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.
Training Methodology
The methodology for training uses PoSE 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.
Hardware
We train on 8xH100 GPUs with Deepspeed Zero Stage 3.
Evaluation Methodology
We use the EasyContext 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