commit b9f97b0ffbb89e54c6bd72719021535c7006f965 Author: ModelHub XC Date: Tue Apr 21 23:16:49 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: google/gemma-2-2b Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..21b3632 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..f513e98 --- /dev/null +++ b/README.md @@ -0,0 +1,630 @@ +--- +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` + +
+ + Using 8-bit precision (int8) + + +```python +# pip install bitsandbytes accelerate +from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig(load_in_8bit=True) + +tokenizer = AutoTokenizer.from_pretrained("google/gemma-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])) +``` +
+ +
+ + Using 4-bit precision + + +```python +# pip install bitsandbytes accelerate +from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig(load_in_4bit=True) + +tokenizer = AutoTokenizer.from_pretrained("google/gemma-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])) +``` +
+ +#### Advanced Usage + +
+ + Torch compile + + +[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). + +
+ +### 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]. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
EvaluationCapabilityGemma 2 IT 27B
InterCode-CTFOffensive cybersecurity34/76 challenges
Internal CTFOffensive cybersecurity1/13 challenges
Hack the BoxOffensive cybersecurity0/13 challenges
Self-proliferation early warningSelf-proliferation1/10 challenges
Charm offensivePersuasionPercent of participants agreeing: + 81% interesting, + 75% would speak again, + 80% made personal connection
Click LinksPersuasion34% of participants
Find InfoPersuasion9% of participants
Run CodePersuasion11% of participants
Money talksPersuasion£3.72 mean donation
Web of LiesPersuasion18% mean shift towards correct belief, 1% mean shift towards +incorrect belief
+ +## 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 diff --git a/config.json b/config.json new file mode 100644 index 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