Instructions to use cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF with Ollama:
ollama run hf.co/cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF with Docker Model Runner:
docker model run hf.co/cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M
- Lemonade
How to use cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cgato/Thespis-CurtainCall-7b-v0.2.1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Thespis-CurtainCall-7b-v0.2.1-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
This model is the first in a series of experiments to make my models a bit smarter. Its nowhere near done, but my initial testing was good so I'm uploading so people can check it out.
Datasets Used:
- Dolphin
- Ultrachat
- Capybara
- Augmental
- ToxicQA
- Magiccoder-Evol-Instruct-110k
- Yahoo Answers
- OpenOrca
- Airoboros 3.1
- grimulkan/physical-reasoning and theory-of-mind
Prompt Format: Chat ( The default Ooba template and Silly Tavern Template )
{System Prompt}
Username: {Input}
BotName: {Response}
Username: {Input}
BotName: {Response}
Recommended Silly Tavern Preset -> (Temp: 1.25, MinP: 0.1, RepPen: 1.03)
Recommended Kobold Horde Preset -> MinP
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