Llama 3.1 8B Instruct
Optimized for multilingual dialogue, outperforming open-source and closed chat models on industry benchmarks.
Meta 
DeepInfra Multilingual instruction-tuned dialogue model from Meta
Llama 3.1 8B Instruct is an 8-billion-parameter instruction-tuned text generation model developed by Meta as part of the Llama 3.1 collection, which also includes 70B and 405B variants. It accepts text input and produces text output, and has been fine-tuned specifically for dialogue and instruction-following tasks across multiple languages. The model supports a 128,000-token context window, making it suitable for tasks involving long documents or extended conversations.
This model is optimized for multilingual dialogue use cases and is released under Meta's Llama 3.1 community license, allowing broad use including commercial applications. It is well-suited for chatbots, text summarization, question answering, and other conversational AI applications where a relatively compact model size is desirable for deployment efficiency. Developers can run it locally or via API, and it is widely available through platforms like Hugging Face with support for the Transformers library.
What Llama 3.1 8B Instruct supports
Multilingual Dialogue
Handles conversational tasks across multiple languages, fine-tuned specifically for instruction-following and chat use cases.
Long Context Processing
Supports a 128,000-token context window, enabling processing of long documents, extended conversations, or large code files in a single pass.
Text Summarization
Condenses long-form text into concise summaries, leveraging its large context window to handle lengthy source documents.
Instruction Following
Responds to structured prompts and multi-step instructions, trained with supervised fine-tuning and RLHF alignment techniques.
Code Assistance
Generates, explains, and debugs code across common programming languages as part of its general instruction-tuned capabilities.
Question Answering
Answers factual and open-ended questions in a conversational format, drawing on knowledge encoded during pretraining.
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Get Started FreeCommon questions about Llama 3.1 8B Instruct
What is the context window size for Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct supports a context window of 128,000 tokens, allowing it to process long documents or extended conversations in a single request.
What languages does this model support?
The model is optimized for multilingual dialogue. Meta's Llama 3.1 release officially supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, among others.
Is Llama 3.1 8B Instruct available for commercial use?
Yes. The model is released under Meta's Llama 3.1 community license, which permits commercial use subject to the terms outlined in that license agreement.
What is the knowledge cutoff date for this model?
The training cutoff date is listed as not available in the current metadata. Meta has not publicly specified a precise cutoff date for Llama 3.1 8B Instruct in its official documentation.
How does this model differ from the base Llama 3.1 8B model?
The Instruct variant has been fine-tuned for instruction-following and dialogue tasks using supervised fine-tuning and reinforcement learning from human feedback (RLHF), whereas the base model is a pretrained model without task-specific alignment.
Can I run Llama 3.1 8B Instruct locally?
Yes. The model weights are available on Hugging Face and are compatible with the Transformers library, enabling local deployment on appropriate hardware.
What people think about Llama 3.1 8B Instruct
Community discussions on r/LocalLLaMA frequently include Llama 3.1 8B Instruct in comparative benchmarks and hardware experiments, with users noting it as a commonly referenced baseline in the 7B–9B parameter class. Threads involving multi-model evaluations and local hardware builds often include it as one of the models tested.
Some community members note that newer models in the same size class have emerged since its release, and discussions around "personality" probing and grading experiments suggest ongoing interest in understanding its behavioral characteristics relative to peers. It remains a frequently cited model for users running inference on consumer hardware with limited VRAM.
I locally benchmarked 41 open-source LLMs across 19 tasks and ranked them
4x AMD R9700 (128GB VRAM) + Threadripper 9955WX Build
LLMs grading other LLMs 2
I measured the "personality" of 6 open-source LLMs (7B-9B) by probing their hidden states. Here's what I found.
I gave the same silly task to ~70 models that fit on 32GB of VRAM - thousands of times (resharing my post from /r/LocalLLM)
Documentation & links
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