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Model: TheBloke/AquilaChat2-34B-16K-GPTQ Source: Original Platform
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BAAI-Aquila-Model-License -Agreement.pdf
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---
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base_model: BAAI/AquilaChat2-34B-16K
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inference: false
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license: other
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model_creator: Beijing Academy of Artificial Intelligence
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model_name: Aquilachat2 34B 16K
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model_type: aquila
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prompt_template: 'System: A chat between a curious human and an artificial intelligence
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assistant. The assistant gives helpful, detailed, and polite answers to the human''s
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questions.
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Human: {prompt}
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Assistant:
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'
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quantized_by: TheBloke
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---
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<!-- markdownlint-disable MD041 -->
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<!-- header start -->
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<!-- 200823 -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
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</div>
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<div style="display: flex; flex-direction: column; align-items: flex-end;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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</div>
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</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
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<!-- header end -->
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# Aquilachat2 34B 16K - GPTQ
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- Model creator: [Beijing Academy of Artificial Intelligence](https://huggingface.co/BAAI)
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- Original model: [Aquilachat2 34B 16K](https://huggingface.co/BAAI/AquilaChat2-34B-16K)
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<!-- description start -->
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## Description
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This repo contains GPTQ model files for [Beijing Academy of Artificial Intelligence's Aquilachat2 34B 16K](https://huggingface.co/BAAI/AquilaChat2-34B-16K).
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Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
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These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
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<!-- description end -->
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<!-- repositories-available start -->
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## Repositories available
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* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-AWQ)
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GPTQ)
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GGUF)
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* [Beijing Academy of Artificial Intelligence's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/BAAI/AquilaChat2-34B-16K)
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<!-- repositories-available end -->
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<!-- prompt-template start -->
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## Prompt template: AquilaChat
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```
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System: A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
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Human: {prompt}
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Assistant:
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```
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<!-- prompt-template end -->
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<!-- README_GPTQ.md-compatible clients start -->
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## Known compatible clients / servers
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These GPTQ models are known to work in the following inference servers/webuis.
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- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
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- [KobaldAI United](https://github.com/henk717/koboldai)
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- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
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- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
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This may not be a complete list; if you know of others, please let me know!
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<!-- README_GPTQ.md-compatible clients end -->
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<!-- README_GPTQ.md-provided-files start -->
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## Provided files, and GPTQ parameters
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Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
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Each separate quant is in a different branch. See below for instructions on fetching from different branches.
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Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
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<details>
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<summary>Explanation of GPTQ parameters</summary>
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- Bits: The bit size of the quantised model.
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- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
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- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
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- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
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- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
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- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
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- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
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</details>
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| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
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| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
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| [main](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 9.95 GB | No | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
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| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 10.00 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
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| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 10.00 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
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| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 9.99 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
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| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 9.98 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
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| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 10.00 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
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| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 9.94 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
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<!-- README_GPTQ.md-provided-files end -->
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<!-- README_GPTQ.md-download-from-branches start -->
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## How to download, including from branches
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### In text-generation-webui
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To download from the `main` branch, enter `TheBloke/AquilaChat2-34B-16K-GPTQ` in the "Download model" box.
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To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/AquilaChat2-34B-16K-GPTQ:gptq-4bit-128g-actorder_True`
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### From the command line
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I recommend using the `huggingface-hub` Python library:
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```shell
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pip3 install huggingface-hub
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```
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To download the `main` branch to a folder called `AquilaChat2-34B-16K-GPTQ`:
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```shell
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mkdir AquilaChat2-34B-16K-GPTQ
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huggingface-cli download TheBloke/AquilaChat2-34B-16K-GPTQ --local-dir AquilaChat2-34B-16K-GPTQ --local-dir-use-symlinks False
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```
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To download from a different branch, add the `--revision` parameter:
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```shell
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mkdir AquilaChat2-34B-16K-GPTQ
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huggingface-cli download TheBloke/AquilaChat2-34B-16K-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir AquilaChat2-34B-16K-GPTQ --local-dir-use-symlinks False
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```
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<details>
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<summary>More advanced huggingface-cli download usage</summary>
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If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
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The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
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For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
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To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
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```shell
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pip3 install hf_transfer
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```
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And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
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```shell
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mkdir AquilaChat2-34B-16K-GPTQ
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HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/AquilaChat2-34B-16K-GPTQ --local-dir AquilaChat2-34B-16K-GPTQ --local-dir-use-symlinks False
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```
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Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
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</details>
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### With `git` (**not** recommended)
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To clone a specific branch with `git`, use a command like this:
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```shell
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git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/AquilaChat2-34B-16K-GPTQ
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```
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Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
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<!-- README_GPTQ.md-download-from-branches end -->
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<!-- README_GPTQ.md-text-generation-webui start -->
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## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
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Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
|
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It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
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1. Click the **Model tab**.
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2. Under **Download custom model or LoRA**, enter `TheBloke/AquilaChat2-34B-16K-GPTQ`.
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- To download from a specific branch, enter for example `TheBloke/AquilaChat2-34B-16K-GPTQ:gptq-4bit-128g-actorder_True`
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- see Provided Files above for the list of branches for each option.
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3. Click **Download**.
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4. The model will start downloading. Once it's finished it will say "Done".
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5. In the top left, click the refresh icon next to **Model**.
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6. In the **Model** dropdown, choose the model you just downloaded: `AquilaChat2-34B-16K-GPTQ`
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7. The model will automatically load, and is now ready for use!
