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# LongLoRA and LongAlpaca for Long-context LLMs
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[](https://huggingface.co/Yukang)
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[](https://github.com/dvlab-research/LongLoRA)
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[](https://huggingface.co/datasets/Yukang/LongAlpaca-12k)
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[](https://arxiv.org/abs/2309.12307)
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[](https://github.com/dvlab-research/LongLoRA/blob/main/LICENSE)
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[](https://github.com/dvlab-research/LongLoRA/blob/main/DATA_LICENSE)
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[](https://github.com/dvlab-research/LongLoRA/blob/main/WEIGHT_LICENSE)
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For detailed usage and codes, please visit the [Github project](https://github.com/dvlab-research/LongLoRA).
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## TABLE OF CONTENTS
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1. [News](#news)
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2. [Examples](#examples)
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3. [Highlights](#highlights)
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4. [How to contribute](#how-to-contribute)
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5. [Requirements](#usage-requirements)
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6. [Installation and quick guide](#installation-and-quick-guide)
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7. [LongAlpaca Data](#longalpaca-data)
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8. [Models](#models)
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9. [Training](#training)
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10. [Evaluation](#evaluation)
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11. [Demo](#demo)
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12. [Data Generation via Pdf2Text](#data-generation-via-pdf2text)
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13. [Citation](#citation)
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14. [Acknowledgement](#acknowledgement)
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15. [License](#license)
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## News
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- [x] [2023.10.8] **We release the long instruction-following dataset**, [LongAlpaca-12k](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) and **the corresponding models**, [LongAlpaca-7B](https://huggingface.co/Yukang/LongAlpaca-7B), [LongAlpaca-13B](https://huggingface.co/Yukang/LongAlpaca-13B), and [LongAlpaca-70B](https://huggingface.co/Yukang/LongAlpaca-70B).
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- (*The previous sft models*, [Llama-2-13b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-13b-chat-longlora-32k-sft) and [Llama-2-70b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k-sft), *have been depreciated*.)
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- [x] [2023.10.3] We add support GPTNeoX models. Please refer to this [PR](https://github.com/dvlab-research/LongLoRA/pull/32) for usage. Thanks for @naubull2 for this contribution.
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- [x] [2023.9.22] We release all our fine-tuned [models](https://huggingface.co/Yukang), including **70B-32k models**, [LLaMA2-LongLoRA-70B-32k](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k), [LLaMA2-LongLoRA-7B-100k](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft). Welcome to check them out!
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- [x] [2023.9.22] We release [Paper](http://arxiv.org/abs/2309.12307) and this GitHub repo, including training and evaluation code.
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**LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models [[Paper](http://arxiv.org/abs/2309.12307)]** <br />
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[Yukang Chen](https://scholar.google.com/citations?user=6p0ygKUAAAAJ&hl=en),
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[Shengju Qian](https://scholar.google.com/citations?user=QNnWmasAAAAJ),
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[Haotian Tang](https://scholar.google.com/citations?user=WxL13BAAAAAJ&hl),
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[Xin Lai](https://scholar.google.com/citations?user=tqNDPA4AAAAJ&hl=zh-CN),
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[Zhijian Liu](https://scholar.google.com/citations?user=3coYSTUAAAAJ&hl=en),
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[Song Han](https://scholar.google.com/citations?user=E0iCaa4AAAAJ&hl=zh-CN),
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[Jiaya Jia](https://scholar.google.com/citations?user=XPAkzTEAAAAJ&hl=en)<br />
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## Highlights
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1. In LongLoRA approach, The proposed shifted short attention is easy to implement, compatible with Flash-Attention, and is not required during inference.
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2. We released all our models, including models from 7B to 70B, context length from 8k to 100k, including [LLaMA2-LongLoRA-7B-100k](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft), [LLaMA2-LongLoRA-13B-64k](https://huggingface.co/Yukang/Llama-2-13b-longlora-64k), and [LLaMA2-LongLoRA-70B-32k](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k).
