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Llama 3.1 8B Instruct GGUF

** Updated as of 2024-07-27 **

Original model: Meta-Llama-3.1-8B-Instruct

Model creator: Meta

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

This repo contains GGUF format model files for Meta’s Llama 3.1 8B Instruct, updated as of 2024-07-27 to incorporate long context improvements, as well as changes to the huggingface model itself.

Learn more on Meta’s Llama 3.1 page.

What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Converted with llama.cpp build 3472 (revision b5e9546), using autogguf.

Prompt template

<|start_header_id|>system<|end_header_id|>

{{system_prompt}}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{prompt}}<|eot_id|><|start_header_id|>assistant<|end_header_id|>


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Original Model Evaluation

Category Benchmark # Shots Metric Llama 3 8B Instruct Llama 3.1 8B Instruct Llama 3 70B Instruct Llama 3.1 70B Instruct Llama 3.1 405B Instruct
General MMLU 5 macro_avg/acc 68.5 69.4 82.0 83.6 87.3
MMLU (CoT) 0 macro_avg/acc 65.3 73.0 80.9 86.0 88.6
MMLU-Pro (CoT) 5 micro_avg/acc_char 45.5 48.3 63.4 66.4 73.3
IFEval 76.8 80.4 82.9 87.5 88.6
Reasoning ARC-C 0 acc 82.4 83.4 94.4 94.8 96.9
GPQA 0 em 34.6 30.4 39.5 41.7 50.7
Code HumanEval 0 pass@1 60.4 72.6 81.7 80.5 89.0
MBPP ++ base version 0 pass@1 70.6 72.8 82.5 86.0 88.6
Multipl-E HumanEval 0 pass@1 - 50.8 - 65.5 75.2
Multipl-E MBPP 0 pass@1 - 52.4 - 62.0 65.7
Math GSM-8K (CoT) 8 em_maj1@1 80.6 84.5 93.0 95.1 96.8
MATH (CoT) 0 final_em 29.1 51.9 51.0 68.0 73.8
Tool Use API-Bank 0 acc 48.3 82.6 85.1 90.0 92.0
BFCL 0 acc 60.3 76.1 83.0 84.8 88.5
Gorilla Benchmark API Bench 0 acc 1.7 8.2 14.7 29.7 35.3
Nexus (0-shot) 0 macro_avg/acc 18.1 38.5 47.8 56.7 58.7
Multilingual Multilingual MGSM (CoT) 0 em - 68.9 - 86.9 91.6

Multilingual benchmarks

Category Benchmark Language Llama 3.1 8B Llama 3.1 70B Llama 3.1 405B
General MMLU (5-shot, macro_avg/acc) Portuguese 62.12 80.13 84.95
Spanish 62.45 80.05 85.08
Italian 61.63 80.4 85.04
German 60.59 79.27 84.36
French 62.34 79.82 84.66
Hindi 50.88 74.52 80.31
Thai 50.32 72.95 78.21
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