Meta

Models by this creator

225.2K

meta-llama-3-8b-instruct

meta

meta-llama-3-8b-instruct is an 8 billion parameter language model from Meta that has been fine-tuned for chat completions. This model is part of the Llama 3 series, which also includes the base meta-llama-3-8b and the larger meta-llama-3-70b models. Compared to the base Llama 3 models, the meta-llama-3-8b-instruct version has been further trained on dialogue and instruction-following tasks, giving it enhanced capabilities for open-ended conversations and task completion. Model inputs and outputs The meta-llama-3-8b-instruct model takes a prompt as input and generates text as output. The prompt can be a statement, question, or instruction that the model uses to continue the conversation or complete the task. The output is a completion of the prompt, generated based on the model's understanding of the context and its training on dialogue and instruction-following. Inputs Prompt**: The starting text that the model should use to generate a completion. Outputs Text completion**: The model's generated continuation or completion of the input prompt. Capabilities The meta-llama-3-8b-instruct model is capable of engaging in open-ended dialogue, answering questions, and following instructions. It can be used for a variety of tasks such as language modeling, text generation, question answering, and task completion. The model's fine-tuning on dialogue and instruction-following allows it to generate more coherent and relevant responses compared to the base Llama 3 models. What can I use it for? The meta-llama-3-8b-instruct model can be used for a wide range of applications, such as building chatbots, virtual assistants, and content generation tools. Its ability to understand and respond to instructions makes it well-suited for automating various tasks, from customer service to content creation. Developers and businesses can leverage this model to enhance their products and services, while researchers can use it to further explore the capabilities of large language models. Things to try One interesting aspect of the meta-llama-3-8b-instruct model is its ability to follow complex instructions and generate coherent responses. You can try prompting the model with multi-step tasks or open-ended questions and observe how it handles the complexity. Additionally, you can experiment with different temperature and top-k/top-p settings to see how they affect the model's output in terms of creativity, coherence, and safety.

Updated 12/13/2024

Text-to-Text

134.6K

meta-llama-3-70b-instruct

meta

meta-llama-3-70b-instruct is a 70 billion parameter language model from Meta that has been fine-tuned for chat completions. It is part of Meta's Llama series of language models, which also includes the meta-llama-3-8b-instruct, codellama-70b-instruct, meta-llama-3-70b, codellama-13b-instruct, and codellama-7b-instruct models. Model inputs and outputs meta-llama-3-70b-instruct is a text-based model, taking in a prompt as input and generating text as output. The model has been specifically fine-tuned for chat completions, meaning it is well-suited for engaging in open-ended dialogue and responding to prompts in a conversational manner. Inputs Prompt**: The text that is provided as input to the model, which it will use to generate a response. Outputs Generated text**: The text that the model outputs in response to the input prompt. Capabilities meta-llama-3-70b-instruct can engage in a wide range of conversational tasks, from open-ended discussion to task-oriented dialog. It has been trained on a vast amount of text data, allowing it to draw upon a deep knowledge base to provide informative and coherent responses. The model can also generate creative and imaginative text, making it well-suited for applications such as story writing and idea generation. What can I use it for? With its strong conversational abilities, meta-llama-3-70b-instruct can be used for a variety of applications, such as building chatbots, virtual assistants, and interactive educational tools. Businesses could leverage the model to provide customer service, while writers and content creators could use it to generate new ideas and narrative content. Researchers may also find the model useful for exploring topics in natural language processing and exploring the capabilities of large language models. Things to try One interesting aspect of meta-llama-3-70b-instruct is its ability to engage in multi-turn dialogues and maintain context over the course of a conversation. You could try prompting the model with an initial query and then continuing the dialog, observing how it builds upon the previous context. Another interesting experiment would be to provide the model with prompts that require reasoning or problem-solving, and see how it responds.

