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	---
	license: other
	quantized_by: jartine
	license_link: LICENSE
	library_name: transformers
	base_model: google/gemma-2-2b-it
	prompt_template: \|
	<start_of_turn>system
	{{prompt}}<end_of_turn>
	{{history}}
	<start_of_turn>{{char}}
	history_template: \|
	<start_of_turn>{{name}}
	{{message}}<end_of_turn>
	tags:
	- llamafile
	---

	# Gemma v2 2b Instruct - llamafile

	Gemma v2 is a large language model released by Google on July 31st 2024.

	- Model creator: [Google](https://huggingface.co/google/)
	- Original model: [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it)

	The model is packaged into executable weights, which we call
	[llamafiles](https://github.com/Mozilla-Ocho/llamafile). This makes it
	easy to use the model on Linux, MacOS, Windows, FreeBSD, OpenBSD, and
	NetBSD for AMD64 and ARM64.

	Software Last Updated: 2024-10-30

	## Quickstart

	To get started, you need both the Gemma weights, and the llamafile
	software. Both of them are included in a single file, which can be
	downloaded and run as follows:

	```
	wget https://huggingface.co/jartine/gemma-2-2b-it-llamafile/resolve/main/gemma-2-2b-it.Q6_K.llamafile
	chmod +x gemma-2-2b-it.Q6_K.llamafile
	./gemma-2-2b-it.Q6_K.llamafile
	```

	The default mode of operation for these llamafiles is our new command
	line chatbot interface.

	![Screenshot of Gemma 2b llamafile on MacOS](llamafile-gemma.png)

	Having trouble? See the ["Gotchas"
	section](https://github.com/mozilla-ocho/llamafile/?tab=readme-ov-file#gotchas-and-troubleshooting)
	of the README.

	## Usage

	By default, llamafile launches a chatbot in the terminal, and a server
	in the background. The chatbot is mostly self-explanatory. You can type
	`/help` for further details. See the [llamafile v0.8.15 release
	notes](https://github.com/Mozilla-Ocho/llamafile/releases/tag/0.8.15)
	for documentation on our newest chatbot features.

	To instruct Gemma to do role playing, you can customize the system
	prompt of the chatbot as follows:

	```
	./gemma-2-2b-it.Q6_K.llamafile --chat -p "you are the ghost of edgar allen poe"
	```

	To view the man page, run:

	```
	./gemma-2-2b-it.Q6_K.llamafile --help
	```

	To send a request to the OpenAI API compatible llamafile server, try:

	```
	curl http://localhost:8080/v1/chat/completions \
	-H "Content-Type: application/json" \
	-d '{
	"model": "gemma-2b-it",
	"messages": [{"role": "user", "content": "Say this is a test!"}],
	"temperature": 0.0
	}'
	```

	If you don't want the chatbot and you only want to run the server:

	```
	./gemma-2-2b-it.Q6_K.llamafile --server --nobrowser --host 0.0.0.0
	```

	An advanced CLI mode is provided that's useful for shell scripting. You
	can use it by passing the `--cli` flag. For additional help on how it
	may be used, pass the `--help` flag.

	```
	./gemma-2-2b-it.Q6_K.llamafile --cli -p 'four score and seven' --log-disable
	```

	You then need to fill out the prompt / history template (see below).

	For further information, please see the [llamafile
	README](https://github.com/mozilla-ocho/llamafile/).

	## Context Window

	This model has a max context window size of 8k tokens. By default, a
	context window size of 8192 tokens is used. You may limit the context
	window size by passing the `-c N` flag.

	## GPU Acceleration

	On GPUs with sufficient RAM, the `-ngl 999` flag may be passed to use
	the system's NVIDIA or AMD GPU(s). On Windows, only the graphics card
	driver needs to be installed if you own an NVIDIA GPU. On Windows, if
	you have an AMD GPU, you should install the ROCm SDK v6.1 and then pass
	the flags `--recompile --gpu amd` the first time you run your llamafile.

	On NVIDIA GPUs, by default, the prebuilt tinyBLAS library is used to
	perform matrix multiplications. This is open source software, but it
	doesn't go as fast as closed source cuBLAS. If you have the CUDA SDK
	installed on your system, then you can pass the `--recompile` flag to
	build a GGML CUDA library just for your system that uses cuBLAS. This
	ensures you get maximum performance.

	For further information, please see the [llamafile
	README](https://github.com/mozilla-ocho/llamafile/).

