Llama 3.2 1B Instruct Q4 K M Alternatives

Llama 3.2 1B Instruct Q8 0 is a quantized GGUF version of the Llama 3.2 1B Instruct model hosted on Hugging Face by the Hugging Quants organization. It belongs to the class of foundation models and supports text generation and conversational tasks across eight languages. The model card indicates it uses the Llama architecture from Meta and includes a specific chat template with tokens such as bos_token, eos_token, and structured role-based formatting for assistant responses. The repository provides the file llama-3.2-1b-instruct-q8_0.gguf along with associated metadata confirming it is quantized. Total file size is listed as 1,321,079,200 bytes. It carries tags for GGUF, PyTorch, llama.cpp, and facebook/meta/llama families, positioning it for use in compatible inference runtimes that handle this format. Hugging Quants maintains the repository, which has accumulated around 50 likes and hosts four discussions. The page forms part of the broader Hugging Face ecosystem for models, datasets, and related resources focused on open-source AI.

This repository contains GGUF quantized files of Meta's Llama 3.2 3B Instruct model, maintained by the LM Studio community. It supports tool calling, system prompts, and efficient local execution using tools like llama.cpp. The model is ideal for developers seeking lightweight, locally runnable instruction-tuned LLMs.

This repository contains GGUF format quantized weights for Meta's Llama 3.2 3B Instruct model, created by bartowski. These files are optimized for use with llama.cpp, LM Studio, and other local LLM inference tools.

A 1B parameter version of Meta's Llama 3.2 Instruct model, quantized to 4-bit and packaged for the MLC-LLM runtime. It enables high-performance local inference across GPUs, CPUs, and mobile devices. The model is distributed via Hugging Face and supports standard chat templates for conversational use.

This is a 4-bit quantized version of Meta's Llama 3.2 3B Instruct model, optimized by Unsloth for faster inference and lower memory usage. It maintains strong instruction-following capabilities while being suitable for deployment on laptops and modest GPUs. The model includes full chat templates and is compatible with the Hugging Face ecosystem.

This is a community-quantized GGUF version of Meta's Llama 3.1 8B Instruct model, optimized for use with LM Studio and other local inference tools. It provides an instruction-tuned 8 billion parameter language model that can run efficiently on consumer CPUs and GPUs. The GGUF format enables flexible quantization levels to balance performance and resource requirements for local AI applications.

This is an optimized 4-bit quantized version of the Llama 3.2 1B Instruct model created by Unsloth. It is designed for efficient local inference while maintaining strong instruction-following capabilities. The model is available on Hugging Face and works with popular LLM runtimes and fine-tuning tools.

This is an AWQ INT4 quantized version of Meta's Llama 3.3 70B Instruct model. It enables efficient local or server-based inference while preserving most of the original model's capabilities, including tool use and reasoning. The model is hosted on Hugging Face.

Meta-Llama-3.1-8B-Instruct-GGUF provides GGUF quantized files for Meta's Llama 3.1 8B Instruct model. It supports multiple quantization levels (Q2_K through Q8_0) for efficient local execution. The model is used by developers who want to run a capable instruction-tuned LLM on consumer-grade hardware without relying on cloud APIs.

This is an AWQ (INT4) quantized version of Meta's Llama 3.1 70B Instruct model, optimized for reduced memory usage while maintaining performance. It includes a detailed chat template and is suitable for local inference. The model is provided by the hugging-quants organization on Hugging Face.

This is an FP8 dynamically quantized version of Meta's Llama-3.3-70B-Instruct model. It maintains strong reasoning and instruction-following capabilities while significantly reducing memory requirements compared to the original 16-bit model. The model is suitable for local or self-hosted inference using compatible quantization and serving frameworks.

This repository hosts an AWQ-quantized version of Meta's Llama 3 70B Instruct model, created by casperhansen. It enables efficient inference of this powerful instruction-tuned model on consumer or enterprise hardware with lower VRAM requirements. The model supports chat templates and is compatible with standard Hugging Face transformers and inference tools.

MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF is a repository on Hugging Face that hosts GGUF quantized versions of Meta's Llama 3 8B Instruct model. It supplies multiple quantization files to support efficient local inference on a range of hardware. The available files include Meta-Llama-3-8B-Instruct.IQ1_M.gguf, Meta-Llama-3-8B-Instruct.IQ1_S.gguf, Meta-Llama-3-8B-Instruct.IQ2_XS.gguf, Meta-Llama-3-8B-Instruct.IQ3_XS.gguf, Meta-Llama-3-8B-Instruct.IQ4_XS.gguf, Meta-Llama-3-8B-Instruct.Q2_K.gguf, Meta-Llama-3-8B-Instruct.Q3_K_L.gguf, Meta-Llama-3-8B-Instruct.Q3_K_M.gguf, Meta-Llama-3-8B-Instruct.Q3_K_S.gguf, Meta-Llama-3-8B-Instruct.Q4_K_M.gguf, Meta-Llama-3-8B-Instruct.Q4_K_S.gguf, Meta-Llama-3-8B-Instruct.Q5_K_M.gguf, and Meta-Llama-3-8B-Instruct.Q5_K_S.gguf. These different levels allow users to select trade-offs between model size and performance. The repository also contains a chat template that defines formatting for messages with roles, beginning and end of text tokens, and assistant prompts. It is delivered as downloadable GGUF files through the Hugging Face platform. The total file size across all variants is listed as 16069403840 bytes. This format enables compatibility with inference engines that support GGUF, such as those used for running large language models locally. The repository forms part of the class of foundation models provided in quantized GGUF format for open-source use. No pricing, licensing details, or specific target audience beyond general access on Hugging Face are stated.

Llama-3.2-1B-Instruct is an open-source, instruction-tuned language model from Meta, designed for text generation and conversational AI. It is suitable for developers and researchers building chatbots, virtual assistants, and other NLP applications.

Llama-3.1-8B-Instruct is an open-source large language model developed by Meta for instruction-following and conversational AI tasks. It is designed for developers and researchers to build advanced NLP applications and is available via Hugging Face with open weights.

Meta-Llama-3.1-8B-Instruct-AWQ-INT4 is an open-source, quantized large language model optimized for instruction-following and efficient text generation. It supports API and local deployment, serving AI developers and researchers.

This is an FP8-quantized version of Meta's Llama 3.1 70B Instruct model, published by RedHatAI. It maintains strong instruction-following capabilities while using a more memory-efficient format suitable for self-hosted deployment. The model includes a detailed chat template and is designed for production inference environments.

llama-3.3-70b-instruct-awq is a quantized version of Meta's Llama 3.3 70B Instruct model using the AWQ quantization method. This allows the large model to run with reduced memory requirements while maintaining performance. It includes a chat template optimized for instruction following and is compatible with Transformers and other inference frameworks.

A quantized (w4a16) version of Meta's Llama 3.1 70B Instruct model provided by RedHatAI. It maintains the strong instruction-following and reasoning capabilities of the original while using 4-bit weights for more efficient inference. The model is compatible with standard Hugging Face and vLLM inference stacks.

This repository contains an INT4 (w4a16) quantized version of Meta's Llama-3.1-8B-Instruct model. It enables efficient inference on hardware with limited resources while preserving most of the original model's instruction-following capabilities. The model is distributed for use with Transformers and compatible inference engines.

Unsloth's version of Meta's Llama 3.2 3B Instruct model is optimized for faster fine-tuning and inference. It supports custom tools, system messages, and date-aware prompting. The model is designed for efficient local and cloud deployment via standard Hugging Face pipelines.

Llama-3.1-8B-Instruct is an instruction-tuned version of Meta's Llama 3.1 8B model, optimized by Unsloth for faster training and inference. It supports chat templates, function calling, and various quantization formats for efficient local or cloud deployment. The model is intended for developers building custom AI applications, agents, or fine-tuned domain-specific assistants.

Llama 3.1 405B Instruct is Meta's flagship open-weights large language model. It supports advanced chat templates, tool calling, and high-quality instruction following. The model is available on Hugging Face for download and can be used with various inference frameworks and providers.

Llama-3.2-3B-Instruct is an open-source, instruction-tuned language model designed for text generation and conversational AI. It supports both local and API-based inference, making it ideal for developers building chatbots, virtual assistants, and other NLP applications.