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Llama-3-70B-Instruct-Storywriter-exl2-rpcal

Name: Llama-3-70B-Instruct-Storywriter-exl2-rpcal
Availability: InStock

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Llama-3-70B-Instruct-Storywriter-exl2-rpcal

Quantized using 200 samples of 8192 tokens from an RP-oriented PIPPA dataset.

Branches:

main -- measurement.json
2.25b6h -- 2.25bpw, 6bit lm_head
3.5b6h -- 3.5bpw, 6bit lm_head
3.75b6h -- 3.75bpw, 6bit lm_head
4.5b6h -- 4.5bpw, 6bit lm_head
4.65b6h -- 4.65bpw, 6bit lm_head
6b6h -- 6bpw, 6bit lm_head
8b8h -- 8bpw, 8bit lm_head

Original model link: tdrussell/Llama-3-70B-Instruct-Storywriter

Original model README below.

Llama 3 70B Instruct Storywriter

Llama 3 70B Instruct, further finetuned on a dataset consisting of books in the fiction genre.

This was just an experiment, but it turned out well enough that I'm sharing it. The finetuning has caused a significant shift in the model's writing style, and seems to have made it more creative. There may be a slight decrease in overall intelligence.

Because this was trained on Instruct, you can use the normal Instruct chat formatting. It may also work well in raw completion mode.

Training details

Trained on 4 4090s using qlora-pipe. Dataset consists of about 800 books in the fiction genre, totaling 570 MB of raw text. Rank 64 QLoRA trained at 8192 sequence length.

Evaluation metrics

Why no 8B?

I tried multiple times to train this on Llama 3 8B Instruct, using a variety of hyperparameters. It never worked well. The model took a huge hit to intelligence every time, to the point of being unusable. 70B fared much better. I don't know why, maybe 8B is just too small for this type of technique, and loses too much of the instruction-tuned smarts.

Quant-Cartel/Llama-3-70B-Instruct-Storywriter-exl2-rpcal