TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
This repo contains GGUF format model files for brucethemoose's Yi 34B 200K DARE MegaMerge V8.
The GGUF files in this repo were made using new k-quant methods, added Jan 2024.
They will only be compatible with llama.cpp from Jan 4th onwards. Other clients may not have been updated for support yet.
The new GGUF k-quant method enables use of an "importance matrix", which is similar in concept to the calibration datasets used by GPTQ, AWQ and EXL2. This improves GGUF quantization quality.
The dataset used for generating the importance matrix for these GGUFs was: VMware open-instruct (5K lines).
Use of the importance matrix enables providing new quant formats: IQ2_XXS, IQ2_XS and Q2_K_S.
Note: adding support for this new GGUF quant method is still a work-in-progress for me. Other GGUF repos I'm creating won't necessarily have this, at least for the next couple of days.
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
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 |
|---|---|---|---|---|---|
| yi-34b-200k-dare-megamerge-v8.IQ2_XXS.gguf | IQ2_XXS | 2 | 9.31 GB | 11.81 GB | smallest size. 2.06 bpw. New IQuant method, Jan 2024 |
| yi-34b-200k-dare-megamerge-v8.IQ2_XS.gguf | IQ2_XS | 2 | 10.31 GB | 12.81 GB | second smallest size. 2.31 bpw quant. New IQuant method, Jan 2024 |
| yi-34b-200k-dare-megamerge-v8.Q2_K_S.gguf | Q2_K_S | 2 | 11.76 GB | 14.26 GB | significant quality loss - not recommended for most purposes. New method, Jan 2024 |
| yi-34b-200k-dare-megamerge-v8.Q2_K.gguf | Q2_K | 2 | 12.77 GB | 15.27 GB | significant quality loss - not recommended for most purposes |
| yi-34b-200k-dare-megamerge-v8.Q3_K_S.gguf | Q3_K_S | 3 | 14.96 GB | 17.46 GB | very small, high quality loss |
| yi-34b-200k-dare-megamerge-v8.Q3_K_M.gguf | Q3_K_M | 3 | 16.65 GB | 19.15 GB | very small, high quality loss |
| yi-34b-200k-dare-megamerge-v8.Q3_K_L.gguf | Q3_K_L | 3 | 18.14 GB | 20.64 GB | small, substantial quality loss |
| yi-34b-200k-dare-megamerge-v8.Q4_0.gguf | Q4_0 | 4 | 19.47 GB | 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| yi-34b-200k-dare-megamerge-v8.Q4_K_S.gguf | Q4_K_S | 4 | 19.60 GB | 22.10 GB | small, greater quality loss |
| yi-34b-200k-dare-megamerge-v8.Q4_K_M.gguf | Q4_K_M | 4 | 20.66 GB | 23.16 GB | medium, balanced quality - recommended |
| yi-34b-200k-dare-megamerge-v8.Q5_0.gguf | Q5_0 | 5 | 23.71 GB | 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| yi-34b-200k-dare-megamerge-v8.Q5_K_S.gguf | Q5_K_S | 5 | 23.71 GB | 26.21 GB | large, low quality loss - recommended |
| yi-34b-200k-dare-megamerge-v8.Q5_K_M.gguf | Q5_K_M | 5 | 24.32 GB | 26.82 GB | large, very low quality loss - recommended |
| yi-34b-200k-dare-megamerge-v8.Q6_K.gguf | Q6_K | 6 | 28.21 GB | 30.71 GB | very large, extremely low quality loss |
| yi-34b-200k-dare-megamerge-v8.Q8_0.gguf | Q8_0 | 8 | 36.54 GB | 39.04 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 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/Yi-34B-200K-DARE-megamerge-v8-GGUF and below it, a specific filename to download, such as: yi-34b-200k-dare-megamerge-v8.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/Yi-34B-200K-DARE-megamerge-v8-GGUF yi-34b-200k-dare-megamerge-v8.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/Yi-34B-200K-DARE-megamerge-v8-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/Yi-34B-200K-DARE-megamerge-v8-GGUF yi-34b-200k-dare-megamerge-v8.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 yi-34b-200k-dare-megamerge-v8.Q4_K_M.gguf --color -c 200000 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "SYSTEM: {system_message}\nUSER: {prompt}\nASSISTANT:"
Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change -c 200000 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="./yi-34b-200k-dare-megamerge-v8.Q4_K_M.gguf", # Download the model file first
n_ctx=200000, # 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(
"SYSTEM: {system_message}\nUSER: {prompt}\nASSISTANT:", # 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="./yi-34b-200k-dare-megamerge-v8.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.
A merge of many Yi 34B 200K models using the new DARE Ties method via mergekit. The goal is to create a merge model that excels at 32K+ context performance, without any additional finetuning.
