Instructions to use mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/CataLlama-v0.2-Instruct-SFT-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 mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/CataLlama-v0.2-Instruct-SFT-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 mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/CataLlama-v0.2-Instruct-SFT-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 mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/CataLlama-v0.2-Instruct-SFT-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 mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF with Ollama:
```
ollama run hf.co/mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/CataLlama-v0.2-Instruct-SFT-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 mradermacher/CataLlama-v0.2-Instruct-SFT-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 mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF to start chatting

Docker Model Runner
How to use mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/CataLlama-v0.2-Instruct-SFT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CataLlama-v0.2-Instruct-SFT-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

CataLlama-v0.2-Instruct-SFT-GGUF / CataLlama-v0.2-Instruct-SFT.Q3_K_S.gguf

mradermacher

uploaded from nethype/back

7debcb4 verified about 2 years ago

Download

History Blame

3.66 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 0b38e92dbfc3daf8211c07c5b3bd417c70af4a1a8886d074c731e8cd4cf0037b
Size of remote file:: 3.66 GB
SHA256:: 9110fa4d3a75a65252c4e07552828bbb47baca9165b5064b17fd66a878b43ee5

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.