EAGLE3 LLaMA3.1 Instruct 8B Alternatives

EAGLE-LLaMA3-Instruct-8B is an instruction-tuned 8 billion parameter model based on Llama 3 that incorporates EAGLE speculative decoding techniques for faster inference. It supports popular libraries including Transformers and vLLM and is available on Hugging Face. The model is built for developers seeking high-speed text generation while maintaining the quality of Llama 3 instruct capabilities.

Llama3_2_1B_speculator.eagle3 is a specialized speculative decoding model designed to accelerate inference for the Llama 3.2 1B language model. Hosted on Hugging Face in Safetensors format, it enables faster text generation by predicting multiple tokens ahead. It is intended for developers optimizing local or hosted LLM performance using compatible inference engines.

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.

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.

LLaDA-8B-Instruct is an 8 billion parameter instruction-tuned model released by GSAI-ML. It uses a custom chat template for conversational interactions and is available on Hugging Face for download and local inference. The model is designed for general text generation and assistant-style applications.

Llama-3.1-8B-Instruct-FP8 is an FP8 quantized version of Meta's Llama 3.1 8B instruct model, optimized by NVIDIA for high-performance inference. It maintains strong instruction-following capabilities while benefiting from reduced memory bandwidth and faster execution on compatible NVIDIA GPUs. The model is hosted on Hugging Face and is intended for developers building efficient LLM-powered applications.

Meta-Llama-3-8B-Instruct is an 8 billion parameter language model hosted on Hugging Face under the NousResearch organization. It belongs to the Llama 3 family and serves as an instruction-tuned variant intended for conversational and assistant-style applications. The model uses the LlamaForCausalLM architecture with a model type of llama. Its tokenizer configuration includes a specific chat template that structures messages with role-based headers, beginning-of-text and end-of-text tokens, and support for an add-generation-prompt flag to prepare responses from an assistant role. The configuration also defines bos_token as <|begin_of_text| and eos_token as <|eot_id|. It is delivered as a repository on the Hugging Face platform, where it can be accessed for download and use in machine learning workflows. The page indicates substantial community engagement through over two million all-time downloads. The model was created on April 18, 2024. As a foundation model, it is positioned for research, fine-tuning, and integration into local AI systems that require instruction following. The repository is part of the broader Hugging Face ecosystem for open-source machine learning models.

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-3.1-8B-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.

RedHatAI/Llama-3.2-1B-Instruct-FP8-dynamic is a quantized, instruction-tuned large language model based on the Llama architecture. It is open source, supports text generation and fine-tuning, and is suitable for AI researchers and developers seeking efficient, customizable LLMs for experimentation or integration.

The Llama 3.2 1B Instruct Q4_K_M is a quantized GGUF version of a text generation model hosted on Hugging Face. It belongs to the class of foundation models and carries tags for conversational use, Llama architecture, and llama.cpp compatibility. The repository is maintained under the hugging-quants organization and includes the file llama-3.2-1b-instruct-q4_k_m.gguf. It is marked as quantized, with a total file size of 807690656 bytes. The model card lists support for eight languages and references the original Meta Llama 3.2 1B Instruct base along with PyTorch and GGUF formats. A chat template is defined in the repository data using special tokens such as bos_token, eos_token, and structured role headers for assistant and user messages. This enables direct use in compatible inference engines without additional conversion. The page indicates four discussions, two of which are closed. No pricing, licensing terms, or specific target audience beyond the model tags are stated in the repository metadata.

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.

This is the 8B Instruct version of Meta's Llama 3.1 model, hosted by Unsloth on Hugging Face. It supports instruction following, tool use, and long context windows. The model is widely used for local inference, fine-tuning, and as a base for custom applications via the Transformers library.

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.