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3. We built up a long-context instruction-following dataset, [LongAlpaca-12k](#longalpaca-data). We released the corresponding [LongAlpaca-7B](https://huggingface.co/Yukang/LongAlpaca-7B), [LongAlpaca-13B](https://huggingface.co/Yukang/LongAlpaca-13B) and [LongAlpaca-70B](https://huggingface.co/Yukang/LongAlpaca-70B) models. To our best knowledge, this is the first open-sourced long-context 70B model.
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## How to Contribute
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- Make sure to have git installed.
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- Create your own [fork](https://github.com/dvlab-research/LongLoRA/fork) of the project.
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- Clone the repository on your local machine, using git clone and pasting the url of this project.
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- Read both the `Requirements` and `Installation and Quick Guide` sections below.
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- Commit and push your changes.
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- Make a pull request when finished modifying the project.
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## Usage Requirements
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To download and use the [pre-trained weights](#pre-trained-weights) you will need:
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1. Hugging Face (HF) account with valid email. Note, the email used for HF must alse be used for the license agreement.
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2. Accept the Meta [license and acceptable use policy](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
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## Installation and Quick Guide
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To install and run the application:
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1. [Fork this repo](https://github.com/dvlab-research/LongLoRA/fork) on github
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2. Clone the repository on your local machine, using git clone and pasting the url of this project.
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3. Run the following code:
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```
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pip install -r requirements.txt
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pip install flash-attn --no-build-isolation
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```
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4. Use either a [Released model](#released-models) or [Fine tune](#fine-tuning) a model to fit your preferences.
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5. Test your model by chat.
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6. Deploy your own demo.
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## LongAlpaca Data
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LongAlpaca-12k contains 9k long QA data that we collected and 3k short QA sampled from the original [Alpaca data](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json). This is to avoid the case that the model might degrade at short instruction following. The data we collect contains various types and amounts as the following figure.
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| Data | Short QA | Long QA | Total | Download |
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|:---------------|----------|----------|----------|----------|
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| LongAlpaca-12k | 3k | 9k | 12k | [Link](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) |
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Following the original Alpaca format, our Long QA data uses the following prompts for fine-tuning:
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- `instruction`: `str`, describes the task the model should perform. For example, to answer a question after reading a book section or paper. We vary the contents and questions to make instructions diverse.
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- `output`: `str`, the answer to the instruction.
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We did not use the `input` format in the Alpaca format for simplicity.
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## Models
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### Models with supervised fine-tuning
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| Model | Size | Context | Train | Link |
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|:---------------|------|---------|---------|-----------------------------------------------------------------------------------------------------------------------|
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| LongAlpaca-7B | 7B | 32768 | Full FT | [Model](https://huggingface.co/Yukang/LongAlpaca-7B) |
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| LongAlpaca-13B | 13B | 32768 | Full FT | [Model](https://huggingface.co/Yukang/LongAlpaca-13B) |
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| LongAlpaca-70B | 70B | 32768 | LoRA+ | [Model](https://huggingface.co/Yukang/LongAlpaca-70B) [(LoRA-weight)](https://huggingface.co/Yukang/LongAlpaca-70B-lora) |
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### Models with context extension via fully fine-tuning
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| Model | Size | Context | Train | Link |
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|:----------------------------|------|---------|-------|-------------------------------------------------------------------|
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| Llama-2-7b-longlora-8k-ft | 7B | 8192 | Full FT | [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-8k-ft) |
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| Llama-2-7b-longlora-16k-ft | 7B | 16384 | Full FT | [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k-ft) |
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| Llama-2-7b-longlora-32k-ft | 7B | 32768 | Full FT | [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-32k-ft) |
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| Llama-2-7b-longlora-100k-ft | 7B | 100000 | Full FT | [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft) |
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| Llama-2-13b-longlora-8k-ft | 13B | 8192 | Full FT | [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-8k-ft) |
