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---
license: llama3
library_name: transformers
datasets:
- aqua_rat
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
model-index:
- name: Smaug-Llama-3-70B-Instruct
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: ENEM Challenge (No Images)
type: eduagarcia/enem_challenge
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 77.89
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BLUEX (No Images)
type: eduagarcia-temp/BLUEX_without_images
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 69.54
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: OAB Exams
type: eduagarcia/oab_exams
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 63.64
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 RTE
type: assin2
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 93.62
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 STS
type: eduagarcia/portuguese_benchmark
split: test
args:
num_few_shot: 15
metrics:
- type: pearson
value: 78.52
name: pearson
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: FaQuAD NLI
type: ruanchaves/faquad-nli
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 80.01
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HateBR Binary
type: ruanchaves/hatebr
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 91.78
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: PT Hate Speech Binary
type: hate_speech_portuguese
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 68.36
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: tweetSentBR
type: eduagarcia/tweetsentbr_fewshot
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 70.29
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct
name: Open Portuguese LLM Leaderboard
---
# Smaug-Llama-3-70B-Instruct
### Built with Meta Llama 3
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](https://huggingface.co/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
- **Developed by:** [Abacus.AI](https://abacus.ai)
- **License:** https://llama.meta.com/llama3/license/
- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
## How to use
The prompt format is unchanged from Llama 3 70B Instruct.
### Use with transformers
See the snippet below for usage with Transformers:
```python
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](https://github.com/EleutherAI/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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard/discussions/770).
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](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228.
# Open Portuguese LLM Leaderboard Evaluation Results
Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/abacusai/Smaug-Llama-3-70B-Instruct) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
| Metric | Value |
|--------------------------|---------|
|Average |**77.07**|
|ENEM Challenge (No Images)| 77.89|
|BLUEX (No Images) | 69.54|
|OAB Exams | 63.64|
|Assin2 RTE | 93.62|
|Assin2 STS | 78.52|
|FaQuAD NLI | 80.01|
|HateBR Binary | 91.78|
|PT Hate Speech Binary | 68.36|
|tweetSentBR | 70.29|
|