Instructions to use mradermacher/atom-80b-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/atom-80b-i1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="mradermacher/atom-80b-i1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/atom-80b-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/atom-80b-i1-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 mradermacher/atom-80b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/atom-80b-i1-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 mradermacher/atom-80b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/atom-80b-i1-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 mradermacher/atom-80b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/atom-80b-i1-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 mradermacher/atom-80b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/atom-80b-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/atom-80b-i1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use mradermacher/atom-80b-i1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mradermacher/atom-80b-i1-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": "mradermacher/atom-80b-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mradermacher/atom-80b-i1-GGUF:Q4_K_M

SGLang

How to use mradermacher/atom-80b-i1-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 "mradermacher/atom-80b-i1-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": "mradermacher/atom-80b-i1-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 "mradermacher/atom-80b-i1-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": "mradermacher/atom-80b-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use mradermacher/atom-80b-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/atom-80b-i1-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/atom-80b-i1-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 mradermacher/atom-80b-i1-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 mradermacher/atom-80b-i1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/atom-80b-i1-GGUF to start chatting

How to use mradermacher/atom-80b-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/atom-80b-i1-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mradermacher/atom-80b-i1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mradermacher/atom-80b-i1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/atom-80b-i1-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mradermacher/atom-80b-i1-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use mradermacher/atom-80b-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/atom-80b-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/atom-80b-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/atom-80b-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.atom-80b-i1-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mradermacher/atom-80b-i1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/atom-80b-i1-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mradermacher/atom-80b-i1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

atom-80b-i1-GGUF

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---
base_model: vanta-research/atom-80b
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- qwen3
- qwen3-next
- qwen
- vanta-research
- cognitive-configuration
- text-generation
- instruction-following
- cognitive-ai
- large-language-model
- helpful-ai
- aligned-ai
- philosophical
- emotional-intelligence
- atom
- collaborative-ai
- collaboration
- conversational
- conversational-ai
- alignment-ai
- chat
- chatbot
- reasoning
- friendly
- ai-research
- ai-alignment-research
- ai-alignment
- ai-behavior-research
- ai-collaboration-research
---
## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags: nicoboss -->
<!-- ### quants:  Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip:  -->
<!-- ### skip_mmproj:  -->
weighted/imatrix quants of https://huggingface.co/vanta-research/atom-80b

<!-- provided-files -->

***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#atom-80b-i1-GGUF).***

static quants are available at https://huggingface.co/mradermacher/atom-80b-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.

## Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.imatrix.gguf) | imatrix | 0.6 | imatrix file (for creating your own quants) |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ1_S.gguf) | i1-IQ1_S | 16.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ1_M.gguf) | i1-IQ1_M | 18.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 21.1 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 23.5 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ2_S.gguf) | i1-IQ2_S | 23.7 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ2_M.gguf) | i1-IQ2_M | 26.2 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q2_K_S.gguf) | i1-Q2_K_S | 27.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q2_K.gguf) | i1-Q2_K | 29.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 30.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 32.8 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 34.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ3_S.gguf) | i1-IQ3_S | 34.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ3_M.gguf) | i1-IQ3_M | 35.1 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 38.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 41.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 42.7 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q4_0.gguf) | i1-Q4_0 | 45.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 45.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 48.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q4_1.gguf) | i1-Q4_1 | 50.1 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 55.1 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 56.8 |  |
| [GGUF](https://huggingface.co/mradermacher/atom-80b-i1-GGUF/resolve/main/atom-80b.i1-Q6_K.gguf) | i1-Q6_K | 65.6 | practically like static Q6_K |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

## FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

## Thanks

I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

<!-- end -->