Instructions to use R-uslan/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use R-uslan/model with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("R-uslan/model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use R-uslan/model 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 R-uslan/model:F16
# Run inference directly in the terminal:
llama cli -hf R-uslan/model:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf R-uslan/model:F16
# Run inference directly in the terminal:
llama cli -hf R-uslan/model:F16

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 R-uslan/model:F16
# Run inference directly in the terminal:
./llama-cli -hf R-uslan/model:F16

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 R-uslan/model:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf R-uslan/model:F16

Use Docker

docker model run hf.co/R-uslan/model:F16

LM Studio
Jan
Ollama
How to use R-uslan/model with Ollama:
```
ollama run hf.co/R-uslan/model:F16
```

Unsloth Studio

How to use R-uslan/model 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 R-uslan/model 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 R-uslan/model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for R-uslan/model to start chatting

Docker Model Runner
How to use R-uslan/model with Docker Model Runner:
```
docker model run hf.co/R-uslan/model:F16
```

Lemonade

How to use R-uslan/model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull R-uslan/model:F16

Run and chat with the model

lemonade run user.model-F16

List all available models

lemonade list

Atomic Chat

model / unsloth.F16.gguf

R-uslan

(Trained with Unsloth)

71f604a verified almost 2 years ago

Download

History Blame GGUF Editor new Delete

16.1 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:: 1923d73b0d07f1e0143a08baf0fdd0f491221bdde72de5138d8cad27b654e83c
Size of remote file:: 16.1 GB
SHA256:: 5aaf583b4811e209fa6c0988abfe0e23500562d61d7d0c17a9dfb5296e8ef706

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