Instructions to use AtomicChat/Hy3-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 AtomicChat/Hy3-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 AtomicChat/Hy3-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Hy3-GGUF:IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/Hy3-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Hy3-GGUF:IQ1_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 AtomicChat/Hy3-GGUF:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/Hy3-GGUF:IQ1_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 AtomicChat/Hy3-GGUF:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/Hy3-GGUF:IQ1_M

Use Docker

docker model run hf.co/AtomicChat/Hy3-GGUF:IQ1_M

LM Studio
Jan

vLLM

How to use AtomicChat/Hy3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/Hy3-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": "AtomicChat/Hy3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/Hy3-GGUF:IQ1_M

Ollama
How to use AtomicChat/Hy3-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/Hy3-GGUF:IQ1_M
```

Unsloth Studio

How to use AtomicChat/Hy3-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 AtomicChat/Hy3-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 AtomicChat/Hy3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AtomicChat/Hy3-GGUF to start chatting

How to use AtomicChat/Hy3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Hy3-GGUF:IQ1_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": "AtomicChat/Hy3-GGUF:IQ1_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AtomicChat/Hy3-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 AtomicChat/Hy3-GGUF:IQ1_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 AtomicChat/Hy3-GGUF:IQ1_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AtomicChat/Hy3-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Hy3-GGUF:IQ1_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 "AtomicChat/Hy3-GGUF:IQ1_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 AtomicChat/Hy3-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/Hy3-GGUF:IQ1_M
```

Lemonade

How to use AtomicChat/Hy3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/Hy3-GGUF:IQ1_M

Run and chat with the model

lemonade run user.Hy3-GGUF-IQ1_M

List all available models

lemonade list

Hy3-GGUF / README.md

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	---
	license: apache-2.0
	license_link: https://huggingface.co/tencent/Hy3/blob/main/LICENSE
	thumbnail: https://huggingface.co/AtomicChat/Hy3-GGUF/resolve/main/hero.png
	base_model:
	- tencent/Hy3
	base_model_relation: quantized
	quantized_by: AtomicChat
	pipeline_tag: text-generation
	library_name: gguf
	tags:
	- atomic-chat
	- hy3
	- tencent
	- gguf
	- llama.cpp
	- imatrix
	- quantized
	---

	<center>

	<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">
	<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Hy3-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>
	<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Hy3-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>
	<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Hy3-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>
	</div>

	<br/>

	<img src="https://huggingface.co/AtomicChat/Hy3-GGUF/resolve/main/hero.png" alt="Hy3" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

	<div style="display:flex; justify-content:center; gap:0.5em;">
	<a href="https://huggingface.co/tencent/Hy3"><strong>Base model: tencent/Hy3</strong></a>
	</div>
	</center>

	Hy3, self-quantized to GGUF by [Atomic Chat](https://atomic.chat). Built straight from Tencent's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

	## Highlights

	- 298.8B parameters: the weights this repo quantizes.
	- Context length: 262,144 tokens (256K), as published by Tencent.
	- 80 layers: Mixture-of-Experts.
	- Full imatrix ladder: every quant is calibrated with an importance matrix, published here alongside the quants.

	> [!NOTE]
	> These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

	> [!IMPORTANT]
	> Always pass `--jinja` so the Hy3 chat template is applied. Without it the model can emit malformed turns.

	## Model Overview

	\| Property \| Value \|
	\|---\|---\|
	\| Base model \| `tencent/Hy3` \|
	\| Parameters \| 298.8B \|
	\| Layers \| 80 \|
	\| Experts \| 192 routed (top-8) \|
	\| Context length \| 262,144 tokens (256K) \|
	\| Vocabulary \| 120,832 \|
	\| Modalities \| Text \|
	\| Architecture \| Mixture-of-Experts, 192 experts (top-8), 64 attention heads over 8 KV heads, `HYV3ForCausalLM` \|
	\| This repo \| GGUF quants (imatrix); the importance matrix is published here as `imatrix-atomic.gguf`. Quants: `IQ1_M`, `Q4_K_M` \|

	<img src="https://huggingface.co/AtomicChat/Hy3-GGUF/resolve/main/benchmark.png" alt="Hy3 benchmark scores" style="width:100%; max-width:900px;"/>

	Scores are Tencent's published results for the base `tencent/Hy3`, not our own measurements. Quantization preserves the large majority of this; `Q4_K_M` and up stay close to full precision.

	## Choosing a quant

	\| Quant \| Size \| Notes \|
	\|---\|---\|---\|
	\| `IQ1_M` \| 91.8 GB \| Last resort, only if nothing else fits. \|
	\| `Q4_K_M` \| 184.7 GB \| Recommended default. Best balance of size, speed and quality. \|

	> [!TIP]
	> Pick the largest file that fits your (V)RAM with room for context. `Q4_K_M` is the sweet spot for most setups; `Q6_K` or `Q8_0` for maximum fidelity.

	## Get started

	Run Hy3 locally with:

	- [Atomic Chat](https://atomic.chat): the easiest path. Open the app, search `AtomicChat/Hy3-GGUF`, pick a quant, hit Use this model.
	- llama.cpp: `llama-server -hf AtomicChat/Hy3-GGUF:Q4_K_M --jinja -c 8192`
	- Ollama: `ollama run hf.co/AtomicChat/Hy3-GGUF:Q4_K_M`
	- LM Studio / Jan: search the repo id, download any quant.

	## Best practices

	\| Parameter \| Value \|
	\|---\|---\|
	\| temperature \| 0.9 \|
	\| top_p \| 1.0 \|
	\| top_k \| -1 \|

	Tencent's recommended sampling configuration for `tencent/Hy3`.

	## Run in llama.cpp

	```bash
	git clone https://github.com/ggml-org/llama.cpp
	cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
	cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server
	```

	```bash
	./llama.cpp/build/bin/llama-server \
	-hf AtomicChat/Hy3-GGUF:Q4_K_M \
	--jinja -ngl 99 -c 8192 -fa on
	```

	## How these were made

	1. Download `tencent/Hy3` (original weights).
	2. Convert to f16 GGUF with [llama.cpp](https://github.com/ggml-org/llama.cpp).
	3. Build an importance matrix over our calibration corpus, published here as `imatrix-atomic.gguf`.
	4. Quantize the ladder with `--imatrix`.

	## License

	Original model by Tencent, released under the Apache 2.0 license. Full terms: [Apache 2.0](https://huggingface.co/tencent/Hy3/blob/main/LICENSE). Quantized by Atomic Chat.