has some basic details on our approach and metrics. We have also published a paper on arXiv
that covers our experiments and analysis a lot more comprehensively.
To use this model at longer lengths the model needs to be patched to interpolate the longer context
lengths. It will not work if it is simply loaded with the
AutoModel
framework of
transformers
.
For full details and usage see:
The evaluation section has detailed code for how to load and patch the model for inference (or further fine-tuning).
Note in particular the
max_position_embeddings
is not relevant since the patched module dynamically reallocates
the position buffers as required.
Using the code in the repository you can load this model with the following code:
from models import load_model, load_tokenizer
tokenizer = load_tokenizer()
model = load_model('abacusai/Giraffe-v2-70b-32k', scale=8)
Runs of abacusai Giraffe-v2-70b-32k on huggingface.co
125
Total runs
0
24-hour runs
2
3-day runs
24
7-day runs
35
30-day runs
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