Instructions to use RikudouSage/Chronos-Mistral-7B-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 RikudouSage/Chronos-Mistral-7B-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 RikudouSage/Chronos-Mistral-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf RikudouSage/Chronos-Mistral-7B-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 RikudouSage/Chronos-Mistral-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf RikudouSage/Chronos-Mistral-7B-GGUF: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 RikudouSage/Chronos-Mistral-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RikudouSage/Chronos-Mistral-7B-GGUF: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 RikudouSage/Chronos-Mistral-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RikudouSage/Chronos-Mistral-7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/RikudouSage/Chronos-Mistral-7B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RikudouSage/Chronos-Mistral-7B-GGUF with Ollama:
ollama run hf.co/RikudouSage/Chronos-Mistral-7B-GGUF:Q4_K_M
- Unsloth Studio
How to use RikudouSage/Chronos-Mistral-7B-GGUF 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 RikudouSage/Chronos-Mistral-7B-GGUF 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 RikudouSage/Chronos-Mistral-7B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RikudouSage/Chronos-Mistral-7B-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use RikudouSage/Chronos-Mistral-7B-GGUF with Docker Model Runner:
docker model run hf.co/RikudouSage/Chronos-Mistral-7B-GGUF:Q4_K_M
- Lemonade
How to use RikudouSage/Chronos-Mistral-7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RikudouSage/Chronos-Mistral-7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Chronos-Mistral-7B-GGUF-Q4_K_M
List all available models
lemonade list
Description
This repo contains GGUF files for the original model.
Files
- Chronos-Mistral-7b_Q2_K.gguf (2.72 GB) - smallest, significant quality loss - not recommended for most purposes
- Chronos-Mistral-7b_Q3_K_S.gguf (3.16 GB) - very small, high quality loss
- Chronos-Mistral-7b_Q3_K_M.gguf (3.52 GB) - very small, high quality loss
- Chronos-Mistral-7b_Q3_K_L.gguf (3.82 GB) - small, substantial quality loss
- Chronos-Mistral-7b_Q4_K_S.gguf (4.14 GB) - small, greater quality loss
- Chronos-Mistral-7b_Q4_K_M.gguf (4.37 GB) - medium, balanced quality - recommended
- Chronos-Mistral-7b_Q5_K_S.gguf (5 GB) - large, low quality loss - recommended
- Chronos-Mistral-7b_Q5_K_M.gguf (5.13 GB) - large, very low quality loss - recommended
- Chronos-Mistral-7b_Q6_K.gguf (5.94 GB) - very large, extremely low quality loss
- Chronos-Mistral-7b_Q8_0.gguf (7.7 GB) - very large, extremely low quality loss - not recommended
Original model description
This model is primarily focused on chat, roleplay, storywriting, with good reasoning and logic.
Chronos can generate very long outputs with coherent text, largely due to the human inputs it was trained on, and it supports context length up to 4096 tokens
Up to 16384 with RoPE with solid coherency.
This model uses Alpaca formatting, so for optimal model performance, use it to start the dialogue or story, and if you use a frontend like SillyTavern ENABLE instruction mode:
### Instruction:
{Your instruction or question here.}
### Response:
Not using the format will make the model perform significantly worse than intended unless it is merged.
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Model tree for RikudouSage/Chronos-Mistral-7B-GGUF
Base model
elinas/chronos-mistral-7b