Instructions to use Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs", filename="CREC-n-WREC-Mate-24B-v2-bf16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M
Use Docker
docker model run hf.co/Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M
- Ollama
How to use Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs with Ollama:
ollama run hf.co/Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M
- Unsloth Studio
How to use Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs to start chatting
- Docker Model Runner
How to use Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs with Docker Model Runner:
docker model run hf.co/Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M
- Lemonade
How to use Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs:Q4_K_M
Run and chat with the model
lemonade run user.CREC-n-WREC-Mate-24B-v2-GGUFs-Q4_K_M
List all available models
lemonade list
CREC-n-WREC-Mate-24B-v2
THIS MODEL IS UNOFFICIAL!
This model has no official affiliation with Weather and his SillyTavern Extensions. This is simply a fan project to help fellow users of these extensions.
Merge Description
CREC-n-WREC-Mate is a model made to help create World Info entries mid-roleplay using the SillyTavern extensions CREC and WREC.
The responses a bit on the shorter side by default, but this should be all the more beneficial for creating World Info entries. Needless to say, this isn't a model designed for creating Char Cards, instead it's meant for saving characters you encounter on your adventures to a Lorebook, so make sure to enable the feature that allows adding characters to a WI entry in the CREC settings menu.
WREC Setup: here
CREC Setup: here
Merge Details
This is a merge of pre-trained language models created using mergekit.
Merge Method
This model was merged using the Conflict-Aware N:M Sparsification merge method using TheDrummer/Cydonia-24B-v2.1 as a base.
Models Merged
The following models were included in the merge:
- CharGen-Archive/CharGen-v3-beta-275-s0
- SlerpE/CardProjector-24B-v3
- Mawdistical/Mawdistic-NightLife-24b
- ReadyArt/Broken-Tutu-24B
Configuration
The following YAML configurations were used to produce this model:
C-n-W-CharGen_v2
models:
- model: CharGen-Archive/CharGen-v3-beta-275-s0
- model: Mawdistical/Mawdistic-NightLife-24b
parameters:
weight: 0.3
n_val: 64
m_val: 128
- model: SlerpE/CardProjector-24B-v3
parameters:
weight: 0.2
n_val: 16
m_val: 32
merge_method: cabs
pruning_order:
- Mawdistical/Mawdistic-NightLife-24b
- SlerpE/CardProjector-24B-v3
base_model: CharGen-Archive/CharGen-v3-beta-275-s0
dtype: float32
tokenizer:
source: union
tokens:
</s>:
source:
kind: model_token
model: CharGen-Archive/CharGen-v3-beta-275-s0
token: "<|im_end|>"
"[INST]":
source:
kind: model_token
model: CharGen-Archive/CharGen-v3-beta-275-s0
token: "<|im_start|>"
C-n-W-CardProj_v2
models:
- model: SlerpE/CardProjector-24B-v3
- model: ReadyArt/Broken-Tutu-24B
parameters:
weight: 0.3
n_val: 64
m_val: 128
- model: CharGen-Archive/CharGen-v3-beta-275-s0
parameters:
weight: 0.2
n_val: 16
m_val: 32
merge_method: cabs
pruning_order:
- ReadyArt/Broken-Tutu-24B
- CharGen-Archive/CharGen-v3-beta-275-s0
base_model: SlerpE/CardProjector-24B-v3
dtype: float32
tokenizer:
source: union
tokens:
"[/INST]":
source:
kind: model_token
model: CharGen-Archive/CharGen-v3-beta-275-s0
token: "<|im_end|>"
source:
kind: model_token
model: CharGen-Archive/CharGen-v3-beta-275-s0
token: "<|im_start|>"
CREC-n-WREC-Mate-24B-v2
models:
- model: TheDrummer/Cydonia-24B-v2.1
- model: C-n-W-CardProj_v2
parameters:
weight: 0.6
n_val: 64
m_val: 128
- model: C-n-W-CharGen_v2
parameters:
weight: 0.4
n_val: 12
m_val: 32
merge_method: cabs
pruning_order:
- C-n-W-CardProj_v2
- C-n-W-CharGen_v2
base_model: TheDrummer/Cydonia-24B-v2.1
dtype: float32
out_dtype: bfloat16
tokenizer:
source: union
tokens:
"[/INST]":
source:
kind: model_token
model: C-n-W-CardProj_v2
token: "[/INST]"
source:
kind: model_token
model: C-n-W-CharGen_v2
token: "[/INST]"
"[INST]":
source:
kind: model_token
model: C-n-W-CardProj_v2
token: "[INST]"
source:
kind: model_token
model: C-n-W-CharGen_v2
token: "[INST]"
</s>:
source:
kind: model_token
model: C-n-W-CardProj_v2
token: "</s>"
source:
kind: model_token
model: C-n-W-CharGen_v2
token: "</s>"
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Model tree for Casual-Autopsy/CREC-n-WREC-Mate-24B-v2-GGUFs
Base model
Casual-Autopsy/CREC-n-WREC-Mate-24B-v2