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. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
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.
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
Licensing
The creator of the source model has listed its license as cc-by-nc-4.0, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: Sao10K's Euryale Inverted L2 70B.
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.
COPY /B euryale-inverted-l2-70b.Q6_K.gguf-split-a + euryale-inverted-l2-70b.Q6_K.gguf-split-b euryale-inverted-l2-70b.Q6_K.gguf
del euryale-inverted-l2-70b.Q6_K.gguf-split-a euryale-inverted-l2-70b.Q6_K.gguf-split-b
COPY /B euryale-inverted-l2-70b.Q8_0.gguf-split-a + euryale-inverted-l2-70b.Q8_0.gguf-split-b euryale-inverted-l2-70b.Q8_0.gguf
del euryale-inverted-l2-70b.Q8_0.gguf-split-a euryale-inverted-l2-70b.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/Euryale-Inverted-L2-70B-GGUF and below it, a specific filename to download, such as: euryale-inverted-l2-70b.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>=0.17.1
Then you can download any individual model file to the current directory, at high speed, with a command like this:
./main -ngl 32 -m euryale-inverted-l2-70b.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.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
How to load this model from Python using ctransformers
First install the package
bash
1# Base ctransformers with no GPU acceleration2pip install ctransformers>=0.2.24
3# Or with CUDA GPU acceleration4pip install ctransformers[cuda]>=0.2.24
5# Or with ROCm GPU acceleration6CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
7# Or with Metal GPU acceleration for macOS systems8CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
Simple example code to load one of these GGUF models
python
1from ctransformers import AutoModelForCausalLM
23# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.4llm = AutoModelForCausalLM.from_pretrained("TheBloke/Euryale-Inverted-L2-70B-GGUF", model_file="euryale-inverted-l2-70b.q4_K_M.gguf", model_type="llama", gpu_layers=50)56print(llm("AI is going to"))
How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
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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.
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Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Sao10K's Euryale Inverted L2 70B
An experimental merging of Several Models using 3 merging methods. Ties-Merge, BlockMerge_Gradient [& SLERP Variant] as well as SLERP.
5 Models included Inside, 2 LORAs.
Early release because I'll be busy for the next month. Incomplete but workable, see below.
Same concept as Stheno & Stheno Inverted, at different densities, weights and gradients.
Illustration for final gradient merge cannot be shown, each of the tensors had a different ratio applied to it.
Test Checklist:
Censorship - NSFL no Issues
Writing - Good Prose and Writing Quality
NSFW - Yes
IQ Level - Slightly dumber than Euryale. Even worse at Coding / Math though. Good for RP. As a general assistant? She's not the most accurate.
Formatting - Markdown Formatting Issues, Able to Follow Statuses well.
Most formats could work, but my tests have all been done in Alpaca format and it works well.
### Instruction:
Your instruction or question here.
For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.
### Response:
My 7th Attempt. Incomplete so far, early release.
Timeline Goals:
Apply COT to model.
Apply the RP LoRA I'm working on to model.
Further tinker and test for potential better combinations.
Once Again, thanks to Chargoddard and Gryphe for their scripts, and @Vali for modifying some of the scripts provided to implement SLERP on a Cloud GPU Service.
Thanks to @gradientputri for partially sponsoring half of the compute costs for my Runpod usage.
Thanks to the original model creators too!
Art by wada_kazu / わだかず (pixiv page private?)
LICENSE
License
This model is strictly non-commercial (cc-by-nc-4.0) use only which takes priority over the LLAMA 2 COMMUNITY LICENSE AGREEMENT.
The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-nc-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences.
Non-Commercial due to parent models having the above license.