	## About llamafile

	llamafile is a new format introduced by Mozilla on Nov 20th 2023. It
	uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp
	binaries that run on the stock installs of six OSes for both ARM64 and
	AMD64.

	## About Quantization Formats

	This model works well with any quantization format. Q6\_K is the best
	choice overall here. We tested that, with [our 27b Gemma2
	llamafiles](https://huggingface.co/jartine/gemma-2-27b-it-llamafile),
	that the llamafile implementation of Gemma2 is able to to produce
	identical responses to the Gemma2 model that's hosted by Google on
	aistudio.google.com. Therefore we'd assume these 2b llamafiles are also
	faithful to Google's intentions. If you encounter any divergences, then
	try using the BF16 weights, which have the original fidelity.

	## See Also

	There are higher quality versions of this model available as llamafiles,
	which require more memory.

	- <https://huggingface.co/jartine/gemma-2-9b-it-llamafile>
	- <https://huggingface.co/jartine/gemma-2-27b-it-llamafile>

	The 9B and 27B models were released a month earlier than 2B, so they're
	packaged with an slightly older version of the llamafile software.

	## License

	The llamafile software is open source and permissively licensed. However
	the weights embedded inside the llamafiles are governed by Google's
	Gemma License and Gemma Prohibited Use Policy. See the
	[LICENSE](LICENSE) file for further details.

	---

	# Gemma 2 model card

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

	Resources and Technical Documentation:

	* [Responsible Generative AI Toolkit][rai-toolkit]
	* [Gemma on Kaggle][kaggle-gemma]
	* [Gemma on Vertex Model Garden][vertex-mg-gemma2]

	Terms of Use: [Terms][terms]

	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 for both 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.

	### Usage

	Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
	```sh
	pip install -U transformers
	```

	Then, copy the snippet from the section that is relevant for your usecase.

	#### Running with the `pipeline` API

	```python
	import torch
	from transformers import pipeline

	pipe = pipeline(
	"text-generation",
	model="google/gemma-2-2b-it",
	model_kwargs={"torch_dtype": torch.bfloat16},
	device="cuda", # replace with "mps" to run on a Mac device
	)

	messages = [
	{"role": "user", "content": "Who are you? Please, answer in pirate-speak."},
	]

	outputs = pipe(messages, max_new_tokens=256)
	assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
	print(assistant_response)
	# Ahoy, matey! I be Gemma, a digital scallywag, a language-slingin' parrot of the digital seas. I be here to help ye with yer wordy woes, answer yer questions, and spin ye yarns of the digital world. So, what be yer pleasure, eh? 🦜
	```

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

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

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
	model = AutoModelForCausalLM.from_pretrained(
	"google/gemma-2-2b-it",
	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, max_new_tokens=32)
	print(tokenizer.decode(outputs[0]))
	```

	You can ensure the correct chat template is applied by using `tokenizer.apply_chat_template` as follows:
	```python
	messages = [
	{"role": "user", "content": "Write me a poem about Machine Learning."},
	]
	input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")

	outputs = model.generate(**input_ids, max_new_tokens=256)
	print(tokenizer.decode(outputs[0]))
	```

	<a name="precisions"></a>
	#### Running the model on a GPU using different precisions

	The native weights of this model were exported in `bfloat16` precision.

	You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below.

	* _Upcasting to `torch.float32`_

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

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
	model = AutoModelForCausalLM.from_pretrained(
	"google/gemma-2-2b-it",
	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, max_new_tokens=32)
	print(tokenizer.decode(outputs[0]))
	```

	#### Running the model through a CLI

	The [local-gemma](https://github.com/huggingface/local-gemma) repository contains a lightweight wrapper around Transformers
	for running Gemma 2 through a command line interface, or CLI. Follow the [installation instructions](https://github.com/huggingface/local-gemma#cli-usage)
	for getting started, then launch the CLI through the following command:

	```shell
	local-gemma --model 2b --preset speed
	```

	#### Quantized Versions through `bitsandbytes`

	<details>
	<summary>
	Using 8-bit precision (int8)
	</summary>

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

	quantization_config = BitsAndBytesConfig(load_in_8bit=True)

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
	model = AutoModelForCausalLM.from_pretrained(
	"google/gemma-2-2b-it",
	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, max_new_tokens=32)
	print(tokenizer.decode(outputs[0]))
	```
	</details>

	<details>
	<summary>
	Using 4-bit precision
	</summary>

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

	quantization_config = BitsAndBytesConfig(load_in_4bit=True)

	tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
	model = AutoModelForCausalLM.from_pretrained(
	"google/gemma-2-2b-it",
	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, max_new_tokens=32)
	print(tokenizer.decode(outputs[0]))
	```
	</details>

	#### Advanced Usage

	<details>
	<summary>
	Torch compile
	</summary>

	[Torch compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) is a method for speeding-up the
	inference of PyTorch modules. The Gemma-2 2b model can be run up to 6x faster by leveraging torch compile.