Updated 12/13/2024

Text-to-Text

50.7K

meta-llama-3-8b

meta

meta-llama-3-8b is the base version of Llama 3, an 8 billion parameter language model from Meta. It is similar to other models like phi-3-mini-4k-instruct, qwen1.5-110b, meta-llama-3-70b, and snowflake-arctic-instruct in that they are all large language models with varying parameter sizes. However, meta-llama-3-8b is specifically optimized for production use and accessibility. Model inputs and outputs meta-llama-3-8b is a text-based language model that can take a prompt as input and generate text output. It can handle a wide range of tasks, from open-ended conversation to task-oriented prompts. Inputs Prompt**: The initial text that the model uses to generate the output. Top K**: The number of highest probability tokens to consider for generating the output. Top P**: A probability threshold for generating the output. Max Tokens**: The maximum number of tokens the model should generate as output. Min Tokens**: The minimum number of tokens the model should generate as output. Temperature**: The value used to modulate the next token probabilities. Presence Penalty**: A penalty applied to tokens based on whether they have appeared in the output previously. Frequency Penalty**: A penalty applied to tokens based on their frequency in the output. Outputs Generated Text**: The text output generated by the model based on the provided inputs. Capabilities meta-llama-3-8b can be used for a variety of natural language processing tasks, including text generation, question answering, and language translation. It has been trained on a large corpus of text data and can generate coherent and contextually relevant output. What can I use it for? meta-llama-3-8b can be used for a wide range of applications, such as chatbots, content generation, and language learning. Its accessibility and production-ready nature make it a useful tool for individual creators, researchers, and businesses looking to experiment with and deploy large language models. Things to try Some interesting things to try with meta-llama-3-8b include fine-tuning the model on a specific task or domain, using it to generate creative fiction or poetry, and exploring its capabilities for question answering and dialogue generation. The model's accessible nature and the provided examples and recipes make it a great starting point for experimenting with large language models.

Updated 12/13/2024

Text-to-Text

15.4K

codellama-70b-python

meta

codellama-70b-python is a 70 billion parameter Llama model fine-tuned by Meta for coding with Python. It is part of the Code Llama family of large language models which also includes the CodeLlama-7b-Python, CodeLlama-13b-Python, and CodeLlama-34b-Python models. These models are built on top of Llama 2 and show state-of-the-art performance among open models for coding tasks, with capabilities like infilling, large input contexts, and zero-shot instruction following. Model inputs and outputs codellama-70b-python takes in text prompts and generates continuations. The model can handle very large input contexts of up to 100,000 tokens. The outputs are Python code or text relevant to the prompt. Inputs Prompt**: The text prompt that the model will continue or generate Outputs Generated text**: The model's continuation or generation based on the input prompt Capabilities codellama-70b-python excels at a variety of coding-related tasks, including generating, understanding, and completing code snippets. It can be used for applications like code autocompletion, code generation, and even open-ended programming. The model's large size and specialized training allow it to handle complex coding challenges and maintain coherence over long input sequences. What can I use it for? With its strong coding capabilities, codellama-70b-python can be a valuable tool for developers, data scientists, and anyone working with Python code. It could be used to accelerate prototyping, assist with debugging, or even generate entire program components from high-level descriptions. Businesses and researchers could leverage the model to boost productivity, explore new ideas, and unlock innovative applications. Things to try Try providing the model with partially completed code snippets and see how it can fill in the missing pieces. You can also experiment with giving it natural language prompts describing a desired functionality and see if it can generate the corresponding Python implementation. The model's ability to maintain coherence over long inputs makes it well-suited for tasks like refactoring or optimizing existing codebases.

