TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
Sonya 7B - GGUF
- Model creator: Sanji Watsuki
- Original model: Sonya 7B
Description
This repo contains GGUF format model files for Sanji Watsuki's Sonya 7B.
These files were quantised using hardware kindly provided by Massed Compute.
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 incomplete 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.
- GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
- LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
- 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.
- 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.
- ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
Repositories available
- AWQ model(s) for GPU inference.
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
- Sanji Watsuki's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Alpaca
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
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 |
|---|---|---|---|---|---|
| sonya-7b.Q2_K.gguf | Q2_K | 2 | 3.08 GB | 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| sonya-7b.Q3_K_S.gguf | Q3_K_S | 3 | 3.17 GB | 5.67 GB | very small, high quality loss |
| sonya-7b.Q3_K_M.gguf | Q3_K_M | 3 | 3.52 GB | 6.02 GB | very small, high quality loss |
| sonya-7b.Q3_K_L.gguf | Q3_K_L | 3 | 3.82 GB | 6.32 GB | small, substantial quality loss |
| sonya-7b.Q4_0.gguf | Q4_0 | 4 | 4.11 GB | 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| sonya-7b.Q4_K_S.gguf | Q4_K_S | 4 | 4.14 GB | 6.64 GB | small, greater quality loss |
| sonya-7b.Q4_K_M.gguf | Q4_K_M | 4 | 4.37 GB | 6.87 GB | medium, balanced quality - recommended |
| sonya-7b.Q5_0.gguf | Q5_0 | 5 | 5.00 GB | 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| sonya-7b.Q5_K_S.gguf | Q5_K_S | 5 | 5.00 GB | 7.50 GB | large, low quality loss - recommended |
| sonya-7b.Q5_K_M.gguf | Q5_K_M | 5 | 5.13 GB | 7.63 GB | large, very low quality loss - recommended |
| sonya-7b.Q6_K.gguf | Q6_K | 6 | 5.94 GB | 8.44 GB | very large, extremely low quality loss |
| sonya-7b.Q8_0.gguf | Q8_0 | 8 | 7.70 GB | 10.20 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.
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/Sonya-7B-GGUF and below it, a specific filename to download, such as: sonya-7b.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/Sonya-7B-GGUF sonya-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
More advanced huggingface-cli download usage (click to read)
You can also download multiple files at once with a pattern:
huggingface-cli download TheBloke/Sonya-7B-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/Sonya-7B-GGUF sonya-7b.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 35 -m sonya-7b.Q4_K_M.gguf --color -c 8192 --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 8192 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
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. 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.
How to load this model in Python code, using llama-cpp-python
For full documentation, please see: llama-cpp-python docs.
First install the package
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
Simple llama-cpp-python example code
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="./sonya-7b.Q4_K_M.gguf", # Download the model file first
n_ctx=8192, # 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="./sonya-7b.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."
}
]
)
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:
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: 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.
Original model card: Sanji Watsuki's Sonya 7B
Top 1 Performer MT-bench 🤪
WTF is This?
Sonya-7B is, at the time of writing, the #1 performing model in MT-Bench first turn, ahead of GPT-4, and overall the #2 model in MT-Bench, to the best of my knowledge. Sonya-7B should be a good all-purpose model for all tasks including assistant, RP, etc.
Sonya-7B has a similar structure to my previous model, Silicon-Maid-7B, and uses a very similar merge. It's a merge of xDAN-AI/xDAN-L1-Chat-RL-v1, Jan-Ai's Stealth v1.2, chargoddard/piano-medley-7b, NeverSleep/Noromaid-7B-v0.2, and athirdpath/NSFW_DPO_vmgb-7b. Sauce is below. Somehow, by combining these pieces, it substantially outscores any of its parents on MT-Bench.
I picked these models because:
- MT-Bench normally correlates well with real world model quality and xDAN performs well on it.
- Almost all models in the mix were Alpaca prompt formatted which gives prompt consistency.
- Stealth v1.2 has been a magic sprinkle that seems to increase my MT-Bench scores.
