Instructions to use BramVanroy/fietje-2-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use BramVanroy/fietje-2-instruct-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 BramVanroy/fietje-2-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BramVanroy/fietje-2-instruct-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 BramVanroy/fietje-2-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BramVanroy/fietje-2-instruct-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 BramVanroy/fietje-2-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BramVanroy/fietje-2-instruct-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 BramVanroy/fietje-2-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BramVanroy/fietje-2-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/BramVanroy/fietje-2-instruct-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use BramVanroy/fietje-2-instruct-gguf with Ollama:
```
ollama run hf.co/BramVanroy/fietje-2-instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use BramVanroy/fietje-2-instruct-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 BramVanroy/fietje-2-instruct-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 BramVanroy/fietje-2-instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BramVanroy/fietje-2-instruct-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use BramVanroy/fietje-2-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/BramVanroy/fietje-2-instruct-gguf:Q4_K_M
```

Lemonade

How to use BramVanroy/fietje-2-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BramVanroy/fietje-2-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.fietje-2-instruct-gguf-Q4_K_M

List all available models

lemonade list

This repository contains quantized versions of BramVanroy/fietje-2-instruct.

Available quantization types and expected performance differences compared to base f16, higher perplexity=worse (from llama.cpp):

Q3_K_M  :  3.07G, +0.2496 ppl @ LLaMA-v1-7B
Q4_K_M  :  3.80G, +0.0532 ppl @ LLaMA-v1-7B
Q5_K_M  :  4.45G, +0.0122 ppl @ LLaMA-v1-7B
Q6_K    :  5.15G, +0.0008 ppl @ LLaMA-v1-7B
Q8_0    :  6.70G, +0.0004 ppl @ LLaMA-v1-7B
F16     : 13.00G              @ 7B

Also available on ollama.

Quants were made with release b2777 of llama.cpp. ```

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GGUF

Model size

3B params

Architecture

phi2

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Collection including BramVanroy/fietje-2-instruct-gguf

Fietje 2

Collection

An open and efficient LLM for Dutch based on phi-2 • 7 items • Updated May 28