Hugging Face's logo

TheBloke
/
Genz-70b-GGUF

Text Generation
Transformers
GGUF
English
llama
text-generation-inference
Model card Files Community
1
Genz-70b-GGUF / README.md
TheBloke's picture
TheBloke
Update README.md
dce95ac
preview code
|
raw
history blame
No virus
24 kB
metadata
inference: false
language:
  - en
library_name: transformers
license: llama2
model_creator: Bud
model_link: https://huggingface.co/budecosystem/genz-70b
model_name: GenZ 70B
model_type: llama
pipeline_tag: text-generation
quantized_by: TheBloke
TheBlokeAI

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

GenZ 70B - GGUF

Description

This repo contains GGUF format model files for Bud's GenZ 70B.

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.

The key benefit of GGUF is that it is a extensible, future-proof format which stores more information about the model as metadata. It also includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates.

As of August 25th, here is a list of clients and libraries that are known to support GGUF:

  • llama.cpp
  • text-generation-webui, the most widely used web UI. Supports GGUF with GPU acceleration via the ctransformers backend - llama-cpp-python backend should work soon too.
  • KoboldCpp, now supports GGUF as of release 1.41! A powerful GGML web UI, with full GPU accel. Especially good for story telling.
  • LoLLMS Web UI, should now work, choose the c_transformers backend. A great web UI with many interesting features. Supports CUDA GPU acceleration.
  • ctransformers, now supports GGUF as of version 0.2.24! A Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
  • llama-cpp-python, supports GGUF as of version 0.1.79. A Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
  • candle, added GGUF support on August 22nd. Candle is a Rust ML framework with a focus on performance, including GPU support, and ease of use.

The clients and libraries below are expecting to add GGUF support shortly:

  • LM Studio, should be updated by end August 25th.

Repositories available

Prompt template: User-Assistant-Newlines 

### User:
{prompt}

### Assistant:

Compatibility

These quantised GGUF files are compatible with llama.cpp from August 21st 2023 onwards, as of commit 6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9

As of August 24th 2023 they are now compatible with KoboldCpp, release 1.41 and later.

They are are not yet compatible with any other third-party UIS, libraries or utilities but this is expected to change very soon.

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
genz-70b.Q2_K.gguf Q2_K 2 29.28 GB 31.78 GB smallest, significant quality loss - not recommended for most purposes
genz-70b.Q3_K_S.gguf Q3_K_S 3 29.92 GB 32.42 GB very small, high quality loss
genz-70b.Q3_K_M.gguf Q3_K_M 3 33.19 GB 35.69 GB very small, high quality loss
genz-70b.Q3_K_L.gguf Q3_K_L 3 36.15 GB 38.65 GB small, substantial quality loss
genz-70b.Q4_K_S.gguf Q4_K_S 4 39.07 GB 41.57 GB small, greater quality loss
genz-70b.Q4_K_M.gguf Q4_K_M 4 41.42 GB 43.92 GB medium, balanced quality - recommended
genz-70b.Q5_K_S.gguf Q5_K_S 5 47.46 GB 49.96 GB large, low quality loss - recommended
genz-70b.Q5_K_M.gguf Q5_K_M 5 48.75 GB 51.25 GB large, very low quality loss - recommended
genz-70b.Q6_K.gguf q6_K 6 56.82 GB 59.32 GB very large, extremely low quality loss
genz-70b.Q8_0.gguf q8_0 8 73.29 GB 75.79 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.

Q6_K and Q8_0 files are split and require joining

Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.

Click for instructions regarding Q6_K and Q8_0 files

q6_K

Please download:

  • genz-70b.Q6_K.gguf-split-a
  • genz-70b.Q6_K.gguf-split-b

q8_0

Please download:

  • genz-70b.Q8_0.gguf-split-a
  • genz-70b.Q8_0.gguf-split-b

To join the files, do the following:

Linux and macOS:

cat genz-70b.Q6_K.gguf-split-* > genz-70b.Q6_K.gguf && rm genz-70b.Q6_K.gguf-split-*
cat genz-70b.Q8_0.gguf-split-* > genz-70b.Q8_0.gguf && rm genz-70b.Q8_0.gguf-split-*

Windows command line:

COPY /B genz-70b.Q6_K.gguf-split-a + genz-70b.Q6_K.gguf-split-b genz-70b.Q6_K.gguf
del genz-70b.Q6_K.gguf-split-a genz-70b.Q6_K.gguf-split-b

COPY /B genz-70b.Q8_0.gguf-split-a + genz-70b.Q8_0.gguf-split-b genz-70b.Q8_0.gguf
del genz-70b.Q8_0.gguf-split-a genz-70b.Q8_0.gguf-split-b

How to run in llama.cpp

Make sure you are using llama.cpp from commit 6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9 or later.

For compatibility with older versions of llama.cpp, or for use with third-party clients and libaries, please use GGML files instead.

./main -t 10 -ngl 32 -m genz-70b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"

Change -t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8.

