GGUF
Merge
mergekit
MaziyarPanahi/Calme-7B-Instruct-v0.1.1
mistralai/Mistral-7B-v0.1+predibase/customer_support
conversational
Instructions to use QuantFactory/CS-Calme-Instruct-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 QuantFactory/CS-Calme-Instruct-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 QuantFactory/CS-Calme-Instruct-7b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/CS-Calme-Instruct-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 QuantFactory/CS-Calme-Instruct-7b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/CS-Calme-Instruct-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 QuantFactory/CS-Calme-Instruct-7b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/CS-Calme-Instruct-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 QuantFactory/CS-Calme-Instruct-7b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/CS-Calme-Instruct-7b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/CS-Calme-Instruct-7b-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/CS-Calme-Instruct-7b-GGUF with Ollama:
ollama run hf.co/QuantFactory/CS-Calme-Instruct-7b-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/CS-Calme-Instruct-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 QuantFactory/CS-Calme-Instruct-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 QuantFactory/CS-Calme-Instruct-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 QuantFactory/CS-Calme-Instruct-7b-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/CS-Calme-Instruct-7b-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/CS-Calme-Instruct-7b-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/CS-Calme-Instruct-7b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/CS-Calme-Instruct-7b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.CS-Calme-Instruct-7b-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- merge
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- mergekit
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- MaziyarPanahi/Calme-7B-Instruct-v0.1.1
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- mistralai/Mistral-7B-v0.1+predibase/customer_support
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---
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# QuantFactory/CS-Calme-Instruct-7b-GGUF
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This is quantized version of [arcee-ai/CS-Calme-Instruct-7b](https://huggingface.co/arcee-ai/CS-Calme-Instruct-7b) created using llama.cpp
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# Original Model Card
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# CS-Calme-Instruct-7b
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CS-Calme-Instruct-7b is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
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* [MaziyarPanahi/Calme-7B-Instruct-v0.1.1](https://huggingface.co/MaziyarPanahi/Calme-7B-Instruct-v0.1.1)
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* [mistralai/Mistral-7B-v0.1+predibase/customer_support](https://huggingface.co/mistralai/Mistral-7B-v0.1+predibase/customer_support)
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## 🧩 Configuration
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```yaml
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slices:
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- sources:
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- model: MaziyarPanahi/Calme-7B-Instruct-v0.1.1
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layer_range: [0, 32]
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- model: mistralai/Mistral-7B-v0.1+predibase/customer_support
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layer_range: [0, 32]
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merge_method: slerp
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base_model: MaziyarPanahi/Calme-7B-Instruct-v0.1.1
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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dtype: bfloat16
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```
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