Instructions to use shalan/DS-R1-8B-bnb-4bit-fortunetelling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use shalan/DS-R1-8B-bnb-4bit-fortunetelling 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 shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0
# Run inference directly in the terminal:
llama cli -hf shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0
# Run inference directly in the terminal:
llama cli -hf shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0

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 shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0

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 shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0

Use Docker

docker model run hf.co/shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0

LM Studio
Jan
Ollama
How to use shalan/DS-R1-8B-bnb-4bit-fortunetelling with Ollama:
```
ollama run hf.co/shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0
```

Unsloth Studio

How to use shalan/DS-R1-8B-bnb-4bit-fortunetelling 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 shalan/DS-R1-8B-bnb-4bit-fortunetelling 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 shalan/DS-R1-8B-bnb-4bit-fortunetelling to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shalan/DS-R1-8B-bnb-4bit-fortunetelling to start chatting

Docker Model Runner
How to use shalan/DS-R1-8B-bnb-4bit-fortunetelling with Docker Model Runner:
```
docker model run hf.co/shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0
```

Lemonade

How to use shalan/DS-R1-8B-bnb-4bit-fortunetelling with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shalan/DS-R1-8B-bnb-4bit-fortunetelling:Q8_0

Run and chat with the model

lemonade run user.DS-R1-8B-bnb-4bit-fortunetelling-Q8_0

List all available models

lemonade list

Atomic Chat

DS-R1-8B-bnb-4bit-fortunetelling / unsloth.Q8_0.gguf

shalan

(Trained with Unsloth)

ca19f94 verified 12 months ago

Download

History Blame GGUF Editor new Delete

8.54 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:: fac9464e6457b0f3b52d336c1c382f18ee420e2ec7d6c3b6e07ec813b2c98d91
Size of remote file:: 8.54 GB
SHA256:: eb0c66bc65ce940a9973b82a2753d7db7a413a0a72480f899ed81189daf0c32d

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