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 this model. 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.

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 here: text-generation-webui/docs/llama.cpp.md.

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!

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: Kacper Wikieł, knownsqashed, Leonard Tan, Asp the Wyvern, Daniel P. Andersen, Luke Pendergrass, Stanislav Ovsiannikov, RoA, Dave, Ai Maven, Kalila, Will Dee, Imad Khwaja, Nitin Borwankar, Joseph William Delisle, Tony Hughes, Cory Kujawski, Rishabh Srivastava, Russ Johnson, Stephen Murray, Lone Striker, Johann-Peter Hartmann, Elle, J, Deep Realms, SuperWojo, Raven Klaugh, Sebastain Graf, ReadyPlayerEmma, Alps Aficionado, Mano Prime, Derek Yates, Gabriel Puliatti, Mesiah Bishop, Magnesian, Sean Connelly, biorpg, Iucharbius, Olakabola, Fen Risland, Space Cruiser, theTransient, Illia Dulskyi, Thomas Belote, Spencer Kim, Pieter, John Detwiler, Fred von Graf, Michael Davis, Swaroop Kallakuri, subjectnull, Clay Pascal, Subspace Studios, Chris Smitley, Enrico Ros, usrbinkat, Steven Wood, alfie_i, David Ziegler, Willem Michiel, Matthew Berman, Andrey, Pyrater, Jeffrey Morgan, vamX, LangChain4j, Luke @flexchar, Trenton Dambrowitz, Pierre Kircher, Alex, Sam, James Bentley, Edmond Seymore, Eugene Pentland, Pedro Madruga, Rainer Wilmers, Dan Guido, Nathan LeClaire, Spiking Neurons AB, Talal Aujan, zynix, Artur Olbinski, Michael Levine, 阿明, K, John Villwock, Nikolai Manek, Femi Adebogun, senxiiz, Deo Leter, NimbleBox.ai, Viktor Bowallius, Geoffrey Montalvo, Mandus, Ajan Kanaga, ya boyyy, Jonathan Leane, webtim, Brandon Frisco, danny, Alexandros Triantafyllidis, Gabriel Tamborski, Randy H, terasurfer, Vadim, Junyu Yang, Vitor Caleffi, Chadd, transmissions 11

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Bud's GenZ 70B

~ GenZ ~

Democratizing access to LLMs for the open-source community.
Let's advance AI, together.

Introduction 🎉

Welcome to GenZ, an advanced Large Language Model (LLM) fine-tuned on the foundation of Meta's open-source Llama V2 70B parameter model. At Bud Ecosystem, we believe in the power of open-source collaboration to drive the advancement of technology at an accelerated pace. Our vision is to democratize access to fine-tuned LLMs, and to that end, we will be releasing a series of models across different parameter counts (7B, 13B, and 70B) and quantizations (32-bit and 4-bit) for the open-source community to use, enhance, and build upon.

The smaller quantization version of our models makes them more accessible, enabling their use even on personal computers. This opens up a world of possibilities for developers, researchers, and enthusiasts to experiment with these models and contribute to the collective advancement of language model technology.

GenZ isn't just a powerful text generator—it's a sophisticated AI assistant, capable of understanding and responding to user prompts with high-quality responses. We've taken the robust capabilities of Llama V2 and fine-tuned them to offer a more user-focused experience. Whether you're seeking informative responses or engaging interactions, GenZ is designed to deliver.

And this isn't the end. It's just the beginning of a journey towards creating more advanced, more efficient, and more accessible language models. We invite you to join us on this exciting journey. 🚀

Milestone Releases ️🏁

[21 August 2023] GenZ-70B : We're excited to announce the release of our Genz 70BB model. Experience the advancements by downloading the model from HuggingFace.

[27 July 2023] GenZ-13B V2 (ggml) : Announcing our GenZ-13B v2 with ggml. This variant of GenZ can run inferencing using only CPU and without the need of GPU. Download the model from HuggingFace.

[27 July 2023] GenZ-13B V2 (4-bit) : Announcing our GenZ-13B v2 with 4-bit quantisation. Enabling inferencing with much lesser GPU memory than the 32-bit variant. Download the model from HuggingFace.

[26 July 2023] GenZ-13B V2 : We're excited to announce the release of our Genz 13B v2 model, a step forward with improved evaluation results compared to v1. Experience the advancements by downloading the model from HuggingFace.

[20 July 2023] GenZ-13B : We marked an important milestone with the release of the Genz 13B model. The journey began here, and you can partake in it by downloading the model from Hugging Face.