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8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
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- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
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9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
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<!-- README_GPTQ.md-text-generation-webui end -->
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<!-- README_GPTQ.md-use-from-tgi start -->
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## Serving this model from Text Generation Inference (TGI)
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It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
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Example Docker parameters:
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|
```shell
|
||||||
|
--model-id TheBloke/AquilaChat2-34B-16K-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
|
||||||
|
```
|
||||||
|
|
||||||
|
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
|
||||||
|
|
||||||
|
```shell
|
||||||
|
pip3 install huggingface-hub
|
||||||
|
```
|
||||||
|
|
||||||
|
```python
|
||||||
|
from huggingface_hub import InferenceClient
|
||||||
|
|
||||||
|
endpoint_url = "https://your-endpoint-url-here"
|
||||||
|
|
||||||
|
prompt = "Tell me about AI"
|
||||||
|
prompt_template=f'''System: A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
|
||||||
|
Human: {prompt}
|
||||||
|
Assistant:
|
||||||
|
'''
|
||||||
|
|
||||||
|
client = InferenceClient(endpoint_url)
|
||||||
|
response = client.text_generation(prompt,
|
||||||
|
max_new_tokens=128,
|
||||||
|
do_sample=True,
|
||||||
|
temperature=0.7,
|
||||||
|
top_p=0.95,
|
||||||
|
top_k=40,
|
||||||
|
repetition_penalty=1.1)
|
||||||
|
|
||||||
|
print(f"Model output: {response}")
|
||||||
|
```
|
||||||
|
<!-- README_GPTQ.md-use-from-tgi end -->
|
||||||
|
<!-- README_GPTQ.md-use-from-python start -->
|
||||||
|
## How to use this GPTQ model from Python code
|
||||||
|
|
||||||
|
### Install the necessary packages
|
||||||
|
|
||||||
|
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
|
||||||
|
|
||||||
|
```shell
|
||||||
|
pip3 install transformers optimum
|
||||||
|
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
|
||||||
|
```
|
||||||
|
|
||||||
|
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
|
||||||
|
|
||||||
|
```shell
|
||||||
|
pip3 uninstall -y auto-gptq
|
||||||
|
git clone https://github.com/PanQiWei/AutoGPTQ
|
||||||
|
cd AutoGPTQ
|
||||||
|
git checkout v0.4.2
|
||||||
|
pip3 install .
|
||||||
|
```
|
||||||
|
|
||||||
|
### You can then use the following code
|
||||||
|
|
||||||
|
```python
|
||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
|
||||||
|
|
||||||
|
model_name_or_path = "TheBloke/AquilaChat2-34B-16K-GPTQ"
|
||||||
|
# To use a different branch, change revision
|
||||||
|
# For example: revision="gptq-4bit-128g-actorder_True"
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
|
||||||
|
device_map="auto",
|
||||||
|
trust_remote_code=True,
|
||||||
|
revision="main")
|
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
|
||||||
|
|
||||||
|
prompt = "Tell me about AI"
|
||||||
|
prompt_template=f'''System: A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
|
||||||
|
Human: {prompt}
|
||||||
|
Assistant:
|
||||||
|
'''
|
||||||
|
|
||||||
|
print("\n\n*** Generate:")
|
||||||
|
|
||||||
|
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
|
||||||
|
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
|
||||||
|
print(tokenizer.decode(output[0]))
|
||||||
|
|
||||||
|
# Inference can also be done using transformers' pipeline
|
||||||
|
|
||||||
|
print("*** Pipeline:")
|
||||||
|
pipe = pipeline(
|
||||||
|
"text-generation",
|
||||||
|
model=model,
|
||||||
|
tokenizer=tokenizer,
|
||||||
|
max_new_tokens=512,
|
||||||
|
do_sample=True,
|
||||||
|
temperature=0.7,
|
||||||
|
top_p=0.95,
|
||||||
|
top_k=40,
|
||||||
|
repetition_penalty=1.1
|
||||||
|
)
|
||||||
|
|
||||||
|
print(pipe(prompt_template)[0]['generated_text'])
|
||||||
|
```
|
||||||
|
<!-- README_GPTQ.md-use-from-python end -->
|
||||||
|
|
||||||
|
<!-- README_GPTQ.md-compatibility start -->
|
||||||
|
## Compatibility
|
||||||
|
|
||||||
|
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
|
||||||
|
|
||||||
|
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
|
||||||
|
|
||||||
|
For a list of clients/servers, please see "Known compatible clients / servers", above.
|
||||||
|
<!-- README_GPTQ.md-compatibility end -->
|
||||||
|
|
||||||
|
<!-- footer start -->
|
||||||
|
<!-- 200823 -->
|
||||||
|
## Discord
|
||||||
|
|
||||||
|
For further support, and discussions on these models and AI in general, join us at:
|
||||||
|
|
||||||
|
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
|
||||||
|
|
||||||
|
## Thanks, and how to contribute
|
||||||
|
|
||||||
|
Thanks to the [chirper.ai](https://chirper.ai) team!
|
||||||
|
|
||||||
|
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
|
||||||
|
|
||||||
|
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
|
||||||
|
|
||||||
|
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
|
||||||
|
|
||||||
|
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
|
||||||
|
|
||||||
|
* Patreon: https://patreon.com/TheBlokeAI
|
||||||
|
* Ko-Fi: https://ko-fi.com/TheBlokeAI
|
||||||
|
|
||||||
|
**Special thanks to**: Aemon Algiz.
|
||||||
|
|
||||||
|
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
|
||||||
|
|
||||||
|
|
||||||
|
Thank you to all my generous patrons and donaters!
|
||||||
|
|
||||||
|
And thank you again to a16z for their generous grant.
