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claysauruswrecks/Giraffe-v2-13b-32k-GGUF
Giraffe-v2-13b-32k-GGUF is a machine learning model from claysauruswrecks. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as llama2.
- Model creator: Abacus.AI - Original model: Giraffe v2 7B 32K
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.gguf30.9 GB · 100%
From the Hugging Face model README
This repo contains GGUF format model files for Abacus.AI's Giraffe v2 7B 32K
These files were quantized on an Intel i9-9980HK with 32GB RAM.
<!-- description end --> <!-- README_GGUF.md-about-gguf start -->GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
These quantized GGUFv2 files are compatible with llama.cpp from November 1st, 2023 onwards, as of commit c43c2da
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
<!-- compatibility_gguf end -->I have only tested these in text-generation-webui with ctx=2048 and compress_pos_emb from 4 through 8.
TODO: Make sure the longer context is actually functional.
<!-- README_GGUF.md-provided-files start -->| Name | Quant method | Bits | Size | Use case |
|---|---|---|---|---|
| giraffe-v2-13b-32k.Q4_K_M.gguf | Q4_K_M | 4 | 7.4 GB | medium, balanced quality - recommended |
| giraffe-v2-13b-32k.Q5_K_M.gguf | Q5_K_M | 5 | 8.6 GB | large, very low quality loss - recommended |
| giraffe-v2-13b-32k.Q8_0.gguf | Q8_0 | 8 | 13.0 GB | very large, extremely low quality loss - not recommended unless as a treat |
Thanks to TheBloke for the README.md template.
<!-- footer end --> <!-- original-model-card start -->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-v2-13b-32k', scale=8)
<!-- original-model-card end -->