OpenHermes 2.5 Open-Source Language Model - Free Deployment, Efficient Code Generation, and General Task Handling!

Openhermes 2.5 Mistral 7B GPTQ

Developed by TheBloke

OpenHermes 2.5 is an advanced language model fine-tuned based on Mistral-7B, focusing on code generation and general task processing, with performance surpassing previous versions.

Large Language Model

Transformers

EnglishOpen Source License:Apache-2.0 #Multi-turn Dialogue Optimization #Enhanced Coding Capability #GPT-4 Level Fine-tuning

Downloads 695

Release Time : 11/2/2023

Model Overview

OpenHermes 2.5 is a Mistral-7B fine-tuned model developed by Teknium. By training on additional code datasets, it has improved performance across multiple benchmarks, excelling particularly in code generation and complex problem-solving.

Model Features

Enhanced Coding Capability

Through additional training on code datasets, the humaneval score improved from 43% to 50.7%, significantly enhancing programming problem-solving ability.

Multi-Benchmark Improvement

Performs excellently in non-code benchmarks such as TruthfulQA, AGIEval, and GPT4All, demonstrating comprehensive capability enhancement.

ChatML Format Support

Adopts the standardized ChatML prompt template, facilitating integration into various dialogue systems.

Model Capabilities

Text Generation

Code Generation

Complex Problem Solving

Role-Playing Dialogue

Knowledge Q&A

Use Cases

Programming Assistance

Code Generation and Explanation

Generate functional code based on natural language descriptions or explain existing code

humaneval pass@1 reaches 50.7%

Intelligent Dialogue

Personalized Role-Playing

Simulate specific roles or characters for natural dialogue

Can simulate anime characters, historical figures, etc.

Knowledge Q&A

Complex Problem Solving

Answer various knowledge-based questions and provide detailed explanations

Performs excellently in the AGIEval benchmark

🚀 Openhermes 2.5 Mistral 7B - GPTQ

This repository provides GPTQ model files for Openhermes 2.5 Mistral 7B, offering multiple quantisation options to suit different hardware and requirements.

📦 Model Information

Property	Details
Model Creator	Teknium
Original Model	Openhermes 2.5 Mistral 7B
Model Type	mistral
Prompt Template	'<
Quantized By	TheBloke
Tags	mistral, instruct, finetune, chatml, gpt4, synthetic data, distillation
License	apache-2.0

✨ Repositories Available

💻 Prompt Template: ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

🛠️ Known Compatible Clients / Servers

These GPTQ models are known to work in the following inference servers/webuis:

This may not be a complete list; if you know of others, please let me know!

📄 Provided Files, and GPTQ Parameters

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.

Each separate quant is in a different branch. See below for instructions on fetching from different branches.

Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.

Explanation of GPTQ parameters

Bits: The bit size of the quantised model.
GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
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.
Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
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).
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.
ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4 - bit.

Branch	Bits	GS	Act Order	Damp %	GPTQ Dataset	Seq Len	Size	ExLlama	Desc
main	4	128	Yes	0.1	wikitext	4096	4.16 GB	Yes	4 - bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy.
gptq-4bit-32g-actorder_True	4	32	Yes	0.1	wikitext	4096	4.57 GB	Yes	4 - bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage.
gptq-8bit--1g-actorder_True	8	None	Yes	0.1	wikitext	4096	4.95 GB	No	8 - bit, with Act Order. No group size, to lower VRAM requirements.
gptq-8bit-128g-actorder_True	8	128	Yes	0.1	wikitext	4096	5.00 GB	No	8 - bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy.
gptq-8bit-32g-actorder_True	8	32	Yes	0.1	wikitext	4096	4.97 GB	No	8 - bit, with group size 32g and Act Order for maximum inference quality.
gptq-4bit-64g-actorder_True	4	64	Yes	0.1	wikitext	4096	4.30 GB	Yes	4 - bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy.

⬇️ How to Download, including from Branches

In text - generation - webui

To download from the main branch, enter TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ in the "Download model" box.

To download from another branch, add :branchname to the end of the download name, eg TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ:gptq-4bit-32g-actorder_True

From the command line

I recommend using the huggingface - hub Python library:

pip3 install huggingface - hub

To download the main branch to a folder called OpenHermes-2.5-Mistral-7B-GPTQ:

mkdir OpenHermes-2.5-Mistral-7B-GPTQ
huggingface-cli download TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ --local-dir OpenHermes-2.5-Mistral-7B-GPTQ --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir OpenHermes-2.5-Mistral-7B-GPTQ
huggingface-cli download TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir OpenHermes-2.5-Mistral-7B-GPTQ --local-dir-use-symlinks False

More advanced huggingface-cli download usage

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.

The cache location can be changed with the HF_HOME environment variable, and/or the --cache-dir parameter to huggingface-cli.

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

mkdir OpenHermes-2.5-Mistral-7B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER = 1 huggingface-cli download TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ --local-dir OpenHermes-2.5-Mistral-7B-GPTQ --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER = 1 before the download command.

With `git` (not recommended)

To clone a specific branch with git, use a command like this:

git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ

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.)

🚀 How to Easily Download and Use this Model in [text - generation - webui](https://github.com/oobabooga/text - generation - webui)

Please make sure you're using the latest version of [text - generation - webui](https://github.com/oobabooga/text - generation - webui).

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.

Click the Model tab.
Under Download custom model or LoRA, enter TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ.
- To download from a specific branch, enter for example TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ:gptq-4bit-32g-actorder_True
- see Provided Files above for the list of branches for each option.
Click Download.
The model will start downloading. Once it's finished it will say "Done".
In the top left, click the refresh icon next to Model.
In the Model dropdown, choose the model you just downloaded: OpenHermes-2.5-Mistral-7B-GPTQ
The model will automatically load, and is now ready for use!
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.
- 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.
Once you're ready, click the Text Generation tab and enter a prompt to get started!

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