Getting Started on Hugging Face 🤗

Getting up and running with our models on Hugging Face is a breeze. Follow these steps:

1️⃣ : Import necessary modules

Start by importing the necessary modules from the ‘transformers’ library and ‘torch’.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("budecosystem/genz-70b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("budecosystem/genz-70b", torch_dtype=torch.bfloat16, rope_scaling={"type": "dynamic", "factor": 2})

prompt = "### User:\nWrite a python flask code for login management\n\n### Assistant:\n"

inputs = tokenizer(prompt, return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))

Want to interact with the model in a more intuitive way? We have a Gradio interface set up for that. Head over to our GitHub page, clone the repository, and run the ‘generate.py’ script to try it out. Happy experimenting! 😄

Why Use GenZ? 💡

You might be wondering, "Why should I choose GenZ over a pretrained model?" The answer lies in the extra mile we've gone to fine-tune our models.

While pretrained models are undeniably powerful, GenZ brings something extra to the table. We've fine-tuned it with curated datasets, which means it has additional skills and capabilities beyond what a pretrained model can offer. Whether you need it for a simple task or a complex project, GenZ is up for the challenge.

What's more, we are committed to continuously enhancing GenZ. We believe in the power of constant learning and improvement. That's why we'll be regularly fine-tuning our models with various curated datasets to make them even better. Our goal is to reach the state of the art and beyond - and we're committed to staying the course until we get there.

But don't just take our word for it. We've provided detailed evaluations and performance details in a later section, so you can see the difference for yourself.

Choose GenZ and join us on this journey. Together, we can push the boundaries of what's possible with large language models.

Model Card for GenZ 70B 📄

Here's a quick overview of everything you need to know about GenZ 70B.

Model Details:

  • Developed by: Bud Ecosystem
  • Base pretrained model type: Llama V2 70B
  • Model Architecture: GenZ 70B, fine-tuned on Llama V2 70B, is an auto-regressive language model that employs an optimized transformer architecture. The fine-tuning process for GenZ 70B leveraged Supervised Fine-Tuning (SFT)
  • License: The model is available for commercial use under a custom commercial license. For more information, please visit: Meta AI Model and Library Downloads

Intended Use 💼

When we created GenZ 70B, we had a clear vision of how it could be used to push the boundaries of what's possible with large language models. We also understand the importance of using such models responsibly. Here's a brief overview of the intended and out-of-scope uses for GenZ 70B.

Direct Use

GenZ 70B is designed to be a powerful tool for research on large language models. It's also an excellent foundation for further specialization and fine-tuning for specific use cases, such as:

  • Text summarization
  • Text generation
  • Chatbot creation
  • And much more!

Out-of-Scope Use 🚩

While GenZ 70B is versatile, there are certain uses that are out of scope:

  • Production use without adequate assessment of risks and mitigation
  • Any use cases which may be considered irresponsible or harmful
  • Use in any manner that violates applicable laws or regulations, including trade compliance laws
  • Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2

Remember, GenZ 70B, like any large language model, is trained on a large-scale corpora representative of the web, and therefore, may carry the stereotypes and biases commonly encountered online.

Recommendations 🧠

We recommend users of GenZ 70B to consider fine-tuning it for the specific set of tasks of interest. Appropriate precautions and guardrails should be taken for any production use. Using GenZ 70B responsibly is key to unlocking its full potential while maintaining a safe and respectful environment.

Training Details 📚

When fine-tuning GenZ 70B, we took a meticulous approach to ensure we were building on the solid base of the pretrained Llama V2 70B model in the most effective way. Here's a look at the key details of our training process:

Fine-Tuning Training Data

For the fine-tuning process, we used a carefully curated mix of datasets. These included data from OpenAssistant, an instruction fine-tuning dataset, and Thought Source for the Chain Of Thought (CoT) approach. This diverse mix of data sources helped us enhance the model's capabilities across a range of tasks.

Hyperparameters

Here are the hyperparameters we used for fine-tuning:

Hyperparameter Value
Warmup Ratio 0.04
Learning Rate Scheduler Type Cosine
Learning Rate 2e-5
Number of Training Epochs 3
Per Device Training Batch Size 4
Gradient Accumulation Steps 4
Precision FP16
Optimizer AdamW

Evaluations 🎯

Evaluating our model is a key part of our fine-tuning process. It helps us understand how our model is performing and how it stacks up against other models. Here's a look at some of the key evaluations for GenZ 70B:

Benchmark Comparison

We've compared GenZ models to understand the improvements our fine-tuning has achieved.

Model Name MT Bench MMLU Human Eval Hellaswag BBH
Genz 13B 6.12 53.62 17.68 77.38 37.76
Genz 13B v2 6.79 53.68 21.95 77.48 38.1
Genz 70B 7.33 70.32

MT Bench Score

A key evaluation metric we use is the MT Bench score. This score provides a comprehensive assessment of our model's performance across a range of tasks.

Looking Ahead 👀

We're excited about the journey ahead with GenZ. We're committed to continuously improving and enhancing our models, and we're excited to see what the open-source community will build with them. We believe in the power of collaboration, and we can't wait to see what we can achieve together.

Remember, we're just getting started. This is just the beginning of a journey that we believe will revolutionize the world of large language models. We invite you to join us on this exciting journey. Together, we can push the boundaries of what's possible with AI. 🚀

Check the GitHub for the code -> GenZ