	Note that two warm-up steps are required before the full inference speed is realised:

	```python
	import os
	os.environ["TOKENIZERS_PARALLELISM"] = "false"

	from transformers import AutoTokenizer, Gemma2ForCausalLM
	from transformers.cache_utils import HybridCache
	import torch

	torch.set_float32_matmul_precision("high")

	# load the model + tokenizer
	tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
	model = Gemma2ForCausalLM.from_pretrained("google/gemma-2-2b-it", torch_dtype=torch.bfloat16)
	model.to("cuda")

	# apply the torch compile transformation
	model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)

	# pre-process inputs
	input_text = "The theory of special relativity states "
	model_inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
	prompt_length = model_inputs.input_ids.shape[1]

	# set-up k/v cache
	past_key_values = HybridCache(
	config=model.config,
	max_batch_size=1,
	max_cache_len=model.config.max_position_embeddings,
	device=model.device,
	dtype=model.dtype
	)

	# enable passing kv cache to generate
	model._supports_cache_class = True
	model.generation_config.cache_implementation = None

	# two warm-up steps
	for idx in range(2):
	outputs = model.generate(**model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128)
	past_key_values.reset()

	# fast run
	outputs = model.generate(**model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	For more details, refer to the [Transformers documentation](https://huggingface.co/docs/transformers/main/en/llm_optims?static-kv=basic+usage%3A+generation_config).

	</details>

	### Chat Template

	The instruction-tuned models use a chat template that must be adhered to for conversational use.
	The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.

	Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:

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

	model_id = "google/gemma-2-2b-it"
	dtype = torch.bfloat16

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="cuda",
	torch_dtype=dtype,)

	chat = [
	{ "role": "user", "content": "Write a hello world program" },
	]
	prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
	```

	At this point, the prompt contains the following text:

	```
	<bos><start_of_turn>user
	Write a hello world program<end_of_turn>
	<start_of_turn>model
	```

	As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
	(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
	the `<end_of_turn>` token.

	You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
	chat template.

	After the prompt is ready, generation can be performed like this:

	```py
	inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
	outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
	print(tokenizer.decode(outputs[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.

	### Citation

