Instructions to use ThreadAbort/h2o-danube3-500m-chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThreadAbort/h2o-danube3-500m-chat-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ThreadAbort/h2o-danube3-500m-chat-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ThreadAbort/h2o-danube3-500m-chat-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ThreadAbort/h2o-danube3-500m-chat-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 ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ThreadAbort/h2o-danube3-500m-chat-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 ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ThreadAbort/h2o-danube3-500m-chat-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 ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ThreadAbort/h2o-danube3-500m-chat-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 ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ThreadAbort/h2o-danube3-500m-chat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThreadAbort/h2o-danube3-500m-chat-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": "ThreadAbort/h2o-danube3-500m-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M

SGLang

How to use ThreadAbort/h2o-danube3-500m-chat-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ThreadAbort/h2o-danube3-500m-chat-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThreadAbort/h2o-danube3-500m-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ThreadAbort/h2o-danube3-500m-chat-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThreadAbort/h2o-danube3-500m-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ThreadAbort/h2o-danube3-500m-chat-GGUF with Ollama:
```
ollama run hf.co/ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M
```

Unsloth Studio

How to use ThreadAbort/h2o-danube3-500m-chat-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 ThreadAbort/h2o-danube3-500m-chat-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 ThreadAbort/h2o-danube3-500m-chat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ThreadAbort/h2o-danube3-500m-chat-GGUF to start chatting

Docker Model Runner
How to use ThreadAbort/h2o-danube3-500m-chat-GGUF with Docker Model Runner:
```
docker model run hf.co/ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M
```

Lemonade

How to use ThreadAbort/h2o-danube3-500m-chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ThreadAbort/h2o-danube3-500m-chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.h2o-danube3-500m-chat-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

h2o-danube3-500m-chat-GGUF

Model creator: H2O.ai
Original model: h2oai/h2o-danube3-500m-chat

Description

This repo contains GGUF format model files for h2o-danube3-500m-chat quantized using llama.cpp framework.

Table below summarizes different quantized versions of h2o-danube3-500m-chat. It shows the trade-off between size, speed and quality of the models.

Name	Quant method	Model size	MT-Bench AVG	Perplexity	Tokens per second
h2o-danube3-500m-chat-F16.gguf	F16	1.03 GB	3.34	9.46	1870
h2o-danube3-500m-chat-Q8_0.gguf	Q8_0	0.55 GB	3.76	9.46	2144
h2o-danube3-500m-chat-Q6_K.gguf	Q6_K	0.42 GB	3.77	9.46	2418
h2o-danube3-500m-chat-Q5_K_M.gguf	Q5_K_M	0.37 GB	3.20	9.55	2430
h2o-danube3-500m-chat-Q4_K_M.gguf	Q4_K_M	0.32 GB	3.16	9.96	2427

Columns in the table are:

Name -- model name and link
Quant method -- quantization method
Model size -- size of the model in gigabytes
MT-Bench AVG -- MT-Bench benchmark score. The score is from 1 to 10, the higher, the better
Perplexity -- perplexity metric on WikiText-2 dataset. It's reported in a perplexity test from llama.cpp. The lower, the better
Tokens per second -- generation speed in tokens per second, as reported in a perplexity test from llama.cpp. The higher, the better. Speed tests are done on a single H100 GPU

Prompt template

<|prompt|>Why is drinking water so healthy?</s><|answer|>

Downloads last month: 50

GGUF

Model size

0.5B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Paper for ThreadAbort/h2o-danube3-500m-chat-GGUF

Judging LLM-as-a-judge with MT-Bench and Chatbot Arena

Paper • 2306.05685 • Published Jun 9, 2023 • 43