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.
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/LLaMA2-13B-Tiefighter-GGUF and below it, a specific filename to download, such as: llama2-13b-tiefighter.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/LLaMA2-13B-Tiefighter-GGUF", model_file="llama2-13b-tiefighter.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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Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: KoboldAI's Llama2 13B Tiefighter
LLaMA2-13B-Tiefighter
Tiefighter is a merged model achieved trough merging two different lora's on top of a well established existing merge.
To achieve this the following recipe was used:
We begin with the base model Undi95/Xwin-MLewd-13B-V0.2 which is a well established merged, contrary to the name this model does not have a strong NSFW bias.
Then we applied the PocketDoc/Dans-RetroRodeo-13b lora which is a finetune on the Choose your own Adventure datasets from our Skein model.
After applying this lora we merged the new model with PocketDoc/Dans-RetroRodeo-13b at 5% to weaken the newly introduced adventure bias.
The resulting merge was used as a new basemodel to which we applied Blackroot/Llama-2-13B-Storywriter-LORA and repeated the same trick, this time at 10%.
This means this model contains the following ingredients from their upstream models for as far as we can track them:
"a lot of different models, like hermes, beluga, airoboros, chronos.. limarp"
lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
Xwin-LM/Xwin-LM-13B-V0.2
PocketDoc/Dans-RetroRodeo-13b
Blackroot/Llama-2-13B-Storywriter-LORA
While we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible!
Thanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible.
Usage
This model is meant to be creative, If you let it improvise you get better results than if you drown it in details.
Story Writing
Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story.
Generate a story on demand
To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong.
Chatbots and persona's
This model has been tested with various forms of chatting, testers have found that typically less is more and the model is good at improvising. Don't drown the model in paragraphs of detailed information, instead keep it simple first and see how far you can lean on the models own ability to figure out your character. Copy pasting paragraphs of background information is not suitable for a 13B model such as this one, code formatted characters or an instruction prompt describing who you wish to talk to goes much further.
For example, you can put this in memory in regular chat mode:
### Instruction:
Generate a conversation between Alice and Henk where they discuss language models.
In this conversation Henk is excited to teach Alice about Tiefigther.
### Response:
Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format.
Instruct Prompting
This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work.
During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias.
Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up.
Adventuring and Adventure Games
This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode).
It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns.
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