Hugging-quants
Models by this creator
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Meta-Llama-3.1-70B-Instruct-AWQ-INT4
68
The meta-llama/Meta-Llama-3.1-70B-Instruct-AWQ-INT4 model is a quantized version of the original meta-llama/Meta-Llama-3.1-70B-Instruct model, which is a large language model developed by Meta AI. This model has been quantized using AutoAWQ from FP16 down to INT4 precision, reducing the memory footprint and computational requirements. The Llama 3.1 collection of models includes versions in 8B, 70B, and 405B parameter sizes, with the instruction-tuned models optimized for multilingual dialogue use cases. Model inputs and outputs Inputs Multilingual text**: The model can accept text input in multiple languages, including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Code**: In addition to natural language, the model can also handle code input. Outputs Multilingual text**: The model can generate text output in the same supported languages as the inputs. Code**: The model can generate code output in addition to natural language. Capabilities The meta-llama/Meta-Llama-3.1-70B-Instruct-AWQ-INT4 model is a powerful text generation model with capabilities across a wide range of tasks, including language understanding, reasoning, code generation, and more. It has demonstrated strong performance on benchmarks like MMLU, ARC-Challenge, and HumanEval, outperforming many available open-source and commercial models. What can I use it for? This model can be used for a variety of natural language processing and generation tasks, such as: Chatbots and virtual assistants**: The instruction-tuned version of the model is well-suited for building helpful, multilingual chatbots and virtual assistants. Content generation**: The model can be used to generate high-quality text content in multiple languages, such as articles, stories, or marketing copy. Code generation**: The model's ability to generate code makes it useful for building code completion or programming assistance tools. Multilingual applications**: The model's support for multiple languages allows it to be used in building truly global, multilingual applications. Things to try Some interesting things to explore with this model include: Experimenting with different prompting and input sequences to see the range of outputs the model can generate. Evaluating the model's performance on specialized tasks or benchmarks relevant to your use case. Trying out the model's code generation capabilities by providing programming prompts and observing the quality of the output. Exploring the model's multilingual capabilities by testing it with input and output in different supported languages.
Updated 9/4/2024
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Meta-Llama-3.1-8B-Instruct-AWQ-INT4
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The Meta-Llama-3.1-8B-Instruct model is a community-driven quantized version of the original meta-llama/Meta-Llama-3.1-8B-Instruct model released by Meta AI. This repository contains a quantized version of the model using AutoAWQ from FP16 down to INT4 precision, with a group size of 128. Similar quantized models include the Meta-Llama-3.1-70B-Instruct-AWQ-INT4 and Meta-Llama-3.1-8B-Instruct models, which provide lower bit-depth versions of the original 8B and 70B Llama 3.1 Instruct models. Model inputs and outputs Inputs Multilingual Text**: The model accepts text input in multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Code**: In addition to natural language, the model can also process code snippets as input. Outputs Multilingual Text**: The model generates output text in the same set of supported languages as the input. Code**: The model can generate code in response to prompts. Capabilities The Meta-Llama-3.1-8B-Instruct model is a powerful text-to-text model capable of a wide range of natural language processing tasks. It has been optimized for multilingual dialogue use cases and outperforms many open-source and commercial chatbots on common industry benchmarks. What can I use it for? The Meta-Llama-3.1-8B-Instruct model can be used for a variety of applications, such as building multilingual chatbots, virtual assistants, and language generation tools. The quantized version offers significant space and memory savings compared to the original FP16 model, making it more accessible for deployment on resource-constrained devices. Things to try Some interesting things to try with the Meta-Llama-3.1-8B-Instruct model include generating multilingual responses, translating between supported languages, and using the model to assist with coding tasks. The quantized version's improved inference speed may also enable new use cases that require real-time text generation.
Updated 9/20/2024