Updated 12/13/2024

Text-to-Text

15.3K

codellama-34b-instruct

meta

codellama-34b-instruct is a 34 billion parameter large language model developed by Meta, based on the Llama 2 architecture. It is part of the Code Llama family of models, which also includes versions with 7 billion, 13 billion, and 70 billion parameters. These models are designed for coding and conversation tasks, providing state-of-the-art performance among open models. The models have infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. Similar models include the codellama-70b-instruct with 70 billion parameters, the meta-llama-3-8b-instruct with 8 billion parameters, and the meta-llama-3-70b and meta-llama-3-8b base Llama 3 models. Model inputs and outputs The codellama-34b-instruct model takes a variety of inputs, including prompts for code generation, conversational tasks, and instruction following. The model supports input sequences of up to 100,000 tokens. Inputs Prompt**: The initial text or code to be used as a starting point for the model's response. System Prompt**: An optional prompt that can be used to provide additional context or guidance to the model. Temperature**: A parameter that controls the randomness of the model's output, with higher values resulting in more diverse and exploratory responses. Top K**: The number of most likely tokens to consider during the sampling process. Top P**: The cumulative probability threshold used for nucleus sampling, which limits the number of tokens considered. Repeat Penalty**: A penalty applied to the model's output to discourage repetition. Presence Penalty**: A penalty applied to the model's output to discourage the repetition of specific tokens. Frequency Penalty**: A penalty applied to the model's output to discourage the repetition of specific token sequences. Outputs Text**: The model's generated response, which can include code, natural language, or a combination of the two. Capabilities The codellama-34b-instruct model is capable of a wide range of tasks, including code generation, code completion, and conversational abilities. It can generate high-quality code in multiple programming languages, and its instruction-following capabilities allow it to perform complex programming tasks with minimal guidance. The model also has strong natural language understanding and generation abilities, enabling it to engage in natural conversations. What can I use it for? The codellama-34b-instruct model can be used for a variety of applications, including: Software development**: The model can be used to assist programmers with tasks such as code generation, code completion, and debugging. Conversational AI**: The model's natural language abilities can be leveraged to build conversational AI assistants for customer service, chatbots, and other applications. Technical writing**: The model can be used to generate technical documentation, tutorials, and other written content related to software and technology. Research and education**: The model can be used in academic and research settings to explore the capabilities of large language models and their potential applications. Things to try Some interesting things to try with the codellama-34b-instruct model include: Exploring the model's ability to generate complex, multi-step code solutions for programming challenges. Experimenting with the model's conversational abilities by engaging it in open-ended discussions on a variety of topics. Investigating the model's zero-shot instruction following capabilities by providing it with novel programming tasks and observing its performance. Analyzing the model's strengths and limitations in terms of its language understanding, code generation, and reasoning abilities.

Updated 12/13/2024

Text-to-Text

15.3K

codellama-13b

meta

codellama-13b is a 13 billion parameter language model developed by Meta that is tuned for code completion. It is part of the Code Llama family of models, which also includes the codellama-7b, codellama-34b, and codellama-70b variants, as well as instruction-following versions like codellama-13b-instruct. The Code Llama models are based on the Llama 2 architecture and provide state-of-the-art performance on code-related tasks. Model inputs and outputs The codellama-13b model takes in prompts as text inputs, which can be code snippets, natural language instructions, or a combination. It then generates text outputs that continue or complete the provided input. The model supports large input contexts up to 100,000 tokens and can perform tasks like code completion, infilling, and zero-shot instruction following. Inputs Prompt**: The text input that the model will use to generate a continuation or completion. Max Tokens**: The maximum number of tokens (words or subwords) to generate in the output. Temperature**: A sampling parameter that controls the randomness of the output generation. Top K**: The number of most likely tokens to consider during sampling. Top P**: The cumulative probability threshold to use for sampling. Frequency Penalty**: A penalty applied to tokens based on their frequency of appearance. Presence Penalty**: A penalty applied to tokens based on whether they have appeared in the input. Repeat Penalty**: A penalty applied to tokens based on how many times they have appeared in the output. Outputs Output**: The generated text continuation or completion of the input prompt. Capabilities The codellama-13b model is capable of generating high-quality code completions and continuations, leveraging its understanding of programming languages and best practices. It can assist with tasks like auto-completing code snippets, generating boilerplate code, and even writing entire functions or algorithms. The model also has the ability to infill missing code segments based on the surrounding context. What can I use it for? The codellama-13b model can be used in a variety of applications that involve code generation or understanding, such as: Integrated development environment (IDE) plugins for intelligent code completion Automated code generation for prototyping or scaffolding Programming education and training tools Chatbots or virtual assistants that can help with coding tasks Augmented programming workflows to boost developer productivity Things to try Some interesting things to try with the codellama-13b model include: Providing partial code snippets and seeing how the model completes them Giving the model natural language instructions for a coding task and observing the generated code Exploring the model's ability to generate code in different programming languages or domains Evaluating the model's performance on specific coding challenges or benchmarks Experimenting with the various input parameters to see how they affect the output quality and creativity Overall, the codellama-13b model represents an exciting advancement in the field of large language models for code-related tasks, and offers a wealth of opportunities for developers, researchers, and AI enthusiasts to explore.