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| Llama-2-13b-longlora-16k-ft | 13B | 16384 | Full FT | [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-16k-ft) |
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| Llama-2-13b-longlora-32k-ft | 13B | 32768 | Full FT | [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-32k-ft) |
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### Models with context extension via improved LoRA fine-tuning
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| Model | Size | Context | Train | Link |
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|:----------------------------|------|---------|-------|---------------------------------------------------------------------|
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| Llama-2-7b-longlora-8k | 7B | 8192 | LoRA+ | [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-8k) |
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| Llama-2-7b-longlora-16k | 7B | 16384 | LoRA+ | [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k) |
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| Llama-2-7b-longlora-32k | 7B | 32768 | LoRA+ | [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-32k) |
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| Llama-2-13b-longlora-8k | 13B | 8192 | LoRA+ | [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-8k) |
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| Llama-2-13b-longlora-16k | 13B | 16384 | LoRA+ | [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-16k) |
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| Llama-2-13b-longlora-32k | 13B | 32768 | LoRA+ | [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-32k) |
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| Llama-2-13b-longlora-64k | 13B | 65536 | LoRA+ | [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-64k) |
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| Llama-2-70b-longlora-32k | 70B | 32768 | LoRA+ | [LoRA-weight](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k) |
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| Llama-2-70b-chat-longlora-32k | 70B | 32768 | LoRA+ | [LoRA-weight](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k) |
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## Training
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### Pre-trained weights
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We use LLaMA2 models as the pre-trained weights and fine-tune them to long context window sizes. Download based on your choices.
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| Pre-trained weights |
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|:-------------------------------------------------------------------------------------|
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| [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) |
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|[Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) |
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| [Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) |
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| [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) |
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| [Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) |
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| [Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) |
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This project also supports GPTNeoX models as the base model architecture. Some candidate pre-trained weights may include [GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b), [Polyglot-ko-12.8B](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) and other variants.
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### Fine-tuning
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```
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torchrun --nproc_per_node=8 fine-tune.py \
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--model_name_or_path path_to/Llama-2-7b-hf \
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--bf16 True \
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--output_dir path_to_saving_checkpoints \
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--cache_dir path_to_cache \
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--model_max_length 8192 \
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--use_flash_attn True \
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--low_rank_training False \
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--num_train_epochs 1 \
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--per_device_train_batch_size 1 \
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--per_device_eval_batch_size 2 \
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--gradient_accumulation_steps 8 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 1000 \
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--save_total_limit 2 \
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--learning_rate 2e-5 \
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--weight_decay 0.0 \
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--warmup_steps 20 \
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--lr_scheduler_type "constant_with_warmup" \
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--logging_steps 1 \
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--deepspeed "ds_configs/stage2.json" \
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--tf32 True \
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--max_steps 1000
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```
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- Please remember to change `path_to/Llama-2-7b-hf`, `path_to_saving_checkpoints`, `path_to_cache` to your own directory.
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- Note that you can change `model_max_length` to other values.
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- You could change `ds_configs/stage2.json` to `ds_configs/stage3.json` if you want.
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- Please set `use_flash_attn` as `False` if you use V100 machines or do not install flash attention.
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- You can set `low_rank_training` as `False` if you want to use fully fine-tuning. It will cost more GPU memory and slower, but the performance will be a bit better.
