Text Generation
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Safetensors
llama
conversational
text-generation-inference
Inference Endpoints
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metadata
library_name: transformers
license: llama3
datasets:
  - aqua_rat
  - microsoft/orca-math-word-problems-200k
  - m-a-p/CodeFeedback-Filtered-Instruction

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

How to use

The prompt format is unchanged from Llama 3 70B Instruct.

Use with transformers

See the snippet below for usage with Transformers:

import transformers
import torch

model_id = "abacusai/Smaug-Llama-3-70B-Instruct"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

prompt = pipeline.tokenizer.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])

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)). GPT-4o and Gemini-1.5-pro-latest were missing from the original blob post, and we produced those numbers from a local run using the same methodology.

Model Score 95% Confidence Interval Average Tokens
GPT-4-Turbo-2024-04-09 82.6 (-1.8, 1.6) 662
GPT-4o 78.3 (-2.4, 2.1) 685
Gemini-1.5-pro-latest 72.1 (-2.3, 2.2) 630
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

OpenLLM Leaderboard Manual Evaluation

Model ARC Hellaswag MMLU TruthfulQA Winogrande GSM8K* Average
Smaug-Llama-3-70B-Instruct 70.6 86.1 79.2 62.5 83.5 90.5 78.7
Llama-3-70B-Instruct 71.4 85.7 80.0 61.8 82.9 91.1 78.8

GSM8K The GSM8K numbers quoted here are computed using a recent release of the LM Evaluation Harness. The commit used by the leaderboard has a significant issue that impacts models that tend to use : in their responses due to a bug in the stop word configuration for GSM8K. The issue is covered in more detail in this GSM8K evaluation discussion. The score for both Llama-3 and this model are significantly different when evaluated with the updated harness as the issue with stop words has been addressed.

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.