Meta

Llama2 by Meta

Llama2 by Meta

Llama 2 is an open-source large language model developed by Meta. It is available for free for both research and commercial use. This next-generation model includes pretrained and fine-tuned language models that range from 7B to 70B parameters. The pretrained models were trained on 2 trillion tokens and offer double the context length compared to Llama 1. The fine-tuned models have been trained on over 1 million human annotations.

Llama 2 outperforms other open-source language models in various external benchmarks, including reasoning, coding, proficiency, and knowledge tests. The model leverages publicly available online data sources for pretraining and publicly available instruction datasets, along with over 1 million human annotations, for fine-tuning.

Meta has established partnerships with cloud providers, companies, and researchers who support their open approach. They aim to create a responsible and collaborative AI innovation ecosystem. They provide resources such as a Responsible Use Guide for developers, safety red-teaming to enhance performance and safety, an Open Innovation AI Research Community for academic researchers, and the Llama Impact Challenge to encourage using Llama 2 to address environmental and educational challenges.

Meta also maintains a Generative AI Community Forum, in consultation with Stanford Deliberative Democracy Lab and the Behavioral Insights Team, to involve the community in decision-making around generative AI technologies.

To access Llama 2, users can complete a download form, agreeing to Meta’s privacy policy. Additional resources, including a technical overview, research paper, and blog post, provide more information on Llama 2 and its responsible use.

Code Llama

Code Llama

Code Llama is a state-of-the-art large language model (LLM) designed specifically for generating code and natural language about code. It is built on top of Llama 2 and is available in three different models: Code Llama (foundational code model), Codel Llama – Python (specialized for Python), and Code Llama – Instruct (fine-tuned for understanding natural language instructions). Code Llama can generate code and natural language about code based on prompts from both code and natural language inputs. It can be used for tasks such as code completion and debugging in popular programming languages like Python, C , Java, PHP, Typescript, C#, and Bash.

Code Llama comes in different sizes with varying parameters, such as 7B, 13B, and 34B. These models have been trained on a large amount of code and code-related data. The 7B and 13B models have fill-in-the-middle capability, enabling them to support code completion tasks. The 34B model provides the best coding assistance but may have higher latency. The models can handle input sequences of up to 100,000 tokens, allowing for more context and relevance in code generation and debugging scenarios.

Additionally, Code Llama has two fine-tuned variations: Code Llama – Python, which is specialized for Python code generation, and Code Llama – Instruct, which has been trained to provide helpful and safe answers in natural language. It is important to note that Code Llama is not suitable for general natural language tasks and should be used solely for code-specific tasks.

Code Llama has been benchmarked against other open-source LLMs and has demonstrated superior performance, scoring high on coding benchmarks such as HumanEval and Mostly Basic Python Programming (MBPP). Responsible development and safety measures have been undertaken in the creation of Code Llama.

Overall, Code Llama is a powerful and versatile tool that can enhance coding workflows, assist developers, and aid in learning and understanding code.

CM3leon by Meta

CM3leon by Meta

CM3leon by Meta Freemium CM3leon: Revolutionizing Vision-Language Generation VISIT WEBSITE Most popular alternative: Photo AI Introduction: Are you looking for an AI tool that can effortlessly generate text and images based on your input? Look no further than CM3leon by Meta. This state-of-the-art generative model combines the power of autoregressive models with low training costs …

CM3leon by Meta Read More »

Code Llama

Code Llama

Code Llama is a state-of-the-art large language model (LLM) designed specifically for generating code and natural language about code. It is built on top of Llama 2 and is available in three different models: Code Llama (foundational code model), Codel Llama – Python (specialized for Python), and Code Llama – Instruct (fine-tuned for understanding natural language instructions). Code Llama can generate code and natural language about code based on prompts from both code and natural language inputs. It can be used for tasks such as code completion and debugging in popular programming languages like Python, C , Java, PHP, Typescript, C#, and Bash.

Code Llama comes in different sizes with varying parameters, such as 7B, 13B, and 34B. These models have been trained on a large amount of code and code-related data. The 7B and 13B models have fill-in-the-middle capability, enabling them to support code completion tasks. The 34B model provides the best coding assistance but may have higher latency. The models can handle input sequences of up to 100,000 tokens, allowing for more context and relevance in code generation and debugging scenarios.

Additionally, Code Llama has two fine-tuned variations: Code Llama – Python, which is specialized for Python code generation, and Code Llama – Instruct, which has been trained to provide helpful and safe answers in natural language. It is important to note that Code Llama is not suitable for general natural language tasks and should be used solely for code-specific tasks.

Code Llama has been benchmarked against other open-source LLMs and has demonstrated superior performance, scoring high on coding benchmarks such as HumanEval and Mostly Basic Python Programming (MBPP). Responsible development and safety measures have been undertaken in the creation of Code Llama.

Overall, Code Llama is a powerful and versatile tool that can enhance coding workflows, assist developers, and aid in learning and understanding code.