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- When training is finished, to get the full model weight:
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```
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cd path_to_saving_checkpoints && python zero_to_fp32.py . pytorch_model.bin
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```
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### Supervised Fine-tuning
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```
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torchrun --nproc_per_node=8 supervised-fine-tune.py \
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--model_name_or_path path_to_Llama2_chat_models \
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--bf16 True \
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--output_dir path_to_saving_checkpoints \
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--model_max_length 32768 \
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--use_flash_attn True \
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--data_path LongAlpaca-12k.json \
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--low_rank_training True \
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--num_train_epochs 3 \
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--per_device_train_batch_size 1 \
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--per_device_eval_batch_size 2 \
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--gradient_accumulation_steps 1 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 1000 \
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--save_total_limit 2 \
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--learning_rate 2e-5 \
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--weight_decay 0.0 \
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--warmup_steps 20 \
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--lr_scheduler_type "constant_with_warmup" \
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--logging_steps 1 \
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--deepspeed "ds_configs/stage2.json" \
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--tf32 True
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```
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- There is no need to make supervised fine-tuning upon the fine-tuned context extended models. It is all right to directly use base model as Llama2-chat models, as the amount of long instruction following data is enough for SFT.
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- Our long instruction following data can be found in [LongAlpaca-12k.json](https://huggingface.co/datasets/Yukang/LongAlpaca-12k).
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### Get trainable weights in low-rank training
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In low-rank training, we set embedding and normalization layers as trainable. Please use the following line to extract the trainable weights `trainable_params.bin` from `pytorch_model.bin`
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```
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python3 get_trainable_weights.py --checkpoint_path path_to_saving_checkpoints --trainable_params "embed,norm"
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```
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### Merge LoRA Weight
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Merge the LoRA weights of `pytorch_model.bin` and trainable parameters `trainable_params.bin`, save the resulting model into your desired path in the Hugging Face format:
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```
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python3 merge_lora_weights_and_save_hf_model.py \
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--base_model path_to/Llama-2-7b-hf \
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--peft_model path_to_saving_checkpoints \
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--context_size 8192 \
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--save_path path_to_saving_merged_model
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```
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For example,
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```
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python3 merge_lora_weights_and_save_hf_model.py \
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--base_model /dataset/pretrained-models/Llama-2-7b-hf \
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--peft_model /dataset/yukangchen/hf_models/lora-models/Llama-2-7b-longlora-8k \
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--context_size 8192 \
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--save_path /dataset/yukangchen/models/Llama-2-7b-longlora-8k-merged
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```
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## Evaluation
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### Perplexity Validation
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To evaluate a model that is trained in the low-rank setting, please set both `base_model` and `peft_model`. `base_model` is the pre-trained weight. `peft_model` is the path to the saved checkpoint, which should contain `trainable_params.bin`, `adapter_model.bin` and `adapter_config.json`. For example,
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```
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python3 eval.py --seq_len 8192 --context_size 8192 --batch_size 1 --base_model path_to/Llama-2-7b-hf --peft_model path_to_saving_checkpoints --data_path pg19/test.bin
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```
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|
||||
To evaluate a model that is fully fine-tuned, you only need to set `base_model` as the path to the saved checkpoint, which should contain `pytorch_model.bin` and `config.json`. `peft_model` should be ignored.
|
||||
```
|
||||
python3 eval.py --seq_len 8192 --context_size 8192 --batch_size 1 --base_model path_to_saving_checkpoints --data_path pg19/test.bin
|
||||
```
|
||||
|
||||
- Note that `--seq_len` is to set the sequence length for evaluation. `--context_size` is to set the context length of the model during fine-tuning. `--seq_len` should not be larger than `--context_size`.
|
||||
|
||||
- We have already tokenized the validation and test splits of PG19 and proof-pile dataset into `pg19/validation.bin`, `pg19/test.bin`, and `proof-pile/test_sampled_data.bin`, with the tokenizer of LLaMA. `proof-pile/test_sampled_data.bin` contains 128 documents that are randomly sampled from the total proof-pile test split. For each document, it has at least 32768 tokens. We also release the sampled ids in [proof-pile/test_sampled_ids.bin](https://drive.google.com/file/d/1cnzWODLRQYAd7HeugzLCIhaqzaLZv7J5/view?usp=share_link). You can download them from the links below.
