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README.md

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+---
+library_name: transformers
+license: gemma
+pipeline_tag: text-generation
+tags:
+- autoquant
+- gptq
+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

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generation_config.json

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