opt-66b
Maintainer: facebook
175
📊
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The opt-66b model is a large language model developed by Facebook AI. It is part of the Open Pre-trained Transformers (OPT) suite of models, which range from 125M to 175B parameters. The opt-66b model was trained on a large corpus of English text with the goal of enabling reproducible and responsible AI research at scale.
The opt-66b model is similar in size and performance to the GPT-3 class of models, but applies the latest best practices in data collection and efficient training. Like GPT-3, it is a decoder-only transformer model trained using a causal language modeling (CLM) objective. However, the key distinction is that the OPT models, including opt-66b, are openly and responsibly shared with the research community, in contrast to the more restricted access to GPT-3.
Model inputs and outputs
Inputs
- Raw text in English
Outputs
- Predicted next token in the input sequence, given the preceding context
Capabilities
The opt-66b model can be used for a variety of natural language processing tasks, such as text generation, language modeling, and few-shot learning. It has shown impressive performance on benchmarks like LAMBADA and COPA, matching or exceeding the capabilities of GPT-3.
What can I use it for?
The opt-66b model is primarily intended for AI researchers and practitioners to study the behaviors, capabilities, biases, and constraints of large language models. By openly sharing these models, the goal is to enable more voices to participate in understanding the impact of such models on society.
Some potential use cases for the opt-66b model include:
- Text generation and creative writing assistance
- Conversational agents and chatbots
- Language understanding and analysis
However, it's important to note that the model reflects the biases inherent in its training data, so care must be taken when deploying it in applications that interact with humans.
Things to try
One interesting aspect of the opt-66b model is its ability to perform zero-shot and few-shot learning on a variety of tasks. Researchers can explore the model's performance on different datasets and prompts to better understand its capabilities and limitations. Additionally, analyzing the model's outputs for potential biases or safety issues can provide valuable insights for improving large language models.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🖼️
opt-6.7b
97
The opt-6.7b model is part of the Open Pretrained Transformer (OPT) suite of decoder-only pre-trained language models introduced by Meta AI in the Open Pre-trained Transformer Language Models paper. The OPT models range in size from 125M to 175B parameters and are designed to match the performance of the GPT-3 class of models, while applying best practices in data collection and efficient training. The goal is to enable reproducible and responsible research at scale by making these large language models more widely available to the research community. The opt-6.7b model was predominantly pre-trained on English text, with a small amount of non-English data present via the CommonCrawl dataset. It was trained using a causal language modeling (CLM) objective, making it a member of the same decoder-only family as GPT-3. For evaluation, the model follows the same prompts and experimental setup as GPT-3. Similar OPT models include the opt-66b, opt-30b, opt-1.3b, and opt-350m models, all of which share the core architecture and training approach. Model inputs and outputs Inputs Text prompts of up to 2048 tokens, using the GPT-2 byte-level Byte Pair Encoding (BPE) tokenizer Outputs Continuation of the input text, generated in an autoregressive manner one token at a time Capabilities The opt-6.7b model can be used for a variety of natural language generation tasks, such as story writing, dialogue generation, and question answering. It has shown strong performance on benchmarks like GPT-3, demonstrating its ability to produce coherent and contextually relevant text. However, as with other large language models, it can also exhibit biases and safety issues due to the nature of its training data. What can I use it for? The opt-6.7b model can be used for a range of text generation tasks, from creative writing to chatbots and virtual assistants. Researchers can also use it as a starting point for fine-tuning on specific downstream tasks, leveraging its strong pre-training on a large corpus of text. Companies may find it useful for generating product descriptions, social media content, or other business-related text, though caution should be exercised due to the potential biases present in the model. Things to try One interesting aspect of the opt-6.7b model is its ability to generate text in a wide variety of styles and genres, thanks to the diversity of its training data. Experiment with different prompts and see how the model responds - you may be surprised by its ability to adapt to topics ranging from fiction to technical writing. Additionally, try applying techniques like top-k sampling to generate more diverse and creative outputs, while being mindful of the model's potential biases.
Updated Invalid Date
❗
opt-30b
133
The opt-30b model is a large open-pretrained transformer language model developed by Facebook. It is part of the Open Pre-trained Transformer (OPT) suite, which ranges from 125M to 175B parameters. The opt-30b model was trained to roughly match the performance and sizes of the GPT-3 class of models, while applying the latest best practices in data collection and efficient training. This aims to enable reproducible and responsible research at scale, and bring more voices to the study of the impact of large language models. The OPT models, including opt-30b, are decoder-only models similar to GPT-3. They were predominantly pretrained on English text, with a small amount of non-English data from CommonCrawl. The models were trained using a causal language modeling (CLM) objective. Model inputs and outputs Inputs Text prompts that the model can continue or generate from, similar to GPT-3. Outputs Continued text that the model generates based on the input prompt. Capabilities The opt-30b model is capable of generating coherent and fluent text continuations based on the provided prompts. It exhibits strong language modeling abilities, allowing it to understand context and produce relevant and grammatically correct outputs. The model can be used for a variety of text generation tasks, such as story writing, dialogue systems, and content creation. What can I use it for? The opt-30b model, like other large language models, can be used for a wide range of text-based tasks. Some potential use cases include: Content Generation**: The model can be used to generate news articles, blog posts, product descriptions, and other types of written content. Dialogue Systems**: The model can be fine-tuned to engage in more natural conversations, making it useful for chatbots and virtual assistants. Creative Writing**: The model can be used to assist in the creative writing process, helping to generate ideas, plot points, and even entire stories. Summarization**: The model can be used to summarize long passages of text, extracting the key points and ideas. Things to try One interesting aspect of the opt-30b model is its potential to generate diverse and creative text outputs. By providing the model with different types of prompts, you can explore its ability to adapt to various writing styles and genres. For example, you could try giving it prompts that start with a particular narrative voice or tone, and see how the model continues the story. Alternatively, you could provide the model with abstract or conceptual prompts and observe the ideas and associations it generates. Another avenue to explore is the model's ability to maintain coherence and logical reasoning over long-form text generation. By giving the model prompts that require sustained narrative or argumentation, you can assess its capacity for maintaining a consistent and compelling storyline or line of reasoning.
