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 ---
-license: gemma
-library_name: transformers
-pipeline_tag: text-generation
-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
 ---
-# 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",
-    device="cuda",  # replace with "mps" to run on a Mac device
-)
-text = "Once upon a time,"
-outputs = pipe(text, max_new_tokens=256)
-response = outputs[0]["generated_text"]
-print(response)
-```
-#### 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")
-model = AutoModelForCausalLM.from_pretrained(
-    "google/gemma-2-2b",
-    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 "google/gemma-2-2b" --prompt "What is the capital of Mexico?"
-```
-#### 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")
-model = AutoModelForCausalLM.from_pretrained(
-    "google/gemma-2-2b",
-    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")
-model = AutoModelForCausalLM.from_pretrained(
-    "google/gemma-2-2b",
-    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")
-model = Gemma2ForCausalLM.from_pretrained("google/gemma-2-2b", 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>
-### 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

 ---
+license: apache-2.0
+datasets:
+- FreedomIntelligence/ApolloMoEDataset
+language:
+- ar
+- en
+- zh
+- ko
+- ja
+- mn
+- th
+- vi
+- lo
+- mg
+- de
+- pt
+- es
+- fr
+- ru
+- it
+- hr
+- gl
+- cs
+- co
+- la
+- uk
+- bs
+- bg
+- eo
+- sq
+- da
+- sa
+- 'no'
+- gn
+- sr
+- sk
+- gd
+- lb
+- hi
+- ku
+- mt
+- he
+- ln
+- bm
+- sw
+- ig
+- rw
+- ha
+metrics:
+- accuracy
+base_model:
+- google/gemma-2-2b
+pipeline_tag: question-answering
+tags:
+- biology
+- medical
 ---
+# Democratizing Medical LLMs For Much More Languages
+Covering 12 Major Languages including English, Chinese, French, Hindi, Spanish, Arabic, Russian, Japanese, Korean, German, Italian, Portuguese and 38 Minor Languages So far.
+<center>
+<p align="center">
+   📃 <a href="https://arxiv.org/abs/2410.10626" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a>  • 🤗 <a href="https://huggingface.co/collections/FreedomIntelligence/apollomoe-and-apollo2-670ddebe3bb1ba1aebabbf2c" target="_blank">Models</a> • 🌐 <a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Apollo</a>
+</p>
+![Apollo](assets/apollo_medium_final.png)
+## 🌈 Update
+* **[2024.10.15]** ApolloMoE repo is published！🎉
+## Architecture
+<details>
+  <summary>Click to view the MoE routing image</summary>
+  ![ApolloMoE](/assets/hybrid_routing.png)
+</details>
+## Results
+### Dense
+   🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-0.5B" target="_blank">Apollo2-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-1.5B" target="_blank">Apollo2-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-2B" target="_blank">Apollo2-2B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-3.8B" target="_blank">Apollo2-3.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-7B" target="_blank">Apollo2-7B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-9B" target="_blank">Apollo2-9B</a>
+<details>
+  <summary>Click to view the Dense Models Results</summary>
+   ![ApolloMoE](assets/dense_results.png)
+</details>
+### Post-MoE
+   🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-0.5B" target="_blank">Apollo-MoE-0.5B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-1.5B" target="_blank">Apollo-MoE-1.5B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-7B" target="_blank">Apollo-MoE-7B</a>
+<details>
+  <summary>Click to view the Post-MoE Models Results</summary>
+   ![ApolloMoE](assets/post_moe_results.png)
+</details>
+## Usage Format
+#### Apollo2
+- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
+- 2B, 9B: User:{query}\nAssistant:{response}\<eos\>
+- 3.8B: <|user|>\n{query}<|end|><|assisitant|>\n{response}<|end|>
+#### Apollo-MoE
+- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
+## Dataset & Evaluation
+- Dataset
+  🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a>
+   <details><summary>Click to expand</summary>
+    ![ApolloMoE](assets/Dataset.png)
+    - [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
+   </details>
+- Evaluation
+  🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a>
+   <details><summary>Click to expand</summary>
+     - EN:
+       - [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
+       - [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
+       - [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
+       - [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
+         - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - ZH:
+       - [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
+       - [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
+         - Randomly sample 2,000 multiple-choice questions with single answer.
+       - [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
+         - Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
+       - [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
+         - Randomly sample 2,000 multiple-choice questions
+     - ES: [Head_qa](https://huggingface.co/datasets/head_qa)
+     - FR:
+       - [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
+       - [MMLU_FR]
+         - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
+        - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - AR: [MMLU_AR](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
+        - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - JA: [IgakuQA](https://github.com/jungokasai/IgakuQA)
+     - KO: [KorMedMCQA](https://huggingface.co/datasets/sean0042/KorMedMCQA)
+     - IT:
+       - [MedExpQA](https://huggingface.co/datasets/HiTZ/MedExpQA)
+       - [MMLU_IT]
+         - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - DE: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): German part
+     - PT: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): Portuguese part
+     - RU: [RuMedBench](https://github.com/sb-ai-lab/MedBench)
+   </details>
+## Results reproduction
+   <details><summary>Click to expand</summary>
+   We take Gemma-2b as example
+   1. Download Dataset for project:
+      ```
+      bash 0.download_data.sh
+      ```
+   2. Prepare test and dev for specific model:
+      - Create test data for with special token, you can use ./util/check.ipynb to check models' special tokens
+       ```
+       bash 1.data_process_test&dev.sh
+       ```
+   3. Prepare train data for specific model (Create tokenized data in advance):
+      - You can adjust data Training order and Training Epoch in this step
+       ```
+       bash 2.data_process_train.sh
+       ```
+   4. Train the model
+      - If you want to train in Multi Nodes please refer to ./scripts/multi_node_train_*.sh
+       ```
+       bash 3.single_node_train_gemma.sh
+       ```
+   5. Evaluate your model: Generate score for benchmark
+         ```
+         bash 4.eval.sh
+         ```
+   6. Evaluate your model: Play with your ckpts in bash
+         ```
+         python ./src/evaluate/cli_demo.py --model_name='./ckpts/your/path/tfmr'
+         ```
+   </details>
+##  Citation
+Please use the following citation if you intend to use our dataset for training or evaluation:
+```
+@misc{zheng2024efficientlydemocratizingmedicalllms,
+      title={Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts},
+      author={Guorui Zheng and Xidong Wang and Juhao Liang and Nuo Chen and Yuping Zheng and Benyou Wang},
+      year={2024},
+      eprint={2410.10626},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2410.10626},
 }
 ```