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ToRA 70B v1.0 - GGUF

Model creator: LLM-Agents
Original model: ToRA 70B v1.0

Description

This repo contains GGUF format model files for LLM-Agents's ToRA 70B v1.0.

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 incomplate 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.
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: ToRA

<|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
tora-70b-v1.0.Q2_K.gguf	Q2_K	2	29.28 GB	31.78 GB	smallest, significant quality loss - not recommended for most purposes
tora-70b-v1.0.Q3_K_S.gguf	Q3_K_S	3	29.92 GB	32.42 GB	very small, high quality loss
tora-70b-v1.0.Q3_K_M.gguf	Q3_K_M	3	33.19 GB	35.69 GB	very small, high quality loss
tora-70b-v1.0.Q3_K_L.gguf	Q3_K_L	3	36.15 GB	38.65 GB	small, substantial quality loss
tora-70b-v1.0.Q4_0.gguf	Q4_0	4	38.87 GB	41.37 GB	legacy; small, very high quality loss - prefer using Q3_K_M
tora-70b-v1.0.Q4_K_S.gguf	Q4_K_S	4	39.07 GB	41.57 GB	small, greater quality loss
tora-70b-v1.0.Q4_K_M.gguf	Q4_K_M	4	41.42 GB	43.92 GB	medium, balanced quality - recommended
tora-70b-v1.0.Q5_0.gguf	Q5_0	5	47.46 GB	49.96 GB	legacy; medium, balanced quality - prefer using Q4_K_M
tora-70b-v1.0.Q5_K_S.gguf	Q5_K_S	5	47.46 GB	49.96 GB	large, low quality loss - recommended
tora-70b-v1.0.Q5_K_M.gguf	Q5_K_M	5	48.75 GB	51.25 GB	large, very low quality loss - recommended
tora-70b-v1.0.Q6_K.gguf	Q6_K	6	56.59 GB	59.09 GB	very large, extremely low quality loss
tora-70b-v1.0.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:

tora-70b-v1.0.Q6_K.gguf-split-a
tora-70b-v1.0.Q6_K.gguf-split-b

q8_0

Please download:

tora-70b-v1.0.Q8_0.gguf-split-a
tora-70b-v1.0.Q8_0.gguf-split-b

To join the files, do the following:

Linux and macOS:

cat tora-70b-v1.0.Q6_K.gguf-split-* > tora-70b-v1.0.Q6_K.gguf && rm tora-70b-v1.0.Q6_K.gguf-split-*
cat tora-70b-v1.0.Q8_0.gguf-split-* > tora-70b-v1.0.Q8_0.gguf && rm tora-70b-v1.0.Q8_0.gguf-split-*

Windows command line:

COPY /B tora-70b-v1.0.Q6_K.gguf-split-a + tora-70b-v1.0.Q6_K.gguf-split-b tora-70b-v1.0.Q6_K.gguf
del tora-70b-v1.0.Q6_K.gguf-split-a tora-70b-v1.0.Q6_K.gguf-split-b

COPY /B tora-70b-v1.0.Q8_0.gguf-split-a + tora-70b-v1.0.Q8_0.gguf-split-b tora-70b-v1.0.Q8_0.gguf
del tora-70b-v1.0.Q8_0.gguf-split-a tora-70b-v1.0.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/tora-70B-v1.0-GGUF and below it, a specific filename to download, such as: tora-70b-v1.0.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/tora-70B-v1.0-GGUF tora-70b-v1.0.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/tora-70B-v1.0-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/tora-70B-v1.0-GGUF tora-70b-v1.0.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 tora-70b-v1.0.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 here: text-generation-webui/docs/llama.cpp.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:

# 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

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/tora-70B-v1.0-GGUF", model_file="tora-70b-v1.0.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: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: LLM-Agents's ToRA 70B v1.0

ToRA: A Tool-Integrated Reasoning Agent
for Mathematical Problem Solving

[🌐 Website] • [📜 Paper] • [🤗 HF Models] • [🐱 GitHub]
[🐦 Twitter] • [💬 Reddit] • [🍀 Unofficial Blog]

Repo for "ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving"

🔥 News

[2023/10/08] 🔥🔥🔥 All ToRA models released at HuggingFace!!!
[2023/09/29] ToRA paper, repo, and website released.