|
||||||
|
|
||||||
|
<!-- footer end -->
|
||||||
|
|
||||||
|
# Original model card: Beijing Academy of Artificial Intelligence's Aquilachat2 34B 16K
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
<h4 align="center">
|
||||||
|
<p>
|
||||||
|
<b>English</b> |
|
||||||
|
<a href="https://huggingface.co/BAAI/AquilaChat2-34B-16K/blob/main/README_zh.md">简体中文</a>
|
||||||
|
</p>
|
||||||
|
</h4>
|
||||||
|
|
||||||
|
|
||||||
|
<p align="center">
|
||||||
|
<a href="https://github.com/FlagAI-Open/Aquila2" target="_blank">Github</a> • <a href="https://github.com/FlagAI-Open/Aquila2/blob/main/assets/wechat-qrcode.jpg" target="_blank">WeChat</a> <br>
|
||||||
|
</p>
|
||||||
|
|
||||||
|
|
||||||
|
We opensource our **Aquila2** series, now including **Aquila2**, the base language models, namely **Aquila2-7B** and **Aquila2-34B**, as well as **AquilaChat2**, the chat models, namely **AquilaChat2-7B** and **AquilaChat2-34B**, as well as the long-text chat models, namely **AquilaChat2-7B-16k** and **AquilaChat2-34B-16k**
|
||||||
|
|
||||||
|
|
||||||
|
2023.10.25 🔥 **AquilaChat2-34B-16K v1.2** is based on the previous **AquilaChat2-34B-16K**. The AquilaChat2-34B-16K-V1.2 has significantly improved long-text synthesis capabilities compared to the V1 version,
|
||||||
|
approaching the level of GPT-3.5-16K. Additionally, the V1.2 version incorporates more conventional instruction fine-tuning corpora, enhancing its performance in non-long-text scenarios compared to the V1 version.
|
||||||
|
|
||||||
|
The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels.
|
||||||
|
|
||||||
|
|
||||||
|
## Quick Start AquilaChat2-34B-16K（Chat model）
|
||||||
|
|
||||||
|
### 1. Inference
|
||||||
|
|
||||||
|
```python
|
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||||
|
import torch
|
||||||
|
|
||||||
|
device = torch.device("cuda:0")
|
||||||
|
model_info = "BAAI/AquilaChat2-34B-16k"
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
|
||||||
|
quantization_config=BitsAndBytesConfig(
|
||||||
|
load_in_4bit=True,
|
||||||
|
bnb_4bit_use_double_quant=True,
|
||||||
|
bnb_4bit_quant_type="nf4",
|
||||||
|
bnb_4bit_compute_dtype=torch.bfloat16,
|
||||||
|
)
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True, torch_dtype=torch.bfloat16,
|
||||||
|
# quantization_config=quantization_config, # Uncomment this line for 4bit quantization
|
||||||
|
)
|
||||||
|
model.eval()
|
||||||
|
model.to(device)
|
||||||
|
text = "请给出10个要到北京旅游的理由。"
|
||||||
|
from predict import predict
|
||||||
|
out = predict(model, text, tokenizer=tokenizer, max_gen_len=200, top_p=0.9,
|
||||||
|
seed=123, topk=15, temperature=1.0, sft=True, device=device,
|
||||||
|
model_name="AquilaChat2-34B-16K")
|
||||||
|
print(out)
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
|
## License
|
||||||
|
|
||||||
|
Aquila2 series open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaChat2-34B-16K/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf)
|
||||||
10
added_tokens.json
Normal file
10
added_tokens.json
Normal file
@@ -0,0 +1,10 @@
|
|||||||
|
{
|
||||||
|
"</s>": 100007,
|
||||||
|
"<|LDWANG|>": 100002,
|
||||||
|
"<|endofpiece|>": 100001,
|
||||||
|
"<|startofpiece|>": 100000,
|
||||||
|
"[CLS]": 100006,
|
||||||
|
"[MASK]": 100003,
|
||||||
|
"[gMASK]": 100004,
|
||||||
|
"[sMASK]": 100005
|
||||||
|
}
|
||||||
54
config.json
Normal file
54
config.json
Normal file
@@ -0,0 +1,54 @@
|
|||||||
|
{
|
||||||
|
"_name_or_path": "/workspace/process/baai_aquilachat2-34b-16k/source",
|
||||||
|
"architectures": [
|
||||||
|
"AquilaForCausalLM"
|
||||||
|
],
|
||||||
|
"auto_map": {
|
||||||
|
"AutoConfig": "configuration_aquila.AquilaConfig",
|
||||||
|
"AutoModelForCausalLM": "modeling_aquila.AquilaForCausalLM"
|
||||||
|
},
|
||||||
|
"bos_token_id": 100006,
|
||||||
|
"eos_token_id": 100007,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 6144,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 24576,
|
||||||
|
"max_position_embeddings": 4096,
|
||||||
|
"model_type": "aquila",
|
||||||
|
"num_attention_heads": 48,
|
||||||
|
"num_hidden_layers": 60,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"pretraining_tp": 1,
|
||||||
|
"quantization_config": {
|
||||||
|
"batch_size": 1,
|
||||||
|
"bits": 4,
|
||||||
|
"block_name_to_quantize": "model.layers",
|
||||||
|
"damp_percent": 0.1,
|
||||||
|
"desc_act": true,
|
||||||
|
"disable_exllama": false,
|
||||||
|
"group_size": -1,
|
||||||
|
"max_input_length": null,
|
||||||
|
"model_seqlen": 8192,
|
||||||
|
"module_name_preceding_first_block": [
|
||||||
|
"model.embed_tokens"
|
||||||
|
],
|
||||||
|
"pad_token_id": null,
|
||||||
|
"quant_method": "gptq",
|
||||||
|
"sym": true,
|
||||||
|
"tokenizer": null,
|
||||||
|
"true_sequential": true,
|
||||||
|
"use_cuda_fp16": true
|
||||||
|
},
|
||||||
|
"rms_norm_eps": 1e-05,
|
||||||
|
"rope_scaling": {
|
||||||
|
"factor": 4.0,
|
||||||
|
"type": "linear"
|
||||||
|
},
|
||||||
|
"rope_theta": 10000.0,
|
||||||
|
"tie_word_embeddings": false,
|
||||||
|
"torch_dtype": "float16",
|
||||||
|
"transformers_version": "4.34.1",
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 100008
|
||||||
|
}
|
||||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
|||||||
|
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||||
128
configuration_aquila.py
Normal file
128
configuration_aquila.py
Normal file
@@ -0,0 +1,128 @@
|
|||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||||
|
#
|
||||||
|
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||||
|
# and OPT implementations in this library. It has been modified from its
|
||||||
|
# original forms to accommodate minor architectural differences compared
|
||||||
|
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
""" Aquila model configuration"""
|
||||||
|
|
||||||
|
from transformers import PretrainedConfig
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
class AquilaConfig(PretrainedConfig):
|
||||||
|
r"""
|
||||||
|
This is the configuration class to store the configuration of a [`AquilaModel`]. It is used to instantiate an Aquila
|
||||||
|
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||||
|
defaults will yield a similar configuration to that of the Aquila-7B.