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
It might recognize ChatML, and possibly Alpaca-like formats. Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/
Being a Yi model, run a lower temperature with 0.05 or higher MinP, a little repetition penalty, maybe mirostat with a low tau, and no other samplers. Yi tends to run "hot" by default, and it really needs a low temperature + MinP to cull Yi's huge vocabulary. See the explanation here: https://github.com/ggerganov/llama.cpp/pull/3841
24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. 16GB GPUs can still run the high context with aggressive quantization.
I recommend exl2 quantizations profiled on data similar to the desired task. It is especially sensitive to the quantization data at low bpw. I've upload my own fiction-oriented quantizations here: https://huggingface.co/collections/brucethemoose/most-recent-merge-65742644ca03b6c514afa204
Lonestriker has also uploaded more general purpose quantizations here: https://huggingface.co/models?sort=trending&search=LoneStriker+Yi-34B-200K-DARE-megamerge-v8
To load/train this in full-context backends like transformers, you must change max_position_embeddings in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends like exllamav2, litellm or unsloth.
See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5#testing-notes
An intermediate merge model was created to try and extend the context of several 4k models before adding them to the main merge, as seen in the "megamerge" recipe below. I can upload this upon request
In addition, the weight gradients are biased towards Vicuna-format models in the first few layers to try and "emphasize" the Orca-Vicuna prompt template. How sucessful this is remains to be seen.
This model was merged using the DARE TIES merge method using /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama
# No parameters necessary for base model
- model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
#200K base to extend the context of 4K models, max density as we *want* it to 'interfere'
parameters:
weight: 0.33
density: 1
- model: /home/alpha/Models/Raw/Weyaxi_Nous-Hermes-2-SUS-Chat-34B-Slerp
parameters:
weight: 0.15
density: 0.36
- model: /home/alpha/Models/Raw/jondurbin_bagel-dpo-34b-v0.2
#Mix dpo with sft to tone down dpo
parameters:
weight: 0.06
density: 0.36
- model: /home/alpha/Models/Raw/jondurbin_bagel-34b-v0.2
parameters:
weight: 0.06
density: 0.41
- model: /home/alpha/Models/Raw/bhenrym14_platypus-yi-34b
#Vicuna format
parameters:
weight: 0.19
density: 0.41
# - model: /home/alpha/Models/Raw/01-ai_Yi-34B-Chat #+/home/alpha/Models/Raw/Doctor-Shotgun_limarpv3-yi-llama-34b-lora
# #Can't get lora OR base model to work without erroring out?
# parameters:
# weight: 0.04
# density: 0.36
- model: /home/alpha/Models/Raw/TriadParty_deepsex-34b
#Base model with no prompt
parameters:
weight: 0.21
density: 0.39
merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama
parameters:
int8_mask: true
dtype: bfloat16
name: 4kmerge-v2
---
models:
- model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
# No parameters necessary for base model
- model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4
#Emphasize the beginning of Vicuna format models
parameters:
weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113]
density: 0.61
- model: /home/alpha/Models/Raw/Mihaiii_Pallas-0.5
# Vicuna format
parameters:
weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113]
density: 0.61
- model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
parameters:
weight: [0.02, 0.081, 0.081, 0.081, 0.081, 0.081]
density: 0.59
- model: /home/alpha/Storage/Models/Raw/jondurbin_bagel-34b-v0.2
#Only the SFT in the main merge since the DPO version seems to have no long context ability at all, and some overfitting(?) issues
parameters:
weight: [0.02, 0.093, 0.093, 0.093, 0.093, 0.093]
density: 0.4
- model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat
parameters:
weight: [0.02, 0.081, 0.081, 0.081, 0.081, 0.081]
density: 0.59
#- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k
# Dolphin 200K seems to be funky according to multiple leaderboards and perplexity tests?
# parameters:
# weight: 0.15
# density: 0.6
- model: /home/alpha/Models/Raw/adamo1139_Yi-34B-200K-AEZAKMI-v2
parameters:
weight: [0.02, 0.096, 0.096, 0.096, 0.096, 0.096]
density: 0.59
- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
parameters:
weight: [0.21, 0.115, 0.115, 0.115, 0.115, 0.115]
density: 0.59
- model: 4kmerge-v2
#Previous merge
parameters:
weight: [0.02, 0.115, 0.115, 0.115, 0.115, 0.115]
density: 0.4
- model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
# Vicuna format
parameters:
weight: [0.21, 0.09, 0.09, 0.09, 0.09, 0.09]
density: 0.61
- model: /home/alpha/Models/Raw/TriadParty_deepmoney-34b-200k-base
# No prompt format, native long context full finetune
parameters:
weight: [0.04, 0.103, 0.103, 0.103, 0.103, 0.103]
density: 0.61
merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16