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.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
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.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
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.

Repositories available

Prompt template: Tulu

<|user|>
{prompt}
<|assistant|>

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
tulu-2-70b.Q2_K.gguf	Q2_K	2	29.28 GB	31.78 GB	smallest, significant quality loss - not recommended for most purposes
tulu-2-70b.Q3_K_S.gguf	Q3_K_S	3	29.92 GB	32.42 GB	very small, high quality loss
tulu-2-70b.Q3_K_M.gguf	Q3_K_M	3	33.19 GB	35.69 GB	very small, high quality loss
tulu-2-70b.Q3_K_L.gguf	Q3_K_L	3	36.15 GB	38.65 GB	small, substantial quality loss
tulu-2-70b.Q4_0.gguf	Q4_0	4	38.87 GB	41.37 GB	legacy; small, very high quality loss - prefer using Q3_K_M
tulu-2-70b.Q4_K_S.gguf	Q4_K_S	4	39.07 GB	41.57 GB	small, greater quality loss
tulu-2-70b.Q4_K_M.gguf	Q4_K_M	4	41.42 GB	43.92 GB	medium, balanced quality - recommended
tulu-2-70b.Q5_0.gguf	Q5_0	5	47.46 GB	49.96 GB	legacy; medium, balanced quality - prefer using Q4_K_M
tulu-2-70b.Q5_K_S.gguf	Q5_K_S	5	47.46 GB	49.96 GB	large, low quality loss - recommended
tulu-2-70b.Q5_K_M.gguf	Q5_K_M	5	48.75 GB	51.25 GB	large, very low quality loss - recommended
tulu-2-70b.Q6_K.gguf	Q6_K	6	56.59 GB	59.09 GB	very large, extremely low quality loss
tulu-2-70b.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:

tulu-2-70b.Q6_K.gguf-split-a
tulu-2-70b.Q6_K.gguf-split-b

q8_0

Please download:

tulu-2-70b.Q8_0.gguf-split-a
tulu-2-70b.Q8_0.gguf-split-b

To join the files, do the following:

Linux and macOS:

cat tulu-2-70b.Q6_K.gguf-split-* > tulu-2-70b.Q6_K.gguf && rm tulu-2-70b.Q6_K.gguf-split-*
cat tulu-2-70b.Q8_0.gguf-split-* > tulu-2-70b.Q8_0.gguf && rm tulu-2-70b.Q8_0.gguf-split-*

Windows command line:

COPY /B tulu-2-70b.Q6_K.gguf-split-a + tulu-2-70b.Q6_K.gguf-split-b tulu-2-70b.Q6_K.gguf
del tulu-2-70b.Q6_K.gguf-split-a tulu-2-70b.Q6_K.gguf-split-b

COPY /B tulu-2-70b.Q8_0.gguf-split-a + tulu-2-70b.Q8_0.gguf-split-b tulu-2-70b.Q8_0.gguf
del tulu-2-70b.Q8_0.gguf-split-a tulu-2-70b.Q8_0.gguf-split-b

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/tulu-2-70B-GGUF and below it, a specific filename to download, such as: tulu-2-70b.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/tulu-2-70B-GGUF tulu-2-70b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage

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

huggingface-cli download TheBloke/tulu-2-70B-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/tulu-2-70B-GGUF tulu-2-70b.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 32 -m tulu-2-70b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|user|>\n{prompt}\n<|assistant|>"

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.

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.

How to load this model in Python code, using ctransformers

First install the package

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

shell

# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers

Simple ctransformers example code

python

from ctransformers import AutoModelForCausalLM

# 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 = AutoModelForCausalLM.from_pretrained("TheBloke/tulu-2-70B-GGUF", model_file="tulu-2-70b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)

print(llm("AI is going to"))

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.

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.

Original model card: Allen Institute for AI's Tulu 2 70B

Model Card for Tulu V2 70B

Tulu is a series of language models that are trained to act as helpful assistants. Tulu V2 70B is a fine-tuned version of Llama 2 that was trained on a mix of publicly available, synthetic and human datasets.

For more details, read the paper: Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2 .

Model description

Model type: A model belonging to a suite of instruction and RLHF tuned chat models on a mix of publicly available, synthetic and human-created datasets.
Language(s) (NLP): Primarily English
License: AI2 ImpACT Low-risk license.
Finetuned from model: meta-llama/Llama-2-70b-hf

Model Sources

Repository: https://github.com/allenai/https://github.com/allenai/open-instruct
Model Family: Other models and the dataset are found in the Tulu V2 collection.

Performance

Model	Size	Alignment	MT-Bench (score)	AlpacaEval (win rate %)
Tulu-v2-7b 🐪	7B	SFT	6.30	73.9
Tulu-v2-dpo-7b 🐪	7B	DPO	6.29	85.1
Tulu-v2-13b 🐪	13B	SFT	6.70	78.9
Tulu-v2-dpo-13b 🐪	13B	DPO	7.00	89.5
Tulu-v2-70b 🐪	70B	SFT	7.49	86.6
Tulu-v2-dpo-70b 🐪	70B	DPO	7.89	95.1

Input Format

The model is trained to use the following format (note the newlines):

<|user|>
Your message here!
<|assistant|>

For best results, format all inputs in this manner. Make sure to include a newline after <|assistant|>, this can affect generation quality quite a bit.

Intended uses & limitations

The model was fine-tuned on a filtered and preprocessed of the Tulu V2 mix dataset, which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs.

Bias, Risks, and Limitations

The Tulu models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 2 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.

Training hyperparameters

The following hyperparameters were used during DPO training:

learning_rate: 1e-5
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.03
num_epochs: 2.0

Citation

If you find Tulu 2 is useful in your work, please cite it with:

@misc{ivison2023camels,
      title={Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2},
      author={Hamish Ivison and Yizhong Wang and Valentina Pyatkin and Nathan Lambert and Matthew Peters and Pradeep Dasigi and Joel Jang and David Wadden and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
      year={2023},
      eprint={2311.10702},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Model card adapted from Zephyr Beta

TheBloke/tulu-2-70B-GGUF

Tulu 2 70B - GGUF

Description

About GGUF