|
||||||
|
|
||||||
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||||
|
documentation from [`PretrainedConfig`] for more information.
|
||||||
|
|
||||||
|
|
||||||
|
Args:
|
||||||
|
vocab_size (`int`, *optional*, defaults to 32000):
|
||||||
|
Vocabulary size of the Aquila model. Defines the number of different tokens that can be represented by the
|
||||||
|
`inputs_ids` passed when calling [`AquilaModel`]
|
||||||
|
hidden_size (`int`, *optional*, defaults to 4096):
|
||||||
|
Dimension of the hidden representations.
|
||||||
|
intermediate_size (`int`, *optional*, defaults to 11008):
|
||||||
|
Dimension of the MLP representations.
|
||||||
|
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||||
|
Number of hidden layers in the Transformer encoder.
|
||||||
|
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||||
|
Number of attention heads for each attention layer in the Transformer encoder.
|
||||||
|
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||||
|
The non-linear activation function (function or string) in the decoder.
|
||||||
|
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
||||||
|
The maximum sequence length that this model might ever be used with. Typically set this to something large
|
||||||
|
just in case (e.g., 512 or 1024 or 2048).
|
||||||
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||||
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||||
|
rms_norm_eps (`float`, *optional*, defaults to 1e-12):
|
||||||
|
The epsilon used by the rms normalization layers.
|
||||||
|
use_cache (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||||
|
relevant if `config.is_decoder=True`.
|
||||||
|
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether to tie weight embeddings
|
||||||
|
Example:
|
||||||
|
|
||||||
|
```python
|
||||||
|
>>> from transformers import AquilaModel, AquilaConfig
|
||||||
|
|
||||||
|
>>> # Initializing a Aquila aquila-7b style configuration
|
||||||
|
>>> configuration = AquilaConfig()
|
||||||
|
|
||||||
|
>>> # Initializing a model from the aquila-7b style configuration
|
||||||
|
>>> model = AquilaModel(configuration)
|
||||||
|
|
||||||
|
>>> # Accessing the model configuration
|
||||||
|
>>> configuration = model.config
|
||||||
|
```"""
|
||||||
|
model_type = "aquila"
|
||||||
|
keys_to_ignore_at_inference = ["past_key_values"]
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_size=100008,
|
||||||
|
hidden_size=4096,
|
||||||
|
intermediate_size=11008,
|
||||||
|
num_hidden_layers=32,
|
||||||
|
num_attention_heads=32,
|
||||||
|
num_key_value_heads=None,
|
||||||
|
hidden_act="silu",
|
||||||
|
max_position_embeddings=2048,
|
||||||
|
initializer_range=0.02,
|
||||||
|
rms_norm_eps=1e-6,
|
||||||
|
use_cache=True,
|
||||||
|
pad_token_id=0,
|
||||||
|
bos_token_id=1,
|
||||||
|
eos_token_id=2,
|
||||||
|
pretraining_tp=1,
|
||||||
|
tie_word_embeddings=False,
|
||||||
|
rope_theta=10000.0,
|
||||||
|
rope_scaling=None,
|
||||||
|
**kwargs,
|
||||||
|
):
|
||||||
|
self.vocab_size = vocab_size
|
||||||
|
self.max_position_embeddings = max_position_embeddings
|
||||||
|
self.hidden_size = hidden_size
|
||||||
|
self.intermediate_size = intermediate_size
|
||||||
|
self.num_hidden_layers = num_hidden_layers
|
||||||
|
|
||||||
|
# for backward compatibility
|
||||||
|
if num_key_value_heads is None:
|
||||||
|
num_key_value_heads = num_attention_heads
|
||||||
|
|
||||||
|
self.num_key_value_heads = num_key_value_heads
|
||||||
|
|
||||||
|
self.num_attention_heads = num_attention_heads
|
||||||
|
self.hidden_act = hidden_act
|
||||||
|
self.initializer_range = initializer_range
|
||||||
|
self.rms_norm_eps = rms_norm_eps
|
||||||
|
self.pretraining_tp = pretraining_tp
|
||||||
|
self.use_cache = use_cache
|
||||||
|
self.rope_theta = rope_theta
|
||||||
|
self.rope_scaling = rope_scaling
|
||||||
|
|
||||||
|
super().__init__(
|
||||||
|
pad_token_id=pad_token_id,
|
||||||
|
bos_token_id=bos_token_id,
|
||||||
|
eos_token_id=eos_token_id,
|
||||||
|
tie_word_embeddings=tie_word_embeddings,
|
||||||
|
**kwargs,
|
||||||
|
)
|
||||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 100006,
|
||||||
|
"eos_token_id": 100007,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"transformers_version": "4.31.0"
|
||||||
|
}
|
||||||
99744
merges.txt
Normal file
99744
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:a5b00065770bcd38d0d1ca7b0cb4197ae4250eeee2bdaa1d1789a1f3307109b9
|
||||||
|
size 9948956912
|
||||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:09b499f121e5d2a1377f14e6cb5f0277b512a0b171cc36ab6ffeddb1ee952b91
|
||||||
|
size 8776077416
|
||||||
2230
model.safetensors.index.json
Normal file
2230
model.safetensors.index.json
Normal file
File diff suppressed because it is too large
Load Diff
1031
modeling_aquila.py
Normal file
1031
modeling_aquila.py
Normal file
File diff suppressed because it is too large
Load Diff
446
predict.py
Normal file
446
predict.py
Normal file
@@ -0,0 +1,446 @@
|
|||||||
|
"""
|
||||||
|
Copied from https://github.com/lm-sys/FastChat.
