Instructions to use QuantFactory/CS-Calme-Instruct-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/CS-Calme-Instruct-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/CS-Calme-Instruct-7b-GGUF",
	filename="CS-Calme-Instruct-7b.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/CS-Calme-Instruct-7b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

QuantFactory/CS-Calme-Instruct-7b-GGUF

This is quantized version of arcee-ai/CS-Calme-Instruct-7b created using llama.cpp

Original Model Card

CS-Calme-Instruct-7b

CS-Calme-Instruct-7b is a merge of the following models using mergekit:

🧩 Configuration

  slices:
    - sources:
        - model: MaziyarPanahi/Calme-7B-Instruct-v0.1.1
          layer_range: [0, 32]
        - model: mistralai/Mistral-7B-v0.1+predibase/customer_support
          layer_range: [0, 32]
  merge_method: slerp
  base_model: MaziyarPanahi/Calme-7B-Instruct-v0.1.1
  parameters:
    t:
      - filter: self_attn
        value: [0, 0.5, 0.3, 0.7, 1]
      - filter: mlp
        value: [1, 0.5, 0.7, 0.3, 0]
      - value: 0.5
  dtype: bfloat16

Downloads last month: 48

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support