Updated Invalid Date
🎲
opt-1.3b
137
opt-1.3b is a large language model released by Meta AI as part of their Open Pre-trained Transformer (OPT) suite of models. Like the GPT-3 family of models, opt-1.3b is a decoder-only transformer model trained using self-supervised causal language modeling. The model was pretrained on a diverse corpus of 180B tokens, including web pages, books, and other online text. The opt-1.3b model is one of several OPT models ranging from 125M to 175B parameters, all of which Meta AI aims to share responsibly with researchers. This open access is intended to enable more voices to study the impact and improve upon these large language models, which can exhibit biases and limitations due to the nature of their training data. Similar OPT models include the larger opt-30b and opt-66b versions. The blip2-opt-2.7b model also leverages the OPT architecture, combining it with CLIP-like image encoding for multimodal applications. Model inputs and outputs Inputs Text prompt**: The model takes in a text prompt as input, which it uses to generate additional text in an autoregressive manner. Outputs Generated text**: The model outputs a sequence of generated text, continuing from the provided prompt. The length and content of the generated text can be controlled through various sampling parameters. Capabilities The opt-1.3b model is capable of open-ended text generation, allowing users to explore a wide range of applications such as creative writing, chatbots, and language-based assistants. However, as with other large language models, the outputs can exhibit biases and inconsistencies due to the nature of the training data. What can I use it for? The opt-1.3b model can be used for a variety of language-based tasks, including: Content generation**: Generating blog posts, news articles, stories, and other types of text content. Chatbots and conversational agents**: Building conversational interfaces that can engage in natural language interactions. Prompt engineering**: Exploring different prompting strategies to elicit desired outputs from the model. Fine-tuning**: Further training the model on specific datasets or tasks to adapt its capabilities. Researchers can also use the opt-1.3b model to study the behavior and limitations of large language models, as part of Meta AI's effort to enable responsible and reproducible research in this field. Things to try One interesting aspect of the opt-1.3b model is its ability to generate text that can exhibit biases and stereotypes present in its training data. By experimenting with different prompts, users can uncover these biases and explore ways to mitigate them, either through prompting strategies or further fine-tuning. This can provide valuable insights into the challenges of developing fair and inclusive language models. Additionally, the model's open-ended text generation capabilities can be used to explore creative writing and storytelling. Users can try generating narratives, dialogues, and other imaginative content, and then analyze the model's outputs to better understand its strengths and limitations in this domain.
Updated Invalid Date
🧠
opt-2.7b
75
The opt-2.7b model is part of the Open Pre-trained Transformers (OPT) suite of decoder-only pre-trained transformer language models developed by Meta AI. The OPT models range in size from 125M to 175B parameters and aim to match the performance of the GPT-3 class of models while applying best practices in data collection and efficient training. The goal is to enable reproducible and responsible research at scale by making these large language models more accessible to the broader research community. The opt-2.7b model was predominantly pretrained on English text, with a small amount of non-English data from CommonCrawl. It was trained using a causal language modeling (CLM) objective, similar to the self-supervised training of GPT-3. Evaluation of OPT models follows the prompts and experimental setup used for GPT-3. Model inputs and outputs Inputs Text prompts of varying lengths, which the model uses to generate additional text. Outputs Continuation of the input text, generating new text one token at a time in an autoregressive fashion. Capabilities The opt-2.7b model, like other large language models, has shown surprising emergent capabilities in areas such as text generation and zero-/few-shot learning. It can be used for a variety of natural language processing tasks by prompting the model and generating relevant text outputs. What can I use it for? The opt-2.7b model can be used for a wide range of applications that involve text generation, such as creative writing, summarization, dialogue systems, and code generation. It can also be fine-tuned on downstream tasks to adapt the model to more specific use cases. For example, the model could be fine-tuned on a dataset of customer service conversations to create a chatbot that can provide personalized responses to customer inquiries. Or it could be fine-tuned on a corpus of technical documentation to generate explanations and summaries for complex topics. Things to try One interesting thing to try with the opt-2.7b model is using it for open-ended text generation and observing the model's ability to maintain coherence and logical flow over long stretches of text. By providing the model with an initial prompt and letting it continue generating, you can see how it builds upon the context and develops the narrative or idea. Another idea is to experiment with different decoding strategies, such as top-k sampling, to generate more diverse and creative outputs from the model. This can uncover interesting variations and novel perspectives that may be useful for certain applications. Overall, the opt-2.7b model and the broader OPT suite represent an important step towards making large language models more accessible and enabling deeper understanding of their capabilities and limitations.
Updated Invalid Date