- I added RP models because it boosted the Writing and Roleplay benchmarks 👀
Based on the parent models, I expect this model to be used with an 8192 context window. Please use NTK scaling alpha of 2.6 to experimentally try out 16384 context.
Let me be candid: Despite the test scores, this model is NOT is a GPT killer. I think it's a very sharp model for a 7B, it probably punches way above its weight for a 7B, but it's still a 7B model. Even for a 7B model, I think it's quirky and has some weird outputs, probably due to how Frankenstein this merge is. Keep your expectations in check 😉
MT-Bench Average Turn
| model | score | size |
|---|---|---|
| gpt-4 | 8.99 | - |
| Sonya-7B | 8.52 | 7b |
| xDAN-L1-Chat-RL-v1 | 8.34 | 7b |
| Starling-7B | 8.09 | 7b |
| Claude-2 | 8.06 | - |
| Silicon-Maid | 7.96 | 7b |
| Loyal-Macaroni-Maid | 7.95 | 7b |
| gpt-3.5-turbo | 7.94 | 20b? |
| Claude-1 | 7.90 | - |
| OpenChat-3.5 | 7.81 | - |
| vicuna-33b-v1.3 | 7.12 | 33b |
| wizardlm-30b | 7.01 | 30b |
| Llama-2-70b-chat | 6.86 | 70b |
The Sauce
models:
- model: xDAN-AI/xDAN-L1-Chat-RL-v1
parameters:
weight: 1
density: 1
- model: chargoddard/piano-medley-7b
parameters:
weight: 0.3
- model: jan-hq/stealth-v1.2
parameters:
weight: 0.2
- model: NeverSleep/Noromaid-7b-v0.2
parameters:
weight: 0.2
- model: athirdpath/NSFW_DPO_vmgb-7b
parameters:
weight: 0.2
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
density: 0.4
int8_mask: true
normalize: true
dtype: bfloat16
There was no additional training, finetuning, or DPO. This is a straight merger.
Prompt Template (Alpaca)
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
I found that this model performed worse with the xDAN prompt format so, despite the heavy weight of xDAN in this merger, I recommeend against its use.
Other Benchmark Stuff
########## First turn ##########
| model | turn | score | size |
|---|---|---|---|
| Sonya-7B | 1 | 9.06875 | 7b |
| gpt-4 | 1 | 8.95625 | - |
| xDAN-L1-Chat-RL-v1 | 1 | 8.87500 | 7b |
| xDAN-L2-Chat-RL-v2 | 1 | 8.78750 | 30b |
| claude-v1 | 1 | 8.15000 | - |
| gpt-3.5-turbo | 1 | 8.07500 | 20b |
| vicuna-33b-v1.3 | 1 | 7.45625 | 33b |
| wizardlm-30b | 1 | 7.13125 | 30b |
| oasst-sft-7-llama-30b | 1 | 7.10625 | 30b |
| Llama-2-70b-chat | 1 | 6.98750 | 70b |
########## Second turn ##########
| model | turn | score | size |
|---|---|---|---|
| gpt-4 | 2 | 9.025000 | - |
| xDAN-L2-Chat-RL-v2 | 2 | 8.087500 | 30b |
| Sonya-7B | 2 | 7.962500 | 7b |
| xDAN-L1-Chat-RL-v1 | 2 | 7.825000 | 7b |
| gpt-3.5-turbo | 2 | 7.812500 | 20b |
| claude-v1 | 2 | 7.650000 | - |
| wizardlm-30b | 2 | 6.887500 | 30b |
| vicuna-33b-v1.3 | 2 | 6.787500 | 33b |
| Llama-2-70b-chat | 2 | 6.725000 | 70b |
If you'd like to replicate the MT-Bench run, please ensure that the Alpaca prompt template is applied to the model. I did this by putting "alpaca" in the model path to trigger the AlpacaAdapter.
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Model tree for TheBloke/Sonya-7B-GGUF
Base model
SanjiWatsuki/Sonya-7B