---
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-Instruct

Abacus.AI presents our longer-necked variant of Llama 3 70B - now with the instruct variant!

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

We have currently trained on ~1.5B tokens.

There are our Needle-in-a-Haystack heatmap results. We are conducting further evals of model efficacy and will update our model card as these come in:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/Z4uUhcjgf1P7EPGQyRLkW.png)

### MT-Bench Evaluation

We also measured performance on MT-Bench to verify that the context extension did not significantly impact performance on instruct tasks:

```
####### 1st turn:
Meta-Llama-3-70B-Instruct      9.21
Llama-3-Giraffe-70B-Instruct 9.19

####### 2nd turn:
Meta-Llama-3-70B-Instruct     2   8.80
Llama-3-Giraffe-70B-Instruct 2   8.54

####### average:
Meta-Llama-3-70B-Instruct      9.00
Llama-3-Giraffe-70B-Instruct 8.87 
```

## 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


### Adapter Transfer

We apply the above techniques first to Llama-3-70B-Base, using LoRA on the Q and K weights only. This adapter is then applied to Llama-3-70B-Instruct, and we
release the merged version here.