Instructions to use cloudyu/hy3-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cloudyu/hy3-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cloudyu/hy3-gguf",
	filename="hy3_q4k_mixed.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cloudyu/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 cloudyu/hy3-gguf
# Run inference directly in the terminal:
llama cli -hf cloudyu/hy3-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cloudyu/hy3-gguf
# Run inference directly in the terminal:
llama cli -hf cloudyu/hy3-gguf

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 cloudyu/hy3-gguf
# Run inference directly in the terminal:
./llama-cli -hf cloudyu/hy3-gguf

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 cloudyu/hy3-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cloudyu/hy3-gguf

Use Docker

docker model run hf.co/cloudyu/hy3-gguf

LM Studio
Jan

vLLM

How to use cloudyu/hy3-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cloudyu/hy3-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cloudyu/hy3-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cloudyu/hy3-gguf

Ollama
How to use cloudyu/hy3-gguf with Ollama:
```
ollama run hf.co/cloudyu/hy3-gguf
```

Unsloth Studio

How to use cloudyu/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 cloudyu/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 cloudyu/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 cloudyu/hy3-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use cloudyu/hy3-gguf with Docker Model Runner:
```
docker model run hf.co/cloudyu/hy3-gguf
```

Lemonade

How to use cloudyu/hy3-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cloudyu/hy3-gguf

Run and chat with the model

lemonade run user.hy3-gguf-{{QUANT_TAG}}

List all available models

lemonade list

hy3-gguf / README.md

cloudyu

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f80f66f verified 3 days ago

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metadata

license: apache-2.0
base_model: tencent/Hy3
tags:
  - hy3
  - hunyuan
  - hy_v3
  - gguf
  - mixture-of-experts
  - moe
language:
  - en
  - zh
library_name: hy3
pipeline_tag: text-generation

hy3-gguf — GGUF weights for Tencent Hy3 (tencent/Hy3)

GGUF-format weights for tencent/Hy3 (HYV3ForCausalLM, model_type: hy_v3), a 295B-parameter / 21B-active-parameter Mixture-of-Experts model from Tencent's Hunyuan ("Hy") team (tencent/Hy3).

These files were produced by the hy3 converter (hy3-convert) and are meant to be run with the hy3 inference engine, a from-scratch C/Metal/CUDA implementation.

⚠️ This GGUF does NOT work with llama.cpp

Despite the .gguf extension, these files are only usable by the hy3 engine. llama.cpp, ollama, LM Studio, text-generation-webui, koboldcpp, and any other llama.cpp-based tool cannot load these files. Three independent reasons:

Unknown architecture. The metadata declares general.architecture = "hy_v3". llama.cpp only knows hunyuan-moe, hunyuan-dense, hunyuan_vl — loading aborts with unknown model architecture: 'hy_v3'.

Custom metadata keys. All hyperparameters use the hy_v3.* prefix (hy_v3.block_count, hy_v3.expert_count, …), which llama.cpp does not look up.

Non-fused expert tensors. Experts are stored one tensor per expert (blk.N.ffn_gate_exps.0.gate_proj.weight, …1…, … — 46080 tensors), whereas llama.cpp expects experts fused into a single stacked 3D tensor per layer. This is a fundamentally different on-disk layout.

This is a custom GGUF readable only by the hy3 loader. Do not open issues against llama.cpp for these files.

How to run

Use the hy3 engine: https://github.com/yuhai-china/hy3

git clone https://github.com/yuhai-china/hy3
cd hy3
make            # macOS builds the Metal backend automatically

# download a GGUF from this repo, then:
./run_metal.sh -m /path/to/hy3_q4k_mixed.gguf -p "The capital of France is" -experts 8

Testing scope: the hy3 engine's performance work and benchmarks were developed and verified only on macOS / Apple Silicon (Metal backend), measured on an M2 Ultra (~20–27 tok/s decode depending on -experts). The CPU and CUDA backends exist in the source but were not exercised as part of that work — treat them as untested.

Files / quantization

The mixed-precision GGUF follows this scheme (see hy3_convert.c):

Tensor group	Type
Routed experts (`ffn_{gate,up,down}_exps`) — the bulk of the model	Q4_K
Attention q/k/v/o projections, shared-expert & dense FFN, `output.weight`	Q8_0
Norms, router (`ffn_gate_inp`), biases	F32
`token_embd.weight`	F16

Model facts


Architecture	`HYV3ForCausalLM` (`hy_v3`)
Layers	80 (layer 0 dense, layers 1–79 MoE)
Hidden size	4096
Attention	64 heads, GQA with 8 KV heads, head_dim 128
Experts	192 routed (top-8 activated) + 1 shared (always active)
Expert intermediate size	1536
Dense (layer 0) intermediate size	13312
Vocab size	120832 (120818 real tokens + padding)
RoPE	theta 11158840, `rotate_half` pairing
QK norm	per-head RMSNorm on Q and K, before RoPE
MoE routing	`sigmoid(router_logits)`; top-8 by `sigmoid + expert_bias`, combined using unbiased sigmoid weights, renormalized to sum 1, scaled by `router_scaling_factor = 2.826`

The engine supports a runtime top-k experts override (-experts 1..8) to trade quality for speed. On a small 13-question code/reasoning eval (greedy, no-think): experts=8 → 10/13, experts=4 → 7/13. Default is 8.

Chat template

Hy3 is instruction-tuned and expects the Hunyuan V3 chat format (the hy3 engine applies it automatically; use --raw to bypass). Single user turn, no-think:

<｜hy_begin_of_sentence:opensource｜><｜reasoning_mode:opensource｜>reasoning_effort:no_think<｜hy_User:opensource｜>{prompt}<｜hy_Assistant:opensource｜><think:opensource></think:opensource>

Generation stops on <｜hy_eos:opensource｜> (120025), <｜hy_endofsentence｜> (120001), or <｜hy_EOT｜> (120008).

License & attribution

Weights derive from tencent/Hy3; refer to the upstream repository for the governing model license. This is an unofficial community conversion, not affiliated with or endorsed by Tencent.