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Llama3.1-8B-Instruct
Model Introduction
Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.
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Language comprehension ability Language comprehension ability
Language comprehension ability
Often makes semantic misjudgments, leading to obvious logical disconnects in responses.
3.7
Knowledge coverage scope Knowledge coverage scope
Knowledge coverage scope
Has significant knowledge blind spots, often showing factual errors and repeating outdated information.
5.5
Reasoning ability Reasoning ability
Reasoning ability
Unable to maintain coherent reasoning chains, often causing inverted causality or miscalculations.
3.7
Related model
Llama4-Maverick-17B-128E-Instruct The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.
Llama4-Maverick-17B-128E-Instruct The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.
Llama3.1-8B-Instruct Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.
Llama3.1-405B-Instruct-FP8 Llama 3.1 405B is the first openly available model that rivals the top AI models when it comes to state-of-the-art capabilities in general knowledge, steerability, math, tool use, and multilingual translation.
Llama3.2-3B-Instruct The Llama 3.2 3B models support context length of 128K tokens and are state-of-the-art in their class for on-device use cases like summarization, instruction following, and rewriting tasks running locally at the edge.
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