Updated 12/13/2024

Text-to-Text

15.2K

codellama-7b-instruct

meta

codellama-7b-instruct is a 7 billion parameter Llama model fine-tuned by Meta for coding and conversation. It is part of the Code Llama family of models, which also includes larger versions such as codellama-13b-instruct and codellama-34b-instruct. These models are based on the Llama 2 language model and show improvements on inputs with up to 100,000 tokens. The 7B and 13B versions also support code infilling capabilities, where the model can fill in missing sections of code given the surrounding context. Model inputs and outputs The codellama-7b-instruct model takes in prompts and generates text outputs. The inputs can include a system prompt, which helps guide the model's behavior, as well as parameters like temperature, top-k, and top-p to control the sampling. The outputs are generated text, which can be used for a variety of coding and conversational tasks. Inputs Prompt**: The main text prompt to be used for generation. System Prompt**: An optional system prompt that is prepended to the main prompt to help guide the model's behavior. Temperature**: Controls the randomness of the generated text, with higher values leading to more diverse outputs. Top-K**: Limits the number of most likely tokens to consider during generation. Top-P**: Limits the cumulative probability of the most likely tokens to consider during generation. Outputs Generated Text**: The text generated by the model in response to the input prompt. Capabilities The codellama-7b-instruct model is capable of generating human-like responses for a variety of coding and conversational tasks. It can be used for tasks like code completion, code generation, and answering coding-related questions. The model also has the capability to fill in missing sections of code given the surrounding context. What can I use it for? The codellama-7b-instruct model can be used for a variety of applications, such as building AI-powered coding assistants, automating code generation workflows, and enhancing conversational interfaces for software development. The model's capabilities can be leveraged by developers, researchers, and businesses to improve productivity, reduce development time, and explore new use cases for large language models in the coding domain. Things to try One interesting thing to try with codellama-7b-instruct is its code infilling capabilities. By providing the model with a partially completed code snippet and the surrounding context, you can see how it fills in the missing pieces. This can be helpful for tasks like code completion, bug fixing, and exploring alternative implementations. Another interesting aspect to explore is the model's ability to follow instructions and generate responses that adhere to a specific format, which can be useful for building interactive coding assistants.

Updated 12/13/2024

Text-to-Text

15.2K

codellama-13b-instruct

meta

codellama-13b-instruct is a 13 billion parameter Llama model developed by Meta, tuned for coding and conversation. It is part of the Code Llama family of models, which also includes variants with 7 billion, 34 billion, and 70 billion parameters. These models are built by fine-tuning Llama 2 and show improvements on inputs with up to 100,000 tokens. The 7 billion and 13 billion versions of Code Llama and Code Llama-Instruct also support infilling based on surrounding content. Model inputs and outputs codellama-13b-instruct takes in a prompt and generates text output. The model supports a variety of input parameters, including top-k, top-p, temperature, max tokens, and different penalty settings to control the output. The output is a list of generated text. Inputs Prompt**: The input text to guide the model's generation. System Prompt**: An optional system-level prompt that helps guide the model's behavior. Max Tokens**: The maximum number of tokens to generate in the output. Temperature**: Controls the randomness of the output, with higher values generating more diverse text. Top K**: Limits the number of most likely tokens to consider during generation. Top P**: Limits the cumulative probability of the most likely tokens to consider during generation. Frequency Penalty**: Penalizes the model for generating the same tokens frequently. Presence Penalty**: Penalizes the model for generating tokens that have not appeared in the prompt. Repeat Penalty**: Penalizes the model for generating repetitive text. Outputs Generated Text**: A list of text generated by the model in response to the input prompt. Capabilities codellama-13b-instruct is capable of generating code, providing explanations, and following instructions in a variety of domains. It can be used for tasks like code generation, code explanation, and even open-ended conversation. The model has been trained with safety mitigations to help address potential risks. What can I use it for? codellama-13b-instruct can be used for a wide range of applications, from building AI-powered coding assistants to developing chatbots and virtual assistants. The model's capabilities make it useful for tasks like automating code generation, explaining programming concepts, and assisting with open-ended tasks. Developers and businesses can experiment, innovate, and scale their ideas using this model. Things to try Some interesting things to try with codellama-13b-instruct include: Generating code snippets based on natural language prompts Asking the model to explain programming concepts or algorithms Exploring the model's ability to follow complex instructions and complete multi-step tasks Combining codellama-13b-instruct with other AI models or tools to build more sophisticated applications