|
||||
|
||||
| Dataset | Split | Link |
|
||||
|:-----------|------------|--------------------------------------------------------------------------------------------------------------|
|
||||
| PG19 | validation | [pg19/validation.bin](https://drive.google.com/file/d/1rbJvb0qRIf2mQoN2ON7S93TbTzMnlrN6/view?usp=share_link) |
|
||||
| PG19 | test | [pg19/test.bin](https://drive.google.com/file/d/1QANDMdctpacPAYgS04adDXqByGEq-Ret/view?usp=share_link) |
|
||||
| Proof-pile | test | [proof-pile/test_sampled_data.bin](https://drive.google.com/file/d/1bUI5lPDvrqzY_XXJJ2sSuvZx0Y9AZClE/view?usp=share_link) |
|
||||
|
||||
|
||||
### Passkey Retrieval
|
||||
We provide a manner to test the passkey retrieval accuracy. For example,
|
||||
```
|
||||
python3 passkey_retrivial.py \
|
||||
--context_size 32768 \
|
||||
--base_model path_to/Llama-2-7b-longlora-32k \
|
||||
--max_tokens 32768 \
|
||||
--interval 1000
|
||||
```
|
||||
- Note that the `context_size` is the context length during fine-tuning.
|
||||
- `max_tokens` is maximum length for the document in passkey retrieval evaluation.
|
||||
- `interval` is the interval during the document length increasing. It is a rough number because the document increases by sentences.
|
||||
|
||||
## Demo
|
||||
### Local Inference
|
||||
To chat with [Llama-2-13b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-13b-chat-longlora-32k-sft) or [Llama-2-70b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k-sft), you need to run `merge_lora_weights_and_save_hf_model.py` first, and then:
|
||||
```
|
||||
python3 inference.py \
|
||||
--base_model path_to_model \
|
||||
--question $question \
|
||||
--context_size $context_length \
|
||||
--max_gen_len $max_gen_len \
|
||||
--flash_attn True \
|
||||
--material $material_content \
|
||||
--material_type $material_type \
|
||||
--material_title $material_title
|
||||
```
|
||||
To ask a question related to a book:
|
||||
```
|
||||
python3 inference.py \
|
||||
--base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \
|
||||
--question "Why doesn't Professor Snape seem to like Harry?" \
|
||||
--context_size 32768 \
|
||||
--max_gen_len 512 \
|
||||
--flash_attn True \
|
||||
--material "materials/Harry Potter and the Philosophers Stone_section2.txt" \
|
||||
--material_type "book" \
|
||||
--material_title "Harry Potter and the Philosophers Stone"
|
||||
```
|
||||
Note that you can ignore `material_type` or `material_title`.
|
||||
|
||||
To ask a question related to a paper:
|
||||
```
|
||||
python3 inference.py \
|
||||
--base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \
|
||||
--question "What are the main contributions and novelties of this work?" \
|
||||
--context_size 32768 \
|
||||
--max_gen_len 512 \
|
||||
--flash_attn True \
|
||||
--material "materials/paper1.txt" \
|
||||
--material_type "paper"
|
||||
```
|
||||
|
||||
### Online Demo
|
||||
To deploy your own demo run
|
||||
```
|
||||
python3 demo.py \
|
||||
--base_model path_to_model \
|
||||
--context_size $context_size \
|
||||
--max_gen_len $max_gen_len \
|
||||
--flash_attn True
|
||||
```
|
||||
Example
|
||||
```
|
||||
python3 demo.py \
|
||||
--base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \
|
||||
--context_size 32768 \
|
||||
--max_gen_len 512 \
|
||||
--flash_attn True
|
||||
```
|
||||
- Note that `flash_attn=True` will make the generation slow but save much GPU memory.
