TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
This repo contains GGUF format model files for Grimulkan's Goliath LongLORA 120B Rope8 32K.
These files were quantised using hardware kindly provided by Massed Compute.
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:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
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.
The new methods available are:
Refer to the Provided Files table below to see what files use which methods, and how.
| Name | Quant method | Bits | Size | Max RAM required | Use case |
|---|---|---|---|---|---|
| goliath-longlora-120b-rope8-32k-fp16.Q2_K.gguf | Q2_K | 2 | 43.25 GB | 45.75 GB | significant quality loss - not recommended for most purposes |
| goliath-longlora-120b-rope8-32k-fp16.Q3_K_S.gguf | Q3_K_S | 3 | 50.71 GB | 53.21 GB | very small, high quality loss |
| goliath-longlora-120b-rope8-32k-fp16.Q3_K_M.gguf | Q3_K_M | 3 | 56.59 GB | 59.09 GB | very small, high quality loss |
| goliath-longlora-120b-rope8-32k-fp16.Q3_K_L.gguf | Q3_K_L | 3 | 61.67 GB | 64.17 GB | small, substantial quality loss |
| goliath-longlora-120b-rope8-32k-fp16.Q4_0.gguf | Q4_0 | 4 | 66.31 GB | 68.81 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| goliath-longlora-120b-rope8-32k-fp16.Q4_K_S.gguf | Q4_K_S | 4 | 66.81 GB | 69.31 GB | small, greater quality loss |
| goliath-longlora-120b-rope8-32k-fp16.Q4_K_M.gguf | Q4_K_M | 4 | 70.64 GB | 73.14 GB | medium, balanced quality - recommended |
| goliath-longlora-120b-rope8-32k-fp16.Q5_0.gguf | Q5_0 | 5 | 81.00 GB | 83.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| goliath-longlora-120b-rope8-32k-fp16.Q5_K_S.gguf | Q5_K_S | 5 | 81.00 GB | 83.50 GB | large, low quality loss - recommended |
| goliath-longlora-120b-rope8-32k-fp16.Q5_K_M.gguf | Q5_K_M | 5 | 83.22 GB | 85.72 GB | large, very low quality loss - recommended |
| goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf | Q6_K | 6 | 96.60 GB | 99.10 GB | very large, extremely low quality loss |
| goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf | Q8_0 | 8 | 125.12 GB | 127.62 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.
Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
Please download:
goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf-split-agoliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf-split-bPlease download:
goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf-split-agoliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf-split-bTo join the files, do the following:
Linux and macOS:
cat goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf-split-* > goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf && rm goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf-split-*
cat goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf-split-* > goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf && rm goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf-split-*
Windows command line:
COPY /B goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf-split-a + goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf-split-b goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf
del goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf-split-a goliath-longlora-120b-rope8-32k-fp16.Q6_K.gguf-split-b
COPY /B goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf-split-a + goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf-split-b goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf
del goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf-split-a goliath-longlora-120b-rope8-32k-fp16.Q8_0.gguf-split-b
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:
text-generation-webui
Under Download Model, you can enter the model repo: TheBloke/Goliath-longLORA-120b-rope8-32k-fp16-GGUF and below it, a specific filename to download, such as: goliath-longlora-120b-rope8-32k-fp16.Q4_K_M.gguf.
Then click Download.
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/Goliath-longLORA-120b-rope8-32k-fp16-GGUF goliath-longlora-120b-rope8-32k-fp16.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
You can also download multiple files at once with a pattern:
huggingface-cli download TheBloke/Goliath-longLORA-120b-rope8-32k-fp16-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/Goliath-longLORA-120b-rope8-32k-fp16-GGUF goliath-longlora-120b-rope8-32k-fp16.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.
llama.cpp command
Make sure you are using llama.cpp from commit d0cee0d or later.
./main -ngl 35 -m goliath-longlora-120b-rope8-32k-fp16.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
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
text-generation-webui
Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.
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.
For full documentation, please see: llama-cpp-python docs.
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
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="./goliath-longlora-120b-rope8-32k-fp16.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(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # 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="./goliath-longlora-120b-rope8-32k-fp16.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."
}
]
)
Here are guides on using llama-cpp-python and ctransformers with LangChain:
For further support, and discussions on these models and AI in general, join us at:
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.
This is an interleaved merge of Xwin-longLORA-70b-rope8-32k-fp16 and Euryale-1.3-longLORA-70b-rope8-32k-fp16, using the same merge formula as alpindale's goliath-120b.
There is no additional fine-tuning. The resulting model seems to not be broken... you can test whether it is truly the original model + 32K capability (use linear rope scaling 8).
ChuckMcSneed did a benchmark here, indicating 30% degradation with 8x the context length.
A 6-bit EXL2 quantization is available here. More EXL2 quants here, thanks to aikitoria.
See this discussion for how the original 70B merges were created with longLORA.