
	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
	model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.bfloat16)

	input_text = "Write me a poem about Machine Learning."
	input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

	outputs = model.generate(**input_ids)
	print(tokenizer.decode(outputs[0]))
	```

	#### Quantized Versions through `bitsandbytes`

	* _Using 8-bit precision (int8)_

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

	quantization_config = BitsAndBytesConfig(load_in_8bit=True)

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
	model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)

	input_text = "Write me a poem about Machine Learning."
	input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

	outputs = model.generate(**input_ids)
	print(tokenizer.decode(outputs[0]))
	```

	* _Using 4-bit precision_

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

	quantization_config = BitsAndBytesConfig(load_in_4bit=True)

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
	model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)

	input_text = "Write me a poem about Machine Learning."
	input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

	outputs = model.generate(**input_ids)
	print(tokenizer.decode(outputs[0]))
	```


	#### Other optimizations

	* _Flash Attention 2_

	First make sure to install `flash-attn` in your environment `pip install flash-attn`

	```diff
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.float16,
	+ attn_implementation="flash_attention_2"
	).to(0)
	```

	### Inputs and outputs

	* Input: Text string, such as a question, a prompt, or a document to be
	summarized.
	* Output: Generated English-language text in response to the input, such
	as an answer to a question, or a summary of a document.

	## Model Data

	Data used for model training and how the data was processed.

	### Training Dataset

	These models were trained on a dataset of text data that includes a wide variety
	of sources, totaling 6 trillion tokens. Here are the key components:

	* Web Documents: A diverse collection of web text ensures the model is exposed
	to a broad range of linguistic styles, topics, and vocabulary. Primarily
	English-language content.
	* Code: Exposing the model to code helps it to learn the syntax and patterns of
	programming languages, which improves its ability to generate code or
	understand code-related questions.
	* Mathematics: Training on mathematical text helps the model learn logical
	reasoning, symbolic representation, and to address mathematical queries.

	The combination of these diverse data sources is crucial for training a powerful
	language model that can handle a wide variety of different tasks and text
	formats.

	### Data Preprocessing

	Here are the key data cleaning and filtering methods applied to the training
	data:

	* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
	applied at multiple stages in the data preparation process to ensure the
	exclusion of harmful and illegal content
	* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
	reliable, automated techniques were used to filter out certain personal
	information and other sensitive data from training sets.
	* Additional methods: Filtering based on content quality and safely in line with
	[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).

	## Implementation Information

	Details about the model internals.

	### Hardware

	Gemma was trained using the latest generation of
	[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).

	Training large language models requires significant computational power. TPUs,
	designed specifically for matrix operations common in machine learning, offer
	several advantages in this domain:

	* Performance: TPUs are specifically designed to handle the massive computations
	involved in training LLMs. They can speed up training considerably compared to
	CPUs.
	* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
	for the handling of large models and batch sizes during training. This can
	lead to better model quality.
	* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
	handling the growing complexity of large foundation models. You can distribute
	training across multiple TPU devices for faster and more efficient processing.
	* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
	solution for training large models compared to CPU-based infrastructure,
	especially when considering the time and resources saved due to faster
	training.
	* These advantages are aligned with
	[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).

	### Software

	Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture).

	JAX allows researchers to take advantage of the latest generation of hardware,
	including TPUs, for faster and more efficient training of large models.

	ML Pathways is Google's latest effort to build artificially intelligent systems
	capable of generalizing across multiple tasks. This is specially suitable for
	[foundation models](https://ai.google/discover/foundation-models/), including large language models like
	these ones.

	Together, JAX and ML Pathways are used as described in the
	[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
	controller' programming model of Jax and Pathways allows a single Python
	process to orchestrate the entire training run, dramatically simplifying the
	development workflow."