Updated 12/13/2024

Text-to-Text

15.2K

codellama-34b

meta

codellama-34b is a 34 billion parameter language model developed by Meta that is tuned for coding and conversation. It is part of the Code Llama family of models, which also includes CodeLlama-7b, CodeLlama-13b, CodeLlama-34b-Instruct, CodeLlama-13b-Instruct, and CodeLlama-70b-Instruct. These models are based on the open-source Llama 2 language model and have been fine-tuned to excel at coding and programming tasks. Model inputs and outputs The codellama-34b model takes natural language prompts as input and generates continuation text as output. The prompts can be related to coding, programming, or general conversation, and the model will attempt to provide relevant and coherent responses. Inputs Natural language prompts related to coding, programming, or general conversation Outputs Continuation text that is relevant and coherent with the input prompt Capabilities The codellama-34b model has state-of-the-art performance among open-source language models for coding and programming tasks. It can generate working code, explain programming concepts, and engage in open-ended conversations. The model also has the ability to perform code infilling, where it can fill in missing parts of code based on the surrounding context. What can I use it for? codellama-34b can be used for a variety of applications, including: Generating code snippets or entire programs based on natural language prompts Explaining programming concepts and answering coding-related questions Engaging in open-ended conversations about technology, coding, and related topics Assisting with code development by performing tasks like code completion and code infilling Companies and developers can use codellama-34b to enhance their existing products or build new applications that leverage the model's natural language understanding and generation capabilities. Things to try You can experiment with codellama-34b by trying different types of prompts, such as: Asking the model to generate a function or program that performs a specific task Prompting the model to explain a programming concept or algorithm Engaging the model in a conversation about a technical topic and observing its responses Providing the model with partially completed code and asking it to fill in the missing parts By exploring the model's capabilities through various prompts and tasks, you can gain insights into its strengths and limitations, and explore ways to integrate it into your own projects and applications.

Updated 12/13/2024

Text-to-Text

15.2K

codellama-13b-python

meta

codellama-13b-python is a 13 billion parameter Llama language model fine-tuned by Meta for coding with Python. It is part of the Code Llama family of models, which also includes variants like Code Llama - Python and Code Llama - Instruct. These models leverage the state-of-the-art Llama 2 architecture and provide capabilities such as code generation, infilling, and zero-shot instruction following for programming tasks. Model inputs and outputs codellama-13b-python takes text prompts as input and generates continuations or completions of that text. The model is particularly adept at generating and completing Python code based on the provided context. Its outputs can range from short code snippets to longer programs, depending on the input prompt. Inputs Prompt**: The text that the model will use as a starting point to generate output. Outputs Generated text**: The model's continuation or completion of the input prompt, which may include Python code. Capabilities The codellama-13b-python model is capable of generating high-quality Python code based on the provided context. It can understand and complete partial code snippets, write entire functions or classes, and even generate complex programs from a high-level description. The model also demonstrates strong code understanding and can be used for tasks like code summarization, translation, and refactoring. What can I use it for? codellama-13b-python can be a valuable tool for a variety of software development and data science tasks. Developers can use it to boost productivity by automating repetitive coding tasks, generating boilerplate code, or prototyping new ideas. Data scientists can leverage the model to generate custom data processing scripts, model training pipelines, or visualization code. Educators and students can also use the model to aid in learning programming concepts and syntax. Things to try One interesting aspect of codellama-13b-python is its ability to perform code infilling, where it can generate missing parts of a code snippet based on the surrounding context. This can be useful for tasks like fixing bugs, implementing new features, or exploring alternative solutions to a problem. You can also try prompting the model with high-level descriptions of programming tasks and see how it translates those into working code.

Updated 12/13/2024

Text-to-Text