|
||||||
|
Later we will contribute our changes into it.
|
||||||
|
"""
|
||||||
|
import dataclasses
|
||||||
|
from enum import auto, IntEnum
|
||||||
|
from typing import List, Any, Dict
|
||||||
|
import math
|
||||||
|
from typing import List, Optional, Tuple, Union
|
||||||
|
import random
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torch.utils.checkpoint
|
||||||
|
from torch import nn
|
||||||
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
||||||
|
|
||||||
|
from transformers.activations import ACT2FN
|
||||||
|
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
|
||||||
|
from transformers.modeling_utils import PreTrainedModel
|
||||||
|
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
|
||||||
|
from transformers import (
|
||||||
|
LogitsProcessorList,
|
||||||
|
MinLengthLogitsProcessor,
|
||||||
|
TopKLogitsWarper,
|
||||||
|
TemperatureLogitsWarper,
|
||||||
|
TopPLogitsWarper,
|
||||||
|
StoppingCriteriaList,
|
||||||
|
MaxLengthCriteria,
|
||||||
|
BitsAndBytesConfig,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
class SeparatorStyle(IntEnum):
|
||||||
|
"""Separator styles."""
|
||||||
|
|
||||||
|
ADD_COLON_SINGLE = auto()
|
||||||
|
ADD_COLON_TWO = auto()
|
||||||
|
ADD_COLON_SPACE_SINGLE = auto()
|
||||||
|
NO_COLON_SINGLE = auto()
|
||||||
|
NO_COLON_TWO = auto()
|
||||||
|
ADD_NEW_LINE_SINGLE = auto()
|
||||||
|
|
||||||
|
|
||||||
|
@dataclasses.dataclass
|
||||||
|
class Conversation:
|
||||||
|
"""A class that manages prompt templates and keeps all conversation history."""
|
||||||
|
|
||||||
|
# The name of this template
|
||||||
|
name: str
|
||||||
|
# The template of the system prompt
|
||||||
|
system_template: str = "{system_message}"
|
||||||
|
# The system message
|
||||||
|
system_message: str = ""
|
||||||
|
# The names of two roles
|
||||||
|
roles: List[str] = (("USER", "ASSISTANT"),)
|
||||||
|
# All messages. Each item is (role, message).
|
||||||
|
messages: List[List[str]] = ()
|
||||||
|
# The number of few shot examples
|
||||||
|
offset: int = 0
|
||||||
|
# The separator style and configurations
|
||||||
|
sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
|
||||||
|
sep: str = "\n"
|
||||||
|
sep2: str = None
|
||||||
|
# Stop criteria (the default one is EOS token)
|
||||||
|
stop_str: str = None
|
||||||
|
# Stops generation if meeting any token in this list
|
||||||
|
stop_token_ids: List[int] = None
|
||||||
|
|
||||||
|
def get_prompt(self) -> str:
|
||||||
|
"""Get the prompt for generation."""
|
||||||
|
system_prompt = self.system_template.format(system_message=self.system_message)
|
||||||
|
if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
|
||||||
|
ret = system_prompt + self.sep
|
||||||
|
for role, message in self.messages:
|
||||||
|
if message:
|
||||||
|
ret += role + ": " + message + self.sep
|
||||||
|
else:
|
||||||
|
ret += role + ":"
|
||||||
|
return ret
|
||||||
|
elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
|
||||||
|
seps = [self.sep, self.sep2]
|
||||||
|
ret = system_prompt + seps[0]
|
||||||
|
for i, (role, message) in enumerate(self.messages):
|
||||||
|
if message:
|
||||||
|
ret += role + ": " + message + seps[i % 2]
|
||||||
|
else:
|
||||||
|
ret += role + ":"
|
||||||
|
return ret
|
||||||
|
elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
|
||||||
|
ret = system_prompt + self.sep
|
||||||
|
for role, message in self.messages:
|
||||||
|
if message:
|
||||||
|
ret += role + ": " + message + self.sep
|
||||||
|
else:
|
||||||
|
ret += role + ": " # must be end with a space
|
||||||
|
return ret
|
||||||
|
elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
|
||||||
|
ret = "" if system_prompt == "" else system_prompt + self.sep
|
||||||
|
for role, message in self.messages:
|
||||||
|
if message:
|
||||||
|
ret += role + "\n" + message + self.sep
|
||||||
|
else:
|
||||||
|
ret += role + "\n"
|
||||||
|
return ret
|
||||||
|
elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
|
||||||
|
ret = system_prompt
|
||||||
|
for role, message in self.messages:
|
||||||
|
if message:
|
||||||
|
ret += role + message + self.sep
|
||||||
|
else:
|
||||||
|
ret += role
|
||||||
|
return ret
|
||||||
|
elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
|
||||||
|
seps = [self.sep, self.sep2]
|
||||||
|
ret = system_prompt
|
||||||
|
for i, (role, message) in enumerate(self.messages):
|
||||||
|
if message:
|
||||||
|
ret += role + message + seps[i % 2]
|
||||||
|
else:
|
||||||
|
ret += role
|
||||||
|
return ret
|
||||||
|
|
||||||
|
def set_system_message(self, system_message: str):
|
||||||
|
"""Set the system message."""
