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RichardErkhov/abacusai_-_Giraffe-v2-70b-32k-gguf
abacusai_-_Giraffe-v2-70b-32k-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
209
3% of all-time downloads
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.gguf902 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
Giraffe-v2-70b-32k - GGUF
| Name | Quant method | Size |
|---|---|---|
| Giraffe-v2-70b-32k.Q2_K.gguf | Q2_K | 23.71GB |
| Giraffe-v2-70b-32k.IQ3_XS.gguf | IQ3_XS | 26.37GB |
| Giraffe-v2-70b-32k.IQ3_S.gguf | IQ3_S | 27.86GB |
| Giraffe-v2-70b-32k.Q3_K_S.gguf | Q3_K_S | 27.86GB |
| Giraffe-v2-70b-32k.IQ3_M.gguf | IQ3_M | 28.82GB |
| Giraffe-v2-70b-32k.Q3_K.gguf | Q3_K | 30.99GB |
| Giraffe-v2-70b-32k.Q3_K_M.gguf | Q3_K_M | 30.99GB |
| Giraffe-v2-70b-32k.Q3_K_L.gguf | Q3_K_L | 33.67GB |
| Giraffe-v2-70b-32k.IQ4_XS.gguf | IQ4_XS | 34.64GB |
| Giraffe-v2-70b-32k.Q4_0.gguf | Q4_0 | 36.2GB |
| Giraffe-v2-70b-32k.IQ4_NL.gguf | IQ4_NL | 36.55GB |
| Giraffe-v2-70b-32k.Q4_K_S.gguf | Q4_K_S | 36.55GB |
| Giraffe-v2-70b-32k.Q4_K.gguf | Q4_K | 38.58GB |
| Giraffe-v2-70b-32k.Q4_K_M.gguf | Q4_K_M | 38.58GB |
| Giraffe-v2-70b-32k.Q4_1.gguf | Q4_1 | 40.2GB |
| Giraffe-v2-70b-32k.Q5_0.gguf | Q5_0 | 44.2GB |
| Giraffe-v2-70b-32k.Q5_K_S.gguf | Q5_K_S | 44.2GB |
| Giraffe-v2-70b-32k.Q5_K.gguf | Q5_K | 45.41GB |
| Giraffe-v2-70b-32k.Q5_K_M.gguf | Q5_K_M | 45.41GB |
| Giraffe-v2-70b-32k.Q5_1.gguf | Q5_1 | 48.2GB |
| Giraffe-v2-70b-32k.Q6_K.gguf | Q6_K | 52.7GB |
| Giraffe-v2-70b-32k.Q8_0.gguf | Q8_0 | 68.26GB |
tags:

We have followed up on our previous training runs related to extending the context length of Llama models. The associated github repository
https://github.com/abacusai/long-context
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.
http://arxiv.org/abs/2308.10882
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:
https://github.com/abacusai/Long-Context
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.
The tokenizer corresponding to this model is https://huggingface.co/abacusai/Giraffe-v1-Tokenizer.
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)