Llama 3 8B Instruct
Best-in-class 8B model with improved reasoning, code generation, and instruction following.
Meta 
DeepInfra Instruction-tuned 8B model from Meta
Llama 3 8B Instruct is an 8-billion parameter instruction-following language model developed by Meta. It is the fine-tuned variant of the Llama 3 8B base model, optimized for chat and task completion through supervised fine-tuning and reinforcement learning from human feedback. The model has a context window of 8,000 tokens and was trained on data up to March 2023. It is released under Meta's Llama 3 Community License, making it available for a wide range of research and commercial applications.
Compared to its predecessor Llama 2, Llama 3 8B Instruct incorporates improvements in pretraining data quality and post-training alignment procedures, which reduced false refusal rates and increased response diversity. The model demonstrates improved performance on reasoning, code generation, and instruction-following tasks relative to the prior generation. It is well-suited for developers building conversational applications, text summarization tools, coding assistants, or other natural language processing workflows where a locally deployable or API-accessible open-weight model is preferred.
What Llama 3 8B Instruct supports
Instruction Following
Responds to natural language instructions with improved steerability, achieved through post-training alignment that reduced false refusal rates and increased response diversity.
Code Generation
Generates and explains code across common programming languages, with capability improvements introduced in the Llama 3 post-training process.
Text Reasoning
Handles multi-step reasoning tasks in natural language, with reasoning improvements over the Llama 2 generation noted in Meta's model documentation.
Conversational Chat
Supports multi-turn dialogue formatted for chat use cases, using the Llama 3 instruct prompt template with special tokens for system, user, and assistant turns.
Text Summarization
Condenses longer documents or passages into concise summaries within the model's 8,000-token context window.
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Get Started FreeCommon questions about Llama 3 8B Instruct
What is the context window for Llama 3 8B Instruct?
Llama 3 8B Instruct supports a context window of 8,000 tokens, which covers both the input prompt and the generated output combined.
What is the knowledge cutoff date for this model?
The model's training data has a cutoff of March 2023, meaning it does not have knowledge of events or information published after that date.
Is Llama 3 8B Instruct open source?
The model weights are publicly available on Hugging Face under Meta's Llama 3 Community License. This license permits use for research and many commercial applications, though it includes usage restrictions detailed in the license agreement.
What is the difference between Llama 3 8B and Llama 3 8B Instruct?
Llama 3 8B is the base pretrained model, while Llama 3 8B Instruct is fine-tuned specifically for instruction following and conversational tasks using supervised fine-tuning and alignment techniques. The Instruct variant is generally more suitable for end-user-facing applications.
Who publishes Llama 3 8B Instruct?
Llama 3 8B Instruct is developed and published by Meta. It was added to MindStudio on May 9, 2024.
What people think about Llama 3 8B Instruct
Community discussions around open-source LLMs like Llama 3 8B Instruct frequently appear in the LocalLLaMA subreddit, where users share benchmarks and local deployment results across a range of hardware configurations. Practitioners value the model's accessibility as an open-weight option that can be run locally without API dependencies.
Common use cases discussed include NPC dialogue in games, local benchmarking comparisons, and hardware performance testing across different GPU setups. Users also note the importance of quantization and hardware compatibility when deploying 8B-scale models locally.
I locally benchmarked 41 open-source LLMs across 19 tasks and ranked them
AMD Instinct MI60 (32gb VRAM) "llama bench" results for 10 models - Qwen3 30B A3B Q4_0 resulted in: pp512 - 1,165 t/s | tg128 68 t/s - Overall very pleased and resulted in a better outcome for my use case than I even expected
Obsessed with Claude Code here.
How am I building a hacking sim game themed on 90s with NPCs powered by AI (LocalLLM)
Documentation & links
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