license: cc-by-sa-4.0
language:
- en
tags:
- text-generation-inference
pipeline_tag: text-generation
Original model card
Buy me a coffee if you like this project ;)
Description
GGML Format model files for This project.
inference
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
Original model card
LLongMA-2, a suite of Llama-2 models, trained at 8k context length using linear positional interpolation scaling. The model was trained in collaboration with Emozilla of NousResearch and Kaiokendev.
We worked directly with Kaiokendev, to extend the context length of the Llama-2 7b model through fine-tuning. The models pass all our evaluations and maintain the same perplexity at 8k extrapolation surpassing the performance of other recent methodologies.
The model has identical performance to LLaMA 2 under 4k context length, performance scales directly to 8k, and works out-of-the-box with the new version of transformers (4.31) or with trust_remote_code for <= 4.30.
A Llama-2 13b model trained at 8k will release soon on huggingface here: https://huggingface.co/conceptofmind/LLongMA-2-13b
Applying the method to the rotary position embedding requires only slight changes to the model's code by dividing the positional index, t, by a scaling factor.
The repository containing u/emozilla’s implementation of scaled rotary embeddings can be found here: https://github.com/jquesnelle/scaled-rope
If you would like to learn more about scaling rotary embeddings, I would strongly recommend reading u/kaiokendev's blog posts on his findings: https://kaiokendev.github.io/
A PR to add scaled rotary embeddings to Huggingface transformers has been added by u/joao_gante and merged: https://github.com/huggingface/transformers/pull/24653
The model was trained for ~1 billion tokens on Togethercompute's Red Pajama dataset. The context length of the examples varies: https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T
The pre-tokenized dataset will be available here for you to use soon: https://huggingface.co/datasets/conceptofmind/rp-llama-2-7b-tokenized-chunked
I would also recommend checking out the phenomenal research by Ofir Press on ALiBi which laid the foundation for many of these scaling techniques: https://arxiv.org/abs/2108.12409
It is also worth reviewing the paper, A Length-Extrapolatable Transformer, and xPos technique which also applies scaling to rotary embeddings: https://arxiv.org/pdf/2212.10554.pdf
We previously trained the first publicly available model with rotary embedding scaling here: https://twitter.com/EnricoShippole/status/1655599301454594049?s=20
A Llama-2 13b model trained at 8k will release soon. As well as a suite of Llama-2 models trained at 16k context lengths will be released soon.
You can find out more about the NousResearch organization here: https://huggingface.co/NousResearch
The compute for this model release is all thanks to the generous sponsorship by CarperAI, Emad Mostaque, and StabilityAI. This is not an official StabilityAI product.
If you have any questions about the data or model be sure to reach out and ask! I will try to respond promptly.
The previous suite of LLongMA model releases can be found here: https://twitter.com/EnricoShippole/status/1677346578720256000?s=20
All of the models can be found on Huggingface: https://huggingface.co/conceptofmind
You can find the Llama-2 usage policy here: https://ai.meta.com/llama/use-policy/
Llama 2 Community License Agreement
Llama 2 Version Release Date: July 18, 2023
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