💡 Introduction

ToRA is a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical reasoning problems by interacting with tools, e.g., computation libraries and symbolic solvers. ToRA series seamlessly integrate natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of external tools.

Model	Size	GSM8k	MATH	AVG@10 math tasks^†
GPT-4	-	92.0	42.5	78.3
GPT-4 (PAL)	-	94.2	51.8	86.4
ToRA-7B	7B	68.8	40.1	62.4
ToRA-Code-7B	7B	72.6	44.6	66.5
ToRA-13B	13B	72.7	43.0	65.9
ToRA-Code-13B	13B	75.8	48.1	71.3
ToRA-Code-34B^*	34B	80.7	51.0	74.8
ToRA-70B	70B	84.3	49.7	76.9

^*ToRA-Code-34B is currently the first and only open-source model to achieve over 50% accuracy (pass@1) on the MATH dataset, which significantly outperforms GPT-4’s CoT result (51.0 vs. 42.5), and is competitive with GPT-4 solving problems with programs. By open-sourcing our codes and models, we hope more breakthroughs will come!
^†10 math tasks include GSM8k, MATH, GSM-Hard, SVAMP, TabMWP, ASDiv, SingleEQ, SingleOP, AddSub, and MultiArith.

⚡️ Training

The models are trained on ToRA-Corpus 16k, which contains tool-integrated reasoning trajectories of MATH and GSM8k from GPT-4.

We use imitation learning (i.e., SFT) to fine-tune the models, and then apply our proposed output space shaping to improve tool-integrated reasoning behaviors. Please refer to the paper for more details.

🪁 Inference & Evaluation

Please refer to ToRA's GitHub repo for inference, evaluation, and training code.

☕️ Citation

If you find this repository helpful, please consider citing our paper:

@misc{gou2023tora,
      title={ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving},
      author={Zhibin Gou and Zhihong Shao and Yeyun Gong and yelong shen and Yujiu Yang and Minlie Huang and Nan Duan and Weizhu Chen},
      year={2023},
      eprint={2309.17452},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

TheBloke
/

tora-70B-v1.0-GGUF

ToRA 70B v1.0 - GGUF

Description

About GGUF

Repositories available

Prompt template: ToRA

Compatibility

Explanation of quantisation methods

Provided files

Q6_K and Q8_0 files are split and require joining

q6_K

q8_0

How to download GGUF files

In `text-generation-webui`

On the command line, including multiple files at once

Example `llama.cpp` command

How to run in `text-generation-webui`

How to run from Python code

How to load this model in Python code, using ctransformers

First install the package

Simple ctransformers example code

How to use with LangChain

Discord

Thanks, and how to contribute

Original model card: LLM-Agents's ToRA 70B v1.0

ToRA: A Tool-Integrated Reasoning Agent
for Mathematical Problem Solving

🔥 News

💡 Introduction

⚡️ Training

🪁 Inference & Evaluation

☕️ Citation

Model tree for TheBloke/tora-70B-v1.0-GGUF

Datasets used to train TheBloke/tora-70B-v1.0-GGUF

ToRA 70B v1.0 - GGUF

Description

About GGUF

Repositories available

Prompt template: ToRA

Compatibility

Explanation of quantisation methods

Provided files

Q6_K and Q8_0 files are split and require joining

q6_K

q8_0

How to download GGUF files

In text-generation-webui

On the command line, including multiple files at once

Example llama.cpp command

How to run in text-generation-webui

How to run from Python code

How to load this model in Python code, using ctransformers

First install the package

Simple ctransformers example code

How to use with LangChain

Discord

Thanks, and how to contribute

Original model card: LLM-Agents's ToRA 70B v1.0

ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

🔥 News

💡 Introduction

⚡️ Training

🪁 Inference & Evaluation

☕️ Citation

Model tree for TheBloke/tora-70B-v1.0-GGUF

Datasets used to train TheBloke/tora-70B-v1.0-GGUF

In `text-generation-webui`

Example `llama.cpp` command

How to run in `text-generation-webui`

ToRA: A Tool-Integrated Reasoning Agent
for Mathematical Problem Solving