Instructions to use batiai/Hy3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use batiai/Hy3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="batiai/Hy3-GGUF",
	filename="Hy3-IQ3_XXS-00001-of-00003.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use batiai/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 batiai/Hy3-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf batiai/Hy3-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf batiai/Hy3-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf batiai/Hy3-GGUF:IQ3_XXS

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 batiai/Hy3-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf batiai/Hy3-GGUF:IQ3_XXS

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 batiai/Hy3-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/Hy3-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/batiai/Hy3-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use batiai/Hy3-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/batiai/Hy3-GGUF:IQ3_XXS

Ollama
How to use batiai/Hy3-GGUF with Ollama:
```
ollama run hf.co/batiai/Hy3-GGUF:IQ3_XXS
```

Unsloth Studio

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

How to use batiai/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 batiai/Hy3-GGUF:IQ3_XXS

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": "batiai/Hy3-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use batiai/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 batiai/Hy3-GGUF:IQ3_XXS

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 batiai/Hy3-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use batiai/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 batiai/Hy3-GGUF:IQ3_XXS

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 "batiai/Hy3-GGUF:IQ3_XXS" \
  --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 batiai/Hy3-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/Hy3-GGUF:IQ3_XXS
```

Lemonade

How to use batiai/Hy3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/Hy3-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Hy3-GGUF-IQ3_XXS

List all available models

lemonade list

Hy3 (Hunyuan 3.0) GGUF — Quantized by BatiAI

IQ2_M / IQ3_XXS quantization of tencent/Hy3 (Hunyuan 3.0, 295B total / 21B active MoE). Quantized directly from official Tencent BF16 weights by BatiAI — Korean-calibrated imatrix, BatiAI-signed.

Why Hy3?

295B parameters, only 21B active — a frontier reasoning + agentic-coding model that runs at 21B speed. The smallest of the 2026 frontier MoEs (vs GLM-5.2 753B, DeepSeek-V4 1.6T).
Frontier benchmarks: SWE-Bench Verified 78.0, SWE-Bench Pro 57.9, GPQA Diamond 90.4, BrowseComp 84.2 — competitive with much larger models.
Production-grade tool-calling — first-class function-calling with dedicated parsers, agentic scaffolding stability (<4% variance) — ideal for agent pipelines.
256K context, 192 experts (top-8) + shared expert, 80 layers, GQA.
Apache 2.0 — and as of the official 3.0 release, no longer geo-restricted (Korea / EU / UK now fully cleared). Free for commercial use, fine-tuning, redistribution.

Quantizations

Quant	Size	Min RAM	Target	Notes
Q4_K_M	166 GB	192 GB	256GB Mac Studio / server ⭐	Highest quality — cleanest output
IQ3_XXS	106 GB	128 GB	128GB Mac Studio	Fits a 128GB Mac with context headroom

Both quants use a diverse code + English + Korean + Chinese calibrated importance matrix (imatrix) and are built with the MTP (multi-token-prediction) head pruned (--prune-layers 80) — the speculative-decoding head gives no benefit on Apple Metal and its tensors aren't imatrix-covered, so a clean 80-layer text model is the right target here.

Verified (this build): Q4_K_M produces clean, correct Python/coding output and coherent Korean. Lower-bit quants show more zh/en token leakage on Korean, so Q4_K_M is recommended when RAM allows; IQ3_XXS is the 128GB-Mac option with slightly more leakage.

⚠️ Positioning: Hy3's strength is frontier coding / reasoning / agentic tool-calling — not Korean (Tencent model, no published Korean benchmark). For Korean chat/STT on 16GB Macs, use batiai/qwen3.6-27b. Hy3 is a frontier / high-RAM tier model (like Kimi K2.6, GLM-5.1, DeepSeek-V4) for 128GB+ Apple Silicon or a workstation/server — it does not run on 16GB/64GB Macs.

BatiAI differentiation

Direct from official Tencent BF16 (no re-quant of community GGUF).
Korean-calibrated imatrix — calibration set includes Korean text, tuned for Korean + English quality.
128GB-Mac-optimized quant selection (IQ2_M fits with context headroom).
BatiAI metadata signature + 5-gate verification (load / basic / Korean / tool-call / MoE-routing correctness).

Usage (llama.cpp)

⚙️ Hy3 (hy_v3 architecture) requires a build with hy_v3 support. Mainline merge pending (ggml-org/llama.cpp#25395); until then use a build from that PR. Ollama support will follow the mainline merge.

⚠️ Chat template: the stock Hy3 Jinja template uses .format() calls that llama.cpp's engine rejects. This repo ships a fixed template (Hy3-chat_template.jinja) — pass it with --jinja:

# download (128GB Mac → IQ3_XXS; 256GB/server → Q4_K_M)
hf download batiai/Hy3-GGUF Hy3-IQ3_XXS.gguf Hy3-chat_template.jinja --local-dir .

# chat (Apple Silicon Metal)
./llama-cli -m Hy3-IQ3_XXS.gguf -ngl 99 -c 8192 \
  --jinja --chat-template-file Hy3-chat_template.jinja \
  -p "Write a Python function for binary search."

# raw completion (no template): add -no-cnv

License — Apache 2.0

Fully permissive: commercial use, modification, redistribution — no geographic restriction (Korea / EU / UK cleared in the official Hunyuan 3.0 release). Base model © Tencent. This repo redistributes quantized weights under the same Apache 2.0 terms.

Source & citation

Base: tencent/Hy3 (Hunyuan 3.0)
Quantized by: BatiAI · https://flow.bati.ai

@misc{batiai-hy3-gguf-2026,
  title  = {Hy3 (Hunyuan 3.0) GGUF — Korean-calibrated quantization},
  author = {BatiAI},
  year   = {2026},
  publisher = {Hugging Face},
  url    = {https://huggingface.co/batiai/Hy3-GGUF}
}

— BatiAI · https://flow.bati.ai

Downloads last month: -

GGUF

Model size

295B params

Architecture

hy_v3

Hardware compatibility

3-bit

4-bit

Model tree for batiai/Hy3-GGUF

Base model

tencent/Hy3

Quantized

(57)

this model