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abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2

Text Generation
Transformers
Safetensors
llama
conversational
text-generation-inference
Inference Endpoints
3-bit
exl2
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metadata
library_name: transformers
license: llama2
datasets:
  - aqua_rat
  - microsoft/orca-math-word-problems-200k
  - m-a-p/CodeFeedback-Filtered-Instruction

Exllamav2 quant (exl2 / 3.0 bpw) made with ExLlamaV2 v0.0.21

Other EXL2 quants:

Quant Model Size lm_head
2.2
20886 MB
6
2.5
23192 MB
6
3.0
27273 MB
6
3.5
31356 MB
6
3.75
33398 MB
6
4.0
35434 MB
6
4.25
37456 MB
6
5.0
43598 MB
6
6.0
51957 MB
8
6.5
56014 MB
8
8.0
60211 MB
8

Smaug-Llama-3-70B-Instruct

Built with Meta Llama 3

image/png

This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to meta-llama/Meta-Llama-3-70B-Instruct.

The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).

EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below.

We are conducting additional benchmark evaluations and will add those when available.

Model Description

Evaluation

Arena-Hard

Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge))

Model Score 95% Confidence Interval Average Tokens
GPT-4-Turbo-2024-04-09 82.6 (-1.8, 1.6) 662
Claude-3-Opus-20240229 60.4 (-3.3, 2.4) 541
Smaug-Llama-3-70B-Instruct 56.7 (-2.2, 2.6) 661
GPT-4-0314 50.0 (-0.0, 0.0) 423
Claude-3-Sonnet-20240229 46.8 (-2.1, 2.2) 552
Llama-3-70B-Instruct 41.1 (-2.5, 2.4) 583
GPT-4-0613 37.9 (-2.2, 2.0) 354
Mistral-Large-2402 37.7 (-1.9, 2.6) 400
Mixtral-8x22B-Instruct-v0.1 36.4 (-2.7, 2.9) 430
Qwen1.5-72B-Chat 36.1 (-2.5, 2.2) 474
Command-R-Plus 33.1 (-2.1, 2.2) 541
Mistral-Medium 31.9 (-2.3, 2.4) 485
GPT-3.5-Turbo-0613 24.8 (-1.6, 2.0) 401

MT-Bench 

########## First turn ##########
                   score
model             turn
Smaug-Llama-3-70B-Instruct         1     9.40000                                                                                                                            
GPT-4-Turbo                        1     9.37500
Meta-Llama-3-70B-Instruct          1     9.21250 
########## Second turn ##########
                   score
model             turn
Smaug-Llama-3-70B-Instruct         2     9.0125
GPT-4-Turbo                        2     9.0000
Meta-Llama-3-70B-Instruct          2     8.8000
########## Average ##########
                 score
model
Smaug-Llama-3-70B-Instruct          9.206250
GPT-4-Turbo                         9.187500
Meta-Llama-3-70B-Instruct           9.006250
Model First turn Second Turn Average
Smaug-Llama-3-70B-Instruct 9.40 9.01 9.21
GPT-4-Turbo 9.38 9.00 9.19
Meta-Llama-3-70B-Instruct 9.21 8.80 9.01

This version of Smaug uses new techniques and new data compared to Smaug-72B, and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228.