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
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.
Runs of abacusai Llama-3-Giraffe-70B-Instruct on huggingface.co
25
Total runs
0
24-hour runs
1
3-day runs
5
7-day runs
15
30-day runs
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