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.
COPY /B opus-v0-70b.Q6_K.gguf-split-a + opus-v0-70b.Q6_K.gguf-split-b opus-v0-70b.Q6_K.gguf
del opus-v0-70b.Q6_K.gguf-split-a opus-v0-70b.Q6_K.gguf-split-b
COPY /B opus-v0-70b.Q8_0.gguf-split-a + opus-v0-70b.Q8_0.gguf-split-b opus-v0-70b.Q8_0.gguf
del opus-v0-70b.Q8_0.gguf-split-a opus-v0-70b.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/opus-v0-70B-GGUF and below it, a specific filename to download, such as: opus-v0-70b.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
Then you can download any individual model file to the current directory, at high speed, with a command like this:
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 in Python code, using ctransformers
First install the package
Run one of the following commands, according to your system:
shell
1# Base ctransformers with no GPU acceleration2pip install ctransformers
3# Or with CUDA GPU acceleration4pip install ctransformers[cuda]5# Or with AMD ROCm GPU acceleration (Linux only)6CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
7# Or with Metal GPU acceleration for macOS systems only8CT_METAL=1 pip install ctransformers --no-binary ctransformers
Simple ctransformers example code
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/opus-v0-70B-GGUF", model_file="opus-v0-70b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)56print(llm("AI is going to"))
How to use with LangChain
Here are guides on using llama-cpp-python and 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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Original model card: DreamGen's Opus v0 70B
DreamGen Opus V0 70B
DreamGen Opus is a family of uncensored models fine-tuned for (steerable) story writing and the model also works great for chat / RP.
The DreamGen Opus V0 70B model is derived from meta-llama/Llama-2-70b-hf.
You can try the Opus V0 70B (AWQ) model for free on dreamgen.com.
Please see the official documentation for more detailed guide, including how to prompt the model for chat / RP.
The (collaborative / steerable) story writing task teaches the model to respect <setting> and <instruction> inserted into the prompt.
Example prompt:
<setting>
(Setting provides general overview of the story and characters)
This story is a twist on the traditional Little Red Riding Hood story.
In this variation, the Little Red Riding Hood and her grandma are secretely werevoles.
</setting>
(Previous part of the story, potentially empty)
<instruction>
(Setting tells the model what should happen in the next few sentences / paragraphs)
The Little Red Riding hood confronts The Big Bad Wolf, transforming into her wolf form.
</instruction>
Dataset
The fine-tuning dataset consisted of >1M tokens of collaborative writing task examples, each example being up to 4096 tokens. On top of that, >20M tokens of more general, but less instructed examples were included to help preserve generalization.
Community
Join the DreamGen community on Discord, or follow our X/Twitter account for new model releases and other news.
We will soon be releasing models with longer context window, as well as models specifically fine-tuned for character chat & roleplay.
Help us shape the future of DreamGen.
Running the model
The model is should be compatible with any software that supports meta-llama/Llama-2-70b-hf.
Note that because this is a 70B model, the resource requirements are large. You can try the quantized versions linked at the top, but expect a quality drop.
Running on DreamGen.com (free)
You can try the 70B (AWQ) model for free at dreamgen.com — note that an account is required.
The version used for the website is the official AWQ 4bit quant dreamgen/opus-v0-70b-awq.