|
||||||
|
self.system_message = system_message
|
||||||
|
|
||||||
|
def append_message(self, role: str, message: str):
|
||||||
|
"""Append a new message."""
|
||||||
|
self.messages.append([role, message])
|
||||||
|
|
||||||
|
def update_last_message(self, message: str):
|
||||||
|
"""Update the last output.
|
||||||
|
|
||||||
|
The last message is typically set to be None when constructing the prompt,
|
||||||
|
so we need to update it in-place after getting the response from a model.
|
||||||
|
"""
|
||||||
|
self.messages[-1][1] = message
|
||||||
|
|
||||||
|
def copy(self):
|
||||||
|
return Conversation(
|
||||||
|
name=self.name,
|
||||||
|
system_template=self.system_template,
|
||||||
|
system_message=self.system_message,
|
||||||
|
roles=self.roles,
|
||||||
|
messages=[[x, y] for x, y in self.messages],
|
||||||
|
offset=self.offset,
|
||||||
|
sep_style=self.sep_style,
|
||||||
|
sep=self.sep,
|
||||||
|
sep2=self.sep2,
|
||||||
|
stop_str=self.stop_str,
|
||||||
|
stop_token_ids=self.stop_token_ids,
|
||||||
|
)
|
||||||
|
|
||||||
|
def dict(self):
|
||||||
|
return {
|
||||||
|
"template_name": self.name,
|
||||||
|
"system_message": self.system_message,
|
||||||
|
"roles": self.roles,
|
||||||
|
"messages": self.messages,
|
||||||
|
"offset": self.offset,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# A global registry for all conversation templates
|
||||||
|
conv_templates: Dict[str, Conversation] = {}
|
||||||
|
|
||||||
|
|
||||||
|
def register_conv_template(template: Conversation, override: bool = False):
|
||||||
|
"""Register a new conversation template."""
|
||||||
|
if not override:
|
||||||
|
assert (
|
||||||
|
template.name not in conv_templates
|
||||||
|
), f"{template.name} has been registered."
|
||||||
|
|
||||||
|
conv_templates[template.name] = template
|
||||||
|
|
||||||
|
|
||||||
|
def get_conv_template(name: str) -> Conversation:
|
||||||
|
"""Get a conversation template."""
|
||||||
|
return conv_templates[name].copy()
|
||||||
|
|
||||||
|
def get_conversation_template(model_path: str) -> Conversation:
|
||||||
|
"""Get the default conversation template."""
|
||||||
|
if "aquila-v1" in model_path:
|
||||||
|
return get_conv_template("aquila-v1")
|
||||||
|
elif "aquila-chat" in model_path:
|
||||||
|
return get_conv_template("aquila-chat")
|
||||||
|
elif "aquila-legacy" in model_path:
|
||||||
|
return get_conv_template("aquila-legacy")
|
||||||
|
else:
|
||||||
|
return get_conv_template("aquila")
|
||||||
|
|
||||||
|
# AquilaChat default template
|
||||||
|
# source: https://github.com/FlagAI-Open/FlagAI/blob/master/examples/Aquila/Aquila-chat/cyg_conversation.py
|
||||||
|
register_conv_template(
|
||||||
|
Conversation(
|
||||||
|
name="aquila-chat",
|
||||||
|
system_message="A chat between a curious human and an artificial intelligence assistant. "
|
||||||
|
"The assistant gives helpful, detailed, and polite answers to the human's questions.",
|
||||||
|
roles=("Human", "Assistant", "System"),
|
||||||
|
messages=(),
|
||||||
|
offset=0,
|
||||||
|
sep_style=SeparatorStyle.ADD_COLON_SINGLE,
|
||||||
|
sep="###",
|
||||||
|
sep2="",
|
||||||
|
stop_str=["###", "</s>", "[UNK]"],
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
register_conv_template(
|
||||||
|
Conversation(
|
||||||
|
name="aquila-legacy",
|
||||||
|
system_message="A chat between a curious human and an artificial intelligence assistant. "
|
||||||
|
"The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
|
||||||
|
roles=("### Human: ", "### Assistant: ", "System"),
|
||||||
|
messages=(),
|
||||||
|
offset=0,
|
||||||
|
sep_style=SeparatorStyle.NO_COLON_TWO,
|
||||||
|
sep="\n",
|
||||||
|
sep2="</s>",
|
||||||
|
stop_str=["</s>", "[UNK]"],
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
register_conv_template(
|
||||||
|
Conversation(
|
||||||
|
name="aquila",
|
||||||
|
system_message="A chat between a curious human and an artificial intelligence assistant. "
|
||||||
|
"The assistant gives helpful, detailed, and polite answers to the human's questions.",
|
||||||
|
roles=("Human", "Assistant", "System"),
|
||||||
|
messages=(),
|
||||||
|
offset=0,
|
||||||
|
sep_style=SeparatorStyle.ADD_COLON_TWO,
|
||||||
|
sep="###",
|
||||||
|
sep2="</s>",
|
||||||
|
stop_str=["</s>", "[UNK]"],
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
register_conv_template(
|
||||||
|
Conversation(
|
||||||
|
name="aquila-v1",
|
||||||
|
roles=("<|startofpiece|>", "<|endofpiece|>", ""),
|
||||||
|
messages=(),
|
||||||
|
offset=0,
|
||||||
|
sep_style=SeparatorStyle.NO_COLON_TWO,
|
||||||
|
sep="",
|
||||||
|
sep2="</s>",
|
||||||
|
stop_str=["</s>", "<|endoftext|>"],
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
print("aquila template:")
|
||||||
|
conv = get_conv_template("aquila")
|
||||||
|
conv.append_message(conv.roles[0], "Hello!")