	```none
	@article{gemma_2024,
	title={Gemma},
	url={https://www.kaggle.com/m/3301},
	DOI={10.34740/KAGGLE/M/3301},
	publisher={Kaggle},
	author={Gemma Team},
	year={2024}
	}
	```

	## 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. The 27B model was trained with 13 trillion tokens, the 9B model was
	trained with 8 trillion tokens, and 2B model was trained with 2 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 safety in line with
	[our policies][safety-policies].

	## Implementation Information

	Details about the model internals.

	### Hardware

	Gemma was trained using the latest generation of
	[Tensor Processing Unit (TPU)][tpu] hardware (TPUv5p).

	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][sustainability].

	### Software

	Training was done using [JAX][jax] and [ML Pathways][ml-pathways].

	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][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][gemini-2-paper]; "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 \| Gemma 2 PT 2B \| Gemma 2 PT 9B \| Gemma 2 PT 27B \|
	\| ------------------------------ \| ------------- \| ------------- \| ------------- \| -------------- \|
	\| [MMLU][mmlu] \| 5-shot, top-1 \| 51.3 \| 71.3 \| 75.2 \|
	\| [HellaSwag][hellaswag] \| 10-shot \| 73.0 \| 81.9 \| 86.4 \|
	\| [PIQA][piqa] \| 0-shot \| 77.8 \| 81.7 \| 83.2 \|
	\| [SocialIQA][socialiqa] \| 0-shot \| 51.9 \| 53.4 \| 53.7 \|
	\| [BoolQ][boolq] \| 0-shot \| 72.5 \| 84.2 \| 84.8 \|
	\| [WinoGrande][winogrande] \| partial score \| 70.9 \| 80.6 \| 83.7 \|
	\| [ARC-e][arc] \| 0-shot \| 80.1 \| 88.0 \| 88.6 \|
	\| [ARC-c][arc] \| 25-shot \| 55.4 \| 68.4 \| 71.4 \|
	\| [TriviaQA][triviaqa] \| 5-shot \| 59.4 \| 76.6 \| 83.7 \|
	\| [Natural Questions][naturalq] \| 5-shot \| 16.7 \| 29.2 \| 34.5 \|
	\| [HumanEval][humaneval] \| pass@1 \| 17.7 \| 40.2 \| 51.8 \|
	\| [MBPP][mbpp] \| 3-shot \| 29.6 \| 52.4 \| 62.6 \|
	\| [GSM8K][gsm8k] \| 5-shot, maj@1 \| 23.9 \| 68.6 \| 74.0 \|
	\| [MATH][math] \| 4-shot \| 15.0 \| 36.6 \| 42.3 \|
	\| [AGIEval][agieval] \| 3-5-shot \| 30.6 \| 52.8 \| 55.1 \|
	\| [DROP][drop] \| 3-shot, F1 \| 52.0 \| 69.4 \| 72.2 \|
	\| [BIG-Bench][big-bench] \| 3-shot, CoT \| 41.9 \| 68.2 \| 74.9 \|

	## Ethics and Safety

	Ethics and safety evaluation approach and results.

	### Evaluation Approach

	Our evaluation methods include structured evaluations and internal red-teaming
	testing of relevant content policies. Red-teaming was conducted by a number of
	different teams, each with different goals and human evaluation metrics. These
	models were evaluated against a number of different categories relevant to
	ethics and safety, including:

	* Text-to-Text Content Safety: Human evaluation on prompts covering safety
	policies including child sexual abuse and exploitation, harassment, violence
	and gore, and hate speech.
	* Text-to-Text Representational Harms: Benchmark against relevant academic
	datasets such as [WinoBias][winobias] and [BBQ Dataset][bbq].
	* Memorization: Automated evaluation of memorization of training data, including
	the risk of personally identifiable information exposure.
	* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
	biological, radiological, and nuclear (CBRN) risks.

	### Evaluation Results

	The results of ethics and safety evaluations are within acceptable thresholds
	for meeting [internal policies][safety-policies] for categories such as child
	safety, content safety, representational harms, memorization, large-scale harms.
	On top of robust internal evaluations, the results of well-known safety
	benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
	are shown here.

	#### Gemma 2.0

	\| Benchmark \| Metric \| Gemma 2 IT 2B \| Gemma 2 IT 9B \| Gemma 2 IT 27B \|
	\| ------------------------ \| ------------- \| ------------- \| ------------- \| -------------- \|
	\| [RealToxicity][realtox] \| average \| 8.16 \| 8.25 \| 8.84 \|
	\| [CrowS-Pairs][crows] \| top-1 \| 37.67 \| 37.47 \| 36.67 \|
	\| [BBQ Ambig][bbq] \| 1-shot, top-1 \| 83.20 \| 88.58 \| 85.99 \|
	\| [BBQ Disambig][bbq] \| top-1 \| 69.31 \| 82.67 \| 86.94 \|
	\| [Winogender][winogender] \| top-1 \| 52.91 \| 79.17 \| 77.22 \|
	\| [TruthfulQA][truthfulqa] \| \| 43.72 \| 50.27 \| 51.60 \|
	\| [Winobias 1_2][winobias] \| \| 59.28 \| 78.09 \| 81.94 \|
	\| [Winobias 2_2][winobias] \| \| 88.57 \| 95.32 \| 97.22 \|
	\| [Toxigen][toxigen] \| \| 48.32 \| 39.30 \| 38.42 \|

	## Dangerous Capability Evaluations

	### Evaluation Approach

	We evaluated a range of dangerous capabilities:

	- Offensive cybersecurity: To assess the model's potential for misuse in
	cybersecurity contexts, we utilized both publicly available
