---
language:
- en
pipeline_tag: text-generation
tags:
- meta
- llama-3
license: llama3
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png)


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/VcZWbW_eZkJAZZ5ricL4B.png)

# Llama-3-Giraffe-70B

Abacus.AI presents our longer-necked variant of Llama 3 70B!

This model has an effective context length of approximately 128k.

We have currently trained on ~1B tokens.
This is an initial release and we are hoping to improve the heatmap below further as we continue training.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/_NVEuQ2ZT-sBtDBNjgmbt.png)

## Training Methodology

The methodology for training uses [PoSE](https://arxiv.org/abs/2309.10400) and dynamic-NTK interpolation. 

### NTK-scaling

The scale factor for NTK is 4. Note that we also tried theta-scaling but this did not work as well as NTK scaling in our experiments.

### PoSE

We utilise Positional Skip-wise Training (PoSE) with the following parameters:

- **Number of Chunks**: 5
- **Max position ID**: 32768

### Data

We use on average ~8K long samples from [RedPajama](https://github.com/togethercomputer/RedPajama-Data).

### Hardware

We train on 8xH100 GPUs with Deepspeed Zero Stage 3.

## Evaluation Methodology

We use the [EasyContext](https://github.com/abacusai/EasyContext/blob/eval_runs/eval_needle.py) implementation of Needle-in-a-Haystack to evaluate Llama-3-Giraffe-70B.

We evaluate with the following parameters:

- **Min context length**: 2000
- **Max context length**: 128000
- **Context interval**: 4000
- **Depth interval**: 0.1
- **Num samples**: 2
- **Rnd number digits**: 7
- **Haystack dir**: PaulGrahamEssays