Instructions to use rkstgr/typer-1.5b-instruct-concise-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rkstgr/typer-1.5b-instruct-concise-gguf with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rkstgr/typer-1.5b-instruct-concise-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rkstgr/typer-1.5b-instruct-concise-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 rkstgr/typer-1.5b-instruct-concise-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rkstgr/typer-1.5b-instruct-concise-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 rkstgr/typer-1.5b-instruct-concise-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rkstgr/typer-1.5b-instruct-concise-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 rkstgr/typer-1.5b-instruct-concise-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rkstgr/typer-1.5b-instruct-concise-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 rkstgr/typer-1.5b-instruct-concise-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rkstgr/typer-1.5b-instruct-concise-gguf:Q4_K_M

Use Docker

docker model run hf.co/rkstgr/typer-1.5b-instruct-concise-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use rkstgr/typer-1.5b-instruct-concise-gguf with Ollama:
```
ollama run hf.co/rkstgr/typer-1.5b-instruct-concise-gguf:Q4_K_M
```

Unsloth Studio

How to use rkstgr/typer-1.5b-instruct-concise-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 rkstgr/typer-1.5b-instruct-concise-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 rkstgr/typer-1.5b-instruct-concise-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rkstgr/typer-1.5b-instruct-concise-gguf to start chatting

Docker Model Runner
How to use rkstgr/typer-1.5b-instruct-concise-gguf with Docker Model Runner:
```
docker model run hf.co/rkstgr/typer-1.5b-instruct-concise-gguf:Q4_K_M
```

Lemonade

How to use rkstgr/typer-1.5b-instruct-concise-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rkstgr/typer-1.5b-instruct-concise-gguf:Q4_K_M

Run and chat with the model

lemonade run user.typer-1.5b-instruct-concise-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

rkstgr/typer-1.5b-instruct-concise-gguf

This repository contains GGUF quantized versions of rkstgr/typer-1.5b-instruct-concise.

Usage with Ollama

ollama run hf.co/rkstgr/typer-1.5b-instruct-concise-gguf

Usage with llama.cpp

# Download the model
wget https://huggingface.co/rkstgr/typer-1.5b-instruct-concise-gguf/resolve/main/{model_file}.gguf

# Run with llama.cpp
./llama-cli -m {model_file}.gguf -p "Your prompt here"

Model Details

Base Model: rkstgr/typer-1.5b-instruct-concise
Training: Fine-tuned using Unsloth

Acknowledgements

Computational resources were provided by the German AI Service Center WestAI.

Downloads last month: 29

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

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