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yayi2-30B-llama-GGUF

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TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

Yayi2 30B Llama - GGUF

Description

This repo contains GGUF format model files for Cognitive Computations's Yayi2 30B Llama.

These files were quantised using hardware kindly provided by Massed Compute.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

  • llama.cpp. The source project for GGUF. Offers a CLI and a server option.
  • text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
  • KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
  • GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
  • LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
  • LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
  • Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
  • llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
  • candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
  • ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Repositories available

Prompt template: None 

{prompt}

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d

They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

Explanation of quantisation methods

Click to see details

The new methods available are:

  • GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
  • GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
  • GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
  • GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
  • GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name Quant method Bits Size Max RAM required Use case
yayi2-30b-llama.Q2_K.gguf Q2_K 2 12.90 GB 15.40 GB smallest, significant quality loss - not recommended for most purposes
yayi2-30b-llama.Q3_K_S.gguf Q3_K_S 3 13.30 GB 15.80 GB very small, high quality loss
yayi2-30b-llama.Q3_K_M.gguf Q3_K_M 3 14.77 GB 17.27 GB very small, high quality loss
yayi2-30b-llama.Q3_K_L.gguf Q3_K_L 3 16.10 GB 18.60 GB small, substantial quality loss
yayi2-30b-llama.Q4_0.gguf Q4_0 4 17.26 GB 19.76 GB legacy; small, very high quality loss - prefer using Q3_K_M
yayi2-30b-llama.Q4_K_S.gguf Q4_K_S 4 17.32 GB 19.82 GB small, greater quality loss
yayi2-30b-llama.Q4_K_M.gguf Q4_K_M 4 18.23 GB 20.73 GB medium, balanced quality - recommended
yayi2-30b-llama.Q5_0.gguf Q5_0 5 20.99 GB 23.49 GB legacy; medium, balanced quality - prefer using Q4_K_M
yayi2-30b-llama.Q5_K_S.gguf Q5_K_S 5 20.99 GB 23.49 GB large, low quality loss - recommended
yayi2-30b-llama.Q5_K_M.gguf Q5_K_M 5 21.49 GB 23.99 GB large, very low quality loss - recommended
yayi2-30b-llama.Q6_K.gguf Q6_K 6 24.95 GB 27.45 GB very large, extremely low quality loss
yayi2-30b-llama.Q8_0.gguf Q8_0 8 32.31 GB 34.81 GB very large, extremely low quality loss - not recommended

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

How to download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

  • LM Studio
  • LoLLMS Web UI
  • Faraday.dev

In text-generation-webui

Under Download Model, you can enter the model repo: TheBloke/yayi2-30B-llama-GGUF and below it, a specific filename to download, such as: yayi2-30b-llama.Q4_K_M.gguf.

Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface-cli download TheBloke/yayi2-30B-llama-GGUF yayi2-30b-llama.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
More advanced huggingface-cli download usage (click to read)

You can also download multiple files at once with a pattern:

huggingface-cli download TheBloke/yayi2-30B-llama-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

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:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/yayi2-30B-llama-GGUF yayi2-30b-llama.Q4_K_M.gguf --local-dir . --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.

Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m yayi2-30b-llama.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 4096 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in text-generation-webui

Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

How to load this model in Python code, using llama-cpp-python

For full documentation, please see: llama-cpp-python docs.

First install the package

Run one of the following commands, according to your system:

# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python

Simple llama-cpp-python example code 

from llama_cpp import Llama

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./yayi2-30b-llama.Q4_K_M.gguf",  # Download the model file first
  n_ctx=4096,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
output = llm(
  "{prompt}", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)

# Chat Completion API

llm = Llama(model_path="./yayi2-30b-llama.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)

How to use with LangChain

Here are guides on using llama-cpp-python and ctransformers with LangChain:

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

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.

Special thanks to: Aemon Algiz.

Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Cognitive Computations's Yayi2 30B Llama

This is wenge-research/yayi2-30b converted to llama compatible format.

Subject to the Yayi 2 license.

Brought to you by @Weyaxi and @ehartford, with thanks to @chargoddard for the pioneering work and the consultation!

And of course thanks to the yayi2 team for sharing an amazing model.

Original card below:

YAYI 2

介绍/Introduction

YAYI 2 是中科闻歌研发的开源大语言模型，包括 Base 和 Chat 版本，参数规模为 30B。YAYI2-30B 是基于 Transformer 的大语言模型，采用了 2.65 万亿 Tokens 的高质量、多语言语料进行预训练。针对通用和特定领域的应用场景，我们采用了百万级指令进行微调，同时借助人类反馈强化学习方法，以更好地使模型与人类价值观对齐。

本次开源的模型为 YAYI2-30B Base 模型。如果您想了解更多关于 YAYI 2 模型的细节，我们建议您参阅 GitHub 仓库。更多技术细节，敬请期待我们的技术报告🔥。

YAYI 2 is a collection of open-source large language models launched by Wenge Technology. YAYI2-30B is a Transformer-based large language model, and has been pretrained for 2.65 trillion tokens of multilingual data with high quality. The base model is aligned with human values through supervised fine-tuning with millions of instructions and reinforcement learning from human feedback (RLHF).