	## Evaluation

	Model evaluation metrics and results.

	### Benchmark Results

	These models were evaluated against a large collection of different datasets and
	metrics to cover different aspects of text generation:

	\| Benchmark \| Metric \| 2B Params \| 7B Params \|
	\| -- \| -- \| -- \| -- \| --- \|


	## Usage and Limitations

	These models have certain limitations that users should be aware of.

	### Intended Usage

	Open Large Language Models (LLMs) have a wide range of applications across
	various industries and domains. The following list of potential uses is not
	comprehensive. The purpose of this list is to provide contextual information
	about the possible use-cases that the model creators considered as part of model
	training and development.

	* Content Creation and Communication
	* Text Generation: These models can be used to generate creative text formats
	such as poems, scripts, code, marketing copy, and email drafts.
	* Chatbots and Conversational AI: Power conversational interfaces for customer
	service, virtual assistants, or interactive applications.
	* Text Summarization: Generate concise summaries of a text corpus, research
	papers, or reports.
	* Research and Education
	* Natural Language Processing (NLP) Research: These models can serve as a
	foundation for researchers to experiment with NLP techniques, develop
	algorithms, and contribute to the advancement of the field.
	* Language Learning Tools: Support interactive language learning experiences,
	aiding in grammar correction or providing writing practice.
	* Knowledge Exploration: Assist researchers in exploring large bodies of text
	by generating summaries or answering questions about specific topics.

	### Limitations

	* Training Data
	* The quality and diversity of the training data significantly influence the
	model's capabilities. Biases or gaps in the training data can lead to
	limitations in the model's responses.
	* The scope of the training dataset determines the subject areas the model can
	handle effectively.
	* Context and Task Complexity
	* LLMs are better at tasks that can be framed with clear prompts and
	instructions. Open-ended or highly complex tasks might be challenging.
	* A model's performance can be influenced by the amount of context provided
	(longer context generally leads to better outputs, up to a certain point).
	* Language Ambiguity and Nuance
	* Natural language is inherently complex. LLMs might struggle to grasp subtle
	nuances, sarcasm, or figurative language.
	* Factual Accuracy
	* LLMs generate responses based on information they learned from their
	training datasets, but they are not knowledge bases. They may generate
	incorrect or outdated factual statements.
	* Common Sense
	* LLMs rely on statistical patterns in language. They might lack the ability
	to apply common sense reasoning in certain situations.

	### Ethical Considerations and Risks

	The development of large language models (LLMs) raises several ethical concerns.
	In creating an open model, we have carefully considered the following:

	* Bias and Fairness
	* LLMs trained on large-scale, real-world text data can reflect socio-cultural
	biases embedded in the training material. These models underwent careful
	scrutiny, input data pre-processing described and posterior evaluations
	reported in this card.
	* Misinformation and Misuse
	* LLMs can be misused to generate text that is false, misleading, or harmful.
	* Guidelines are provided for responsible use with the model, see the
	[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
	* Transparency and Accountability:
	* This model card summarizes details on the models' architecture,
	capabilities, limitations, and evaluation processes.
	* A responsibly developed open model offers the opportunity to share
	innovation by making LLM technology accessible to developers and researchers
	across the AI ecosystem.

	Risks identified and mitigations:

	* Perpetuation of biases: It's encouraged to perform continuous monitoring
	(using evaluation metrics, human review) and the exploration of de-biasing
	techniques during model training, fine-tuning, and other use cases.
	* Generation of harmful content: Mechanisms and guidelines for content safety
	are essential. Developers are encouraged to exercise caution and implement
	appropriate content safety safeguards based on their specific product policies
	and application use cases.
	* Misuse for malicious purposes: Technical limitations and developer and
	end-user education can help mitigate against malicious applications of LLMs.
	Educational resources and reporting mechanisms for users to flag misuse are
	provided. Prohibited uses of Gemma models are outlined in the
	[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
	* Privacy violations: Models were trained on data filtered for removal of PII
	(Personally Identifiable Information). Developers are encouraged to adhere to
	privacy regulations with privacy-preserving techniques.

	### Benefits

	At the time of release, this family of models provides high-performance open
	large language model implementations designed from the ground up for Responsible
	AI development compared to similarly sized models.

	Using the benchmark evaluation metrics described in this document, these models
	have shown to provide superior performance to other, comparably-sized open model
	alternatives.

	<!-- original-model-card end -->