|
||||
|
||||
## Data Generation via Pdf2text
|
||||
During our dataset collection, we convert paper and books from pdf to text. The conversion quality has a large influence on the final model quality. We think that this step is non-trivial. We release the tool for the pdf2txt conversion, in the folder `pdf2txt`. It is built upon `pdf2image`, `easyocr`, `ditod` and `detectron2`. Please refer to the [README.md](pdf2txt/README.md) in `pdf2txt` for more details.
|
||||
|
||||
## Citation
|
||||
If you find this project useful in your research, please consider citing:
|
||||
|
||||
```
|
||||
@article{longlora,
|
||||
title={LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models},
|
||||
author={Yukang Chen and Shengju Qian and Haotian Tang and Xin Lai and Zhijian Liu and Song Han and Jiaya Jia},
|
||||
journal={arXiv:2309.12307},
|
||||
year={2023}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
```
|
||||
@misc{long-alpaca,
|
||||
author = {Yukang Chen and Shaozuo Yu and Shengju Qian and Haotian Tang and Xin Lai and Zhijian Liu and Song Han and Jiaya Jia},
|
||||
title = {Long Alpaca: Long-context Instruction-following models},
|
||||
year = {2023},
|
||||
publisher = {GitHub},
|
||||
journal = {GitHub repository},
|
||||
howpublished = {\url{https://github.com/dvlab-research/LongLoRA}},
|
||||
}
|
||||
```
|
||||
## Acknowledgement
|
||||
- This work is built upon the [LLaMA2](https://ai.meta.com/llama) as the pre-trained models.
|
||||
- This work can also be built upon the [GPTNeoX-HF](https://huggingface.co/docs/transformers/model_doc/gpt_neox) which is based upon [EleutherAI/GPTNeoX](https://github.com/EleutherAI/gpt-neox) as the pre-trained model architecture.
|
||||
- This work is based on [DeepSpeed](https://github.com/microsoft/DeepSpeed), [peft](https://github.com/huggingface/peft), and [Flash-Attention2](https://github.com/Dao-AILab/flash-attention) for acceleration.
|
||||
- Some evaluation code is modified upon [Landmark Attention](https://github.com/epfml/landmark-attention).
|
||||
- We use [LongChat](https://github.com/DachengLi1/LongChat) for the retrieval evaluation.
|
||||
|
||||
## License
|
||||
- LongLoRA is licensed under the Apache License 2.0. This means that it requires the preservation of copyright and license notices.
|
||||
- Data and weights are under CC-BY-NC 4.0 License. They are licensed for research use only, and allowed only non-commercial. Models trained using the dataset should not be used outside of research purposes.
|
||||
3
added_tokens.json
Normal file
3
added_tokens.json
Normal file
@@ -0,0 +1,3 @@
|
||||
{
|
||||
"[PAD]": 32000
|
||||
}
|
||||
29
config.json
Normal file
29
config.json
Normal file
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"_name_or_path": "meta-llama/Llama-2-7b-chat-hf",
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 11008,
|
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"max_position_embeddings": 4096,
|
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"model_type": "llama",
|
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"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 32,
|
||||
"pad_token_id": 0,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"factor": 8.0,
|
||||
"type": "linear"
|
||||
},
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.31.0",
|
||||
"use_cache": false,
|
||||
"vocab_size": 32001
|
||||
}
|
||||
10
generation_config.json
Normal file
10
generation_config.json
Normal file
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"bos_token_id": 1,
|
||||
"do_sample": true,
|
||||
"eos_token_id": 2,
|
||||
"max_length": 4096,
|
||||
"pad_token_id": 0,
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.31.0"
|
||||
}
|
||||
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
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version https://git-lfs.github.com/spec/v1
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3
model-00002-of-00002.safetensors
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3
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Normal file
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330
model.safetensors.index.json
Normal file
330
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Normal file
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|
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||||
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|
||||
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|
||||
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
93412
tokenizer.json
Normal file
93412
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
33
tokenizer_config.json
Normal file
33
tokenizer_config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"legacy": false,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": null,
|
||||
"padding_side": "right",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user