You are a helpful assistant for fiction writing. Always cut the bullshit and provide concise outlines with useful details. Do not turn your stories into fairy tales, be realistic.
### USER: {prompt}
### ASSISTANT:
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.
COPY /B goat-70b-storytelling.Q6_K.gguf-split-a + goat-70b-storytelling.Q6_K.gguf-split-b goat-70b-storytelling.Q6_K.gguf
del goat-70b-storytelling.Q6_K.gguf-split-a goat-70b-storytelling.Q6_K.gguf-split-b
COPY /B goat-70b-storytelling.Q8_0.gguf-split-a + goat-70b-storytelling.Q8_0.gguf-split-b goat-70b-storytelling.Q8_0.gguf
del goat-70b-storytelling.Q8_0.gguf-split-a goat-70b-storytelling.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/GOAT-70B-Storytelling-GGUF and below it, a specific filename to download, such as: goat-70b-storytelling.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:
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 32 -m goat-70b-storytelling.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful assistant for fiction writing. Always cut the bullshit and provide concise outlines with useful details. Do not turn your stories into fairy tales, be realistic.\n### USER: {prompt}\n### ASSISTANT:"
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/GOAT-70B-Storytelling-GGUF", model_file="goat-70b-storytelling.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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Original model card: GOAT.AI's Goat 70B Storytelling
GOAT-70B-Storytelling
GOAT-70B-Storytelling model
GOAT-70B-Storytelling model trained by GOAT.AI lab as a core model for an autonomous story-writing agent.
GOAT-Storytelling-Agent
This agent facilitates the generation of high-quality, cohesive, and captivating narratives, including stories and books. It achieves this by utilizing inputs such as plot outlines, character profiles, their interrelationships, and other relevant details. Examples are provided below.
Model description
Base Architecture: LLaMA 2 70B
License: llama2
Context window length: 4096 tokens
Training details
Training was performed on a GPU cluster of 64xH100s. FSDP ZeRO-3 sharding is employed for efficient training. We instruction finetune on a dataset of 18K examples for one epoch with batch size of 336, AdamW optimizer with learning rate 1e-5.
The main purpose of GOAT-70B-Storytelling is to generate books, novels, movie scripts and etc. as an agent in coping with our GOAT-Storytelling-Agent. It is specifically designed for storywriters.
Usage
Usage can be either self-hosted via transformers or used with Spaces
Currently, we support LLM endpoint generation, where you need to send a post request to the generation endpoint (we recommend using Text Generation Inference by HuggingFace)
First, modify config.py and add your generation endpoint.
Then you can use it inside via GOAT-Storytelling-Agent:
python
1from goat_storytelling_agent import storytelling_agent as goat
23novel_scenes = goat.generate_story('treasure hunt in a jungle', form='novel')
License
GOAT-70B-Storytelling model is based on Meta's LLaMA-2-70b-hf, and using own datasets.
GOAT-70B-Storytelling model weights are available under LLAMA-2 license.
Risks and Biases
GOAT-70B-Storytelling model can produce factually incorrect output and should not be relied on to deliver factually accurate information. Therefore, the GOAT-70B-Storytelling model could possibly generate wrong, biased, or otherwise offensive outputs.