We opensource the pre-trained language model in this release, namely YAYI2-30B. For more details about the YAYI 2, please refer to our GitHub repository. Stay tuned for more technical details in our upcoming technical report! 🔥

模型细节/Model Details

Hyperparameter Value
n_layers 64
n_heads 64
hidden_size 7168
vocab_size 81920
sequence length 4096

要求/Requirements

  • python 3.8及以上版本

  • pytorch 2.0.1 及以上版本

  • 建议使用 CUDA 11.7 及以上版本

  • 运行 BF16 或 FP16 模型需要至少80GB显存（例如1xA100）

  • python 3.8 and above

  • pytorch 2.0.1 and above

  • CUDA 11.7 and above are recommended

  • To run YAYI2-30B in bf16/fp16, at least 80B GPU memory is required (e.g., 1xA100-80G)

快速开始/Quick Start 

>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("wenge-research/yayi2-30b", trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("wenge-research/yayi2-30b", device_map="auto", trust_remote_code=True)
>>> inputs = tokenizer('The winter in Beijing is', return_tensors='pt')
>>> inputs = inputs.to('cuda')
>>> pred = model.generate(
        **inputs,
        max_new_tokens=256,
        eos_token_id=tokenizer.eos_token_id,
        do_sample=True,
        repetition_penalty=1.2,
        temperature=0.4,
        top_k=100,
        top_p=0.8
        )
>>> print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))

评测结果/Evaluation

我们在多个基准数据集上进行了评测，包括 C-Eval、MMLU、 CMMLU、AGIEval、GAOKAO-Bench、GSM8K、MATH、BBH、HumanEval 以及 MBPP。我们考察了模型在语言理解、学科知识、数学推理、逻辑推理以及代码生成方面的表现。YAYI 2 模型在与其规模相近的开源模型中展现出了显著的性能提升。

We evaluate our model on standard benchmarks, including C-Eval, MMLU, CMMLU, AGIEval, GAOKAO-Bench, GSM8K, MATH, BBH, HumanEval, and MBPP. Our goal is to assess the model's performance in language comprehension, knowledge comprehension, mathematical reasoning, logical reasoning, and code generation. YAYI 2 has demonstrated exceptional performance across models with similar size.

Knowledge Math Logic reasonning Code
Model C-Eval(val) MMLU AGIEval CMMLU GAOKAO-Bench GSM8K MATH BBH HumanEval MBPP
5-shot 5-shot 3/0-shot 5-shot 0-shot 8/4-shot 4-shot 3-shot 0-shot 3-shot
MPT-30B - 46.9 33.8 - - 15.2 3.1 38.0 25.0 32.8
Falcon-40B - 55.4 37.0 - - 19.6 5.5 37.1 0.6 29.8
LLaMA2-34B - 62.6 43.4 - - 42.2 6.2 44.1 22.6 33.0
Baichuan2-13B 59.0 59.5 37.4 61.3 45.6 52.6 10.1 49.0 17.1 30.8
Qwen-14B 71.7 67.9 51.9 70.2 62.5 61.6 25.2 53.7 32.3 39.8
InternLM-20B 58.8 62.1 44.6 59.0 45.5 52.6 7.9 52.5 25.6 35.6
Aquila2-34B 98.5 76.0 43.8 78.5 37.8 50.0 17.8 42.5 0.0 41.0
Yi-34B 81.8 76.3 56.5 82.6 68.3 67.6 15.9 66.4 26.2 38.2
YAYI2-30B 80.9 80.5 62.0 84.0 64.4 71.2 14.8 54.5 53.1 45.8

我们使用 OpenCompass Github 仓库 提供的源代码进行了评测。对于对比模型，我们列出了他们在 OpenCompass 榜单上的评测结果，截止日期为 2023年12月15日。对于其他尚未在 OpenCompass 平台参与评测的模型，包括 MPT、Falcon 和 LLaMa 2，我们采用了 LLaMA 2 报告的结果。

We evaluate our model using the source code from the OpenCompass Github repository. If available, we report results for comparative models assessed by OpenCompass with the evaluation reference date set to Dec. 15th, 2013. For MPT, Falcon, and Llama, which have not been evaluated by OpenCompass, we use the results reported in the LLaMA 2 paper.

协议/License

本项目中的代码依照 Apache-2.0 协议开源，社区使用 YAYI 2 模型和数据需要遵循雅意YAYI 2 模型社区许可协议。若您需要将雅意 YAYI 2系列模型或其衍生品用作商业用途，请根据《雅意 YAYI 2 模型商用许可协议》将商用许可申请登记信息发送至指定邮箱 yayi@wenge.com。审核通过后，雅意将授予您商用版权许可，请遵循协议中的商业许可限制。

The code in this project is open-sourced under the Apache-2.0 license. The use of YaYi series model weights and data must adhere to the YAYI 2 Community License. If you intend to use the YAYI 2 series models or their derivatives for commercial purposes, please submit your commercial license application and registration information to yayi@wenge.com, following the YAYI 2 Commercial License. Upon approval, YAYI will grant you a commercial copyright license, subject to the commercial license restrictions outlined in the agreement.

引用/Citation

如果您在工作中使用了我们的模型，请引用我们的论文。

If you are using the resource for your work, please cite our paper.

@article{YAYI 2,
  author    = {Yin Luo, Qingchao Kong, Nan Xu, et.al.},
  title     = {YAYI 2: Multilingual Open Source Large Language Models},
  journal   = {arXiv preprint arXiv},
  year      = {2023}
}
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