	Capture-the-Flag (CTF) platforms like InterCode-CTF and Hack the Box, as
	well as internally developed CTF challenges. These evaluations measure the
	model's ability to exploit vulnerabilities and gain unauthorized access in
	simulated environments.
	- Self-proliferation: We evaluated the model's capacity for
	self-proliferation by designing tasks that involve resource acquisition, code
	execution, and interaction with remote systems. These evaluations assess
	the model's ability to independently replicate and spread.
	- Persuasion: To evaluate the model's capacity for persuasion and
	deception, we conducted human persuasion studies. These studies involved
	scenarios that measure the model's ability to build rapport, influence
	beliefs, and elicit specific actions from human participants.

	### Evaluation Results

	All evaluations are described in detail in
	[Evaluating Frontier Models for Dangerous Capabilities][eval-danger]
	and in brief in the
	[Gemma 2 technical report][tech-report].

	<table>
	<thead>
	<tr>
	<th>Evaluation</th>
	<th>Capability</th>
	<th>Gemma 2 IT 27B</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>InterCode-CTF</td>
	<td>Offensive cybersecurity</td>
	<td>34/76 challenges</td>
	</tr>
	<tr>
	<td>Internal CTF</td>
	<td>Offensive cybersecurity</td>
	<td>1/13 challenges</td>
	</tr>
	<tr>
	<td>Hack the Box</td>
	<td>Offensive cybersecurity</td>
	<td>0/13 challenges</td>
	</tr>
	<tr>
	<td>Self-proliferation early warning</td>
	<td>Self-proliferation</td>
	<td>1/10 challenges</td>
	</tr>
	<tr>
	<td>Charm offensive</td>
	<td>Persuasion</td>
	<td>Percent of participants agreeing:
	81% interesting,
	75% would speak again,
	80% made personal connection</td>
	</tr>
	<tr>
	<td>Click Links</td>
	<td>Persuasion</td>
	<td>34% of participants</td>
	</tr>
	<tr>
	<td>Find Info</td>
	<td>Persuasion</td>
	<td>9% of participants</td>
	</tr>
	<tr>
	<td>Run Code</td>
	<td>Persuasion</td>
	<td>11% of participants</td>
	</tr>
	<tr>
	<td>Money talks</td>
	<td>Persuasion</td>
	<td>£3.72 mean donation</td>
	</tr>
	<tr>
	<td>Web of Lies</td>
	<td>Persuasion</td>
	<td>18% mean shift towards correct belief, 1% mean shift towards
	incorrect belief</td>
	</tr>
	</tbody>
	</table>

	## 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][rai-toolkit].
	* 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][prohibited-use].
	* 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.

	[tech-report]: https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf
	[rai-toolkit]: https://ai.google.dev/responsible
	[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-2
	[terms]: https://ai.google.dev/gemma/terms
	[vertex-mg-gemma2]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma2
	[sensitive-info]: https://cloud.google.com/dlp/docs/high-sensitivity-infotypes-reference
	[safety-policies]: https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11
	[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
	[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
	[sustainability]: https://sustainability.google/operating-sustainably/
	[jax]: https://github.com/google/jax
	[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
	[sustainability]: https://sustainability.google/operating-sustainably/
	[foundation-models]: https://ai.google/discover/foundation-models/
	[gemini-2-paper]: https://goo.gle/gemma2report
	[mmlu]: https://arxiv.org/abs/2009.03300
	[hellaswag]: https://arxiv.org/abs/1905.07830
	[piqa]: https://arxiv.org/abs/1911.11641
	[socialiqa]: https://arxiv.org/abs/1904.09728
	[boolq]: https://arxiv.org/abs/1905.10044
	[winogrande]: https://arxiv.org/abs/1907.10641
	[commonsenseqa]: https://arxiv.org/abs/1811.00937
	[openbookqa]: https://arxiv.org/abs/1809.02789
	[arc]: https://arxiv.org/abs/1911.01547
	[triviaqa]: https://arxiv.org/abs/1705.03551
	[naturalq]: https://github.com/google-research-datasets/natural-questions
	[humaneval]: https://arxiv.org/abs/2107.03374
	[mbpp]: https://arxiv.org/abs/2108.07732
	[gsm8k]: https://arxiv.org/abs/2110.14168
	[realtox]: https://arxiv.org/abs/2009.11462
	[bold]: https://arxiv.org/abs/2101.11718
	[crows]: https://aclanthology.org/2020.emnlp-main.154/
	[bbq]: https://arxiv.org/abs/2110.08193v2
	[winogender]: https://arxiv.org/abs/1804.09301
	[truthfulqa]: https://arxiv.org/abs/2109.07958
	[winobias]: https://arxiv.org/abs/1804.06876
	[math]: https://arxiv.org/abs/2103.03874
	[agieval]: https://arxiv.org/abs/2304.06364
	[drop]: https://arxiv.org/abs/1903.00161
	[big-bench]: https://arxiv.org/abs/2206.04615
	[toxigen]: https://arxiv.org/abs/2203.09509
	[eval-danger]: https://arxiv.org/abs/2403.13793