Instructions to use adamo1139/Danube3-4b-4chan-HESOYAM-2510-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 adamo1139/Danube3-4b-4chan-HESOYAM-2510-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 adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf adamo1139/Danube3-4b-4chan-HESOYAM-2510-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 adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf adamo1139/Danube3-4b-4chan-HESOYAM-2510-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 adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf adamo1139/Danube3-4b-4chan-HESOYAM-2510-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 adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M

Use Docker

docker model run hf.co/adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M

Ollama
How to use adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF with Ollama:
```
ollama run hf.co/adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M
```

Unsloth Studio

How to use adamo1139/Danube3-4b-4chan-HESOYAM-2510-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 adamo1139/Danube3-4b-4chan-HESOYAM-2510-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 adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF to start chatting

Docker Model Runner
How to use adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF with Docker Model Runner:
```
docker model run hf.co/adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M
```

Lemonade

How to use adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Danube3-4b-4chan-HESOYAM-2510-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

GGUF Quants

Quants for adamo1139/danube3-4b-4chan-hesoyam-2510 are available in this repo

Model Details

I finetuned Danube3 4B Base on adamo1139/uninstruct-v1-experimental-chatml dataset with the goal being making AI assistant slop less likely.

Then I did finetuning on adamo1139/4chan_archive_ShareGPT_only5 which is a filtered collection of 4chan threads from various boards for 1 epoch to introduce 4chan-specific slang.

Then I did finetuning on adamo1139/HESOYAM_v0.4 for 3 epochs to improve 1-on-1 chat capabilities.

This is a resulting model.

Prompt format

Use ChatML prompt format.

System message should be in the format as below:

A chat on 4chan board /3/
A chat on 4chan board /g/
A chat on 4chan board /x/
A chat on 4chan board /pol/

Evaluation

I am still vibe-checking the model but initial results are good. I might have put in a bit too much reddit style from HESOYAM, not sure.

Downloads last month: 62

GGUF

Model size

4B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF

Base model

h2oai/h2o-danube3-4b-base

Quantized

(6)

this model

adamo1139
/

Danube3-4b-4chan-HESOYAM-2510-GGUF

GGUF Quants

Model Details

Prompt format

Evaluation

Model tree for adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF

Datasets used to train adamo1139/Danube3-4b-4chan-HESOYAM-2510-GGUF