---
license: llama3
library_name: transformers
tags: []
---

# Dracarys-Llama-3.1-70B-Instruct

### Built with Meta Llama 3

# Introduction

We introduce the latest in the Smaug series, the Dracarys family of finetunes targeting coding performance improvements
across a variety of base models.

This variant is a finetune of [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct)

Compared to meta-llama/Meta-Llama-3.1-70B-Instruct, Dracarys has better LiveCodeBench scores (see evaluation results below).

### Model Description

- **Developed by:** [Abacus.AI](https://abacus.ai)
- **License:** https://llama.meta.com/llama3/license/
- **Finetuned from model:** [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct).

## How to use

The prompt format is unchanged from Llama 3 70B Instruct (see evaluations for prompt details for LCB)

### Use with transformers

See the snippet below for usage with Transformers:

```python
import transformers
import torch

model_id = "abacusai/Dracarys-72B-Instruct"

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

messages = [
    {"role": "system", "content": "You are data science coding assistant that generates Python code using Pandas and Numpy."},
    {"role": "user", "content": "Write code to select rows from the dataframe `df` having the maximum `temp` for each `city`"},
]

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|>"),
    pipeline.tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
]

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 Results


## LiveCodeBench

| Model                               | Code Generation | Code Execution |Test Output Prediction |
|-------------------------------------|-----------------|----------------|-----------------------|
| **Dracarys-Llama-3.1-70B-Instruct** | **33.34**       | **48.329**     | **49.90**             |
| Meta-Llama-3.1-70B-Instruct         | 32.23           | 48.768         | 41.40                 |

## Breakdown of LiveCodeBench CodeGeneration

| Model                               | Easy            | Medium         | Hard                  |
|-------------------------------------|-----------------|----------------|-----------------------|
| **Dracarys-Llama-3.1-70B-Instruct** | **71.89**       | 17.30          | **4.23**              |
| Meta-Llama-3.1-70B-Instruct         | 68.4            | 17.99          | 3.57                  |

## Breakdown of LiveCodeBench TestOutputPrediction

| Model                               | Easy            | Medium         | Hard                  |
|-------------------------------------|-----------------|----------------|-----------------------|
| **Dracarys-Llama-3.1-70B-Instruct** | **60.88**       | **44.53**      | **39.30**             |
| Meta-Llama-3.1-70B-Instruct         | 51.22           | 35.91          | 34.30                 |

## LiveBench(July update)

| Model                               | Global Average | Coding Average | Reasoning Average| Mathematics Average | Data Analysis Average | Language Average | IF Average  |
|-------------------------------------|----------------|----------------|------------------|---------------------|-----------------------|------------------|-------------|
| **Dracarys-Llama-3.1-70B-Instruct** | **48.67**      | **35.23**      | **44.0**         | **45.68**           | 48                    | 41.77            | 77.37       |
| Meta-Llama-3.1-70B-Instruct         | 48.44          | 32.67          | 40.67            | 45.58               | 50.29                 | 42.36            | 79.08       |