---
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](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/ZxYuHKmU_AtuEJbGtuEBC.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](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))

| 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](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.