|
||||||
|
conv.append_message(conv.roles[1], "Hi!")
|
||||||
|
conv.append_message(conv.roles[0], "How are you?")
|
||||||
|
conv.append_message(conv.roles[1], None)
|
||||||
|
print(conv.get_prompt())
|
||||||
|
|
||||||
|
print("\n")
|
||||||
|
|
||||||
|
print("aquila-chat template:")
|
||||||
|
conv = get_conv_template("aquila-chat")
|
||||||
|
conv.append_message(conv.roles[0], "Hello!")
|
||||||
|
conv.append_message(conv.roles[1], "Hi!")
|
||||||
|
conv.append_message(conv.roles[0], "How are you?")
|
||||||
|
conv.append_message(conv.roles[1], None)
|
||||||
|
print(conv.get_prompt())
|
||||||
|
|
||||||
|
print("\n")
|
||||||
|
|
||||||
|
print("aquila-v1 template:")
|
||||||
|
conv = get_conv_template("aquila-v1")
|
||||||
|
conv.append_message(conv.roles[0], "Hello!")
|
||||||
|
conv.append_message(conv.roles[1], "Hi!")
|
||||||
|
conv.append_message(conv.roles[0], "How are you?")
|
||||||
|
conv.append_message(conv.roles[1], None)
|
||||||
|
print(conv.get_prompt())
|
||||||
|
|
||||||
|
print("\n")
|
||||||
|
|
||||||
|
print("aquila-legacy template:")
|
||||||
|
conv = get_conv_template("aquila-legacy")
|
||||||
|
conv.append_message(conv.roles[0], "Hello!")
|
||||||
|
conv.append_message(conv.roles[1], "Hi!")
|
||||||
|
conv.append_message(conv.roles[0], "How are you?")
|
||||||
|
conv.append_message(conv.roles[1], None)
|
||||||
|
print(conv.get_prompt())
|
||||||
|
|
||||||
|
print("\n")
|
||||||
|
|
||||||
|
def set_random_seed(seed):
|
||||||
|
"""Set random seed for reproducability."""
|
||||||
|
if seed is not None and seed > 0:
|
||||||
|
random.seed(seed)
|
||||||
|
np.random.seed(seed)
|
||||||
|
torch.manual_seed(seed)
|
||||||
|
|
||||||
|
def covert_prompt_to_input_ids_with_history(text, history, tokenizer, max_token, convo_template="aquila-chat"):
|
||||||
|
# aquila-chat as default
|
||||||
|
conv = get_conv_template(convo_template)
|
||||||
|
|
||||||
|
conv.append_message(conv.roles[1], None)
|
||||||
|
conv.append_message(conv.roles[0], text)
|
||||||
|
|
||||||
|
example = tokenizer.encode_plus(f"{conv.get_prompt()} ", None, max_length=None)['input_ids']
|
||||||
|
|
||||||
|
if history is None or not isinstance(history, list):
|
||||||
|
history = []
|
||||||
|
|
||||||
|
while(len(history) > 0 and (len(example) < max_token)):
|
||||||
|
tmp = history.pop()
|
||||||
|
if tmp[0] == 'ASSISTANT':
|
||||||
|
conv.append_message(conv.roles[1], tmp[1])
|
||||||
|
else:
|
||||||
|
conv.append_message(conv.roles[0], tmp[1])
|
||||||
|
example = tokenizer.encode_plus(f"{conv.get_prompt()} ", None, max_length=None)['input_ids']
|
||||||
|
|
||||||
|
if len(example) >= max_token:
|
||||||
|
conv.messages.pop()
|
||||||
|
conv.messages = conv.messages[::-1]
|
||||||
|
print('model in:', conv.get_prompt())
|
||||||
|
example = tokenizer.encode_plus(f"{conv.get_prompt()} ", None, max_length=None)['input_ids']
|
||||||
|
|
||||||
|
return example
|
||||||
|
|
||||||
|
def predict(model, text, tokenizer=None,
|
||||||
|
max_gen_len=200, top_p=0.95,
|
||||||
|
seed=1234, topk=100,
|
||||||
|
temperature=0.9,
|
||||||
|
sft=True, convo_template = "",
|
||||||
|
device = "cuda",
|
||||||
|
model_name="AquilaChat2-7B",
|
||||||
|
history=None,
|
||||||
|
**kwargs):
|
||||||
|
|
||||||
|
vocab = tokenizer.get_vocab()
|
||||||
|
|
||||||
|
id2word = {v:k for k, v in vocab.items()}
|
||||||
|
|
||||||
|
|
||||||
|
template_map = {"AquilaChat2-7B": "aquila-v1",
|
||||||
|
"AquilaChat2-34B": "aquila-legacy",
|
||||||
|
"AquilaChat2-7B-16K": "aquila",
|
||||||
|
"AquilaChat2-34B-16K": "aquila"}
|
||||||
|
if not convo_template:
|
||||||
|
convo_template=template_map.get(model_name, "aquila-chat")
|
||||||
|
|
||||||
|
set_random_seed(seed)
|
||||||
|
if temperature == 0:
|
||||||
|
topk = 1
|
||||||
|
temperature = 1.0
|
||||||
|
if sft:
|
||||||
|
tokens = covert_prompt_to_input_ids_with_history(text, history=history, tokenizer=tokenizer, max_token=2048, convo_template=convo_template)
|
||||||
|
tokens = torch.tensor(tokens)[None,].to(device)
|
||||||
|
else :
|
||||||
|
tokens = tokenizer.encode_plus(text)["input_ids"]
|
||||||
|
print(tokenizer.decode(tokens))
|
||||||
|
tokens = torch.tensor(tokens)[None,].to(device)
|
||||||
|
input_length = len(tokens[0])
|
||||||
|
with torch.no_grad():
|
||||||
|
|
||||||
|
# instantiate logits processors
|
||||||
|
logits_processor = LogitsProcessorList(
|
||||||
|
[
|
||||||
|
MinLengthLogitsProcessor(1, eos_token_id=100007),
|
||||||
|
]
|
||||||
|
)
|
||||||
|
# instantiate logits processors
|
||||||
|
logits_warper = LogitsProcessorList(
|
||||||
|
[
|
||||||
|
TopPLogitsWarper(top_p),
|
||||||
|
TopKLogitsWarper(topk),
|
||||||
|
TemperatureLogitsWarper(temperature),
|
||||||
|
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=input_length + max_gen_len)])
|
||||||
|
out = model.sample(
|
||||||
|
tokens,
|
||||||
|
logits_processor=logits_processor,
|
||||||
|
logits_warper=logits_warper,
|
||||||
|
stopping_criteria=stopping_criteria,
|
||||||
|
return_dict_in_generate=True,
|
||||||
|
output_scores=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
# print(out)
|
||||||
|
out_ids = out["sequences"][0][input_length:].cpu().numpy()
|
||||||
|
|
||||||
|
out_scores = out["scores"]
|
||||||
|
|
||||||
|
out_scores = torch.cat(out_scores, dim=0)
|
||||||
|
out_scores = torch.nn.functional.softmax(out_scores, dim=-1).cpu().numpy()
|
||||||
|
|
||||||
|
probs = []
|
||||||
|
for i in range(len(out_ids)):
|
||||||
|
probs.append(float(out_scores[i][out_ids[i]]))
|
||||||
|
|
||||||
|
# print(f"probs is {probs}")
|
||||||
|
|
||||||
|
convert_tokens = []
|
||||||
|
for t in out_ids:
|
||||||
|
if t == 100006:
|
||||||
|
convert_tokens.append("[CLS]")
|
||||||
|
else :
|
||||||
|
convert_tokens.append(id2word.get(t, "[unkonwn_token]"))
|
||||||
|
|
||||||
|
out_text = tokenizer.decode(out_ids.tolist())
|
||||||
|
|
||||||
|
|
||||||
|
out = out_text
|
||||||
|
|
||||||
|
if "[UNK]" in out:
|
||||||
|
special_index = out.index("[UNK]")
|
||||||
|
out = out[:special_index]
|
||||||
|
token_length = len(tokenizer.encode_plus(out)["input_ids"])
|
||||||
|
convert_tokens = convert_tokens[:token_length]
|
||||||
|
probs = probs[:token_length]
|
||||||
|
|
||||||
|
if "</s>" in out:
|
||||||
|
special_index = out.index("</s>")
|
||||||
|
out = out[: special_index]
|
||||||
|
token_length = len(tokenizer.encode_plus(out)["input_ids"])
|
||||||
|
convert_tokens = convert_tokens[:token_length]
|
||||||
|
probs = probs[:token_length]
|
||||||
|
|
||||||
|
if len(out) > 0 and out[0] == " ":
|
||||||
|
out = out[1:]
|
||||||
|
|
||||||
|
convert_tokens = convert_tokens[1:]
|
||||||
|
probs = probs[1:]
|
||||||
|
|
||||||
|
if isinstance(history, list):
|
||||||
|
# Update history
|
||||||
|
history.insert(0, ('ASSISTANT', out))
|
||||||
|
history.insert(0, ('USER', text))
|
||||||
|
|
||||||
|
return out
|
||||||
8
quantize_config.json
Normal file
8
quantize_config.json
Normal file
@@ -0,0 +1,8 @@
|
|||||||
|
{
|
||||||
|
"bits": 4,
|
||||||
|
"group_size": -1,
|
||||||
|
"damp_percent": 0.01,
|
||||||
|
"desc_act": true,
|
||||||
|
"sym": true,
|
||||||
|
"true_sequential": true
|
||||||
|
}
|
||||||
6
special_tokens_map.json
Normal file
6
special_tokens_map.json
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
{
|
||||||
|
"bos_token": "[CLS]",
|
||||||
|
"eos_token": "</s>",
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"unk_token": "<|endoftext|>"
|
||||||
|
}
|
||||||
199863
tokenizer.json
Normal file
199863
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
10
tokenizer_config.json
Normal file
10
tokenizer_config.json
Normal file
@@ -0,0 +1,10 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"bos_token": "[CLS]",
|
||||||
|
"clean_up_tokenization_spaces": true,
|
||||||
|
"eos_token": "</s>",
|
||||||
|
"model_max_length": 4096,
|
||||||
|
"padding_side": "right",
|
||||||
|
"tokenizer_class": "GPT2Tokenizer",
|
||||||
|
"unk_token": "<|endoftext|>"
|
||||||
|
}
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user