Instructions to use Acnoryx/Airy-Core-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Acnoryx/Airy-Core-0.8B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Acnoryx/Airy-Core-0.8B",
	filename="acnoryx-0.8b-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Acnoryx/Airy-Core-0.8B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Acnoryx/Airy-Core-0.8B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Acnoryx/Airy-Core-0.8B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Acnoryx/Airy-Core-0.8B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Acnoryx/Airy-Core-0.8B: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 Acnoryx/Airy-Core-0.8B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Acnoryx/Airy-Core-0.8B: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 Acnoryx/Airy-Core-0.8B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Acnoryx/Airy-Core-0.8B:Q4_K_M

Use Docker

docker model run hf.co/Acnoryx/Airy-Core-0.8B:Q4_K_M

LM Studio
Jan

vLLM

How to use Acnoryx/Airy-Core-0.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Acnoryx/Airy-Core-0.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Acnoryx/Airy-Core-0.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Acnoryx/Airy-Core-0.8B:Q4_K_M

Ollama
How to use Acnoryx/Airy-Core-0.8B with Ollama:
```
ollama run hf.co/Acnoryx/Airy-Core-0.8B:Q4_K_M
```

Unsloth Studio new

How to use Acnoryx/Airy-Core-0.8B 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 Acnoryx/Airy-Core-0.8B 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 Acnoryx/Airy-Core-0.8B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Acnoryx/Airy-Core-0.8B to start chatting

Docker Model Runner
How to use Acnoryx/Airy-Core-0.8B with Docker Model Runner:
```
docker model run hf.co/Acnoryx/Airy-Core-0.8B:Q4_K_M
```

Lemonade

How to use Acnoryx/Airy-Core-0.8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Acnoryx/Airy-Core-0.8B:Q4_K_M

Run and chat with the model

lemonade run user.Airy-Core-0.8B-Q4_K_M

List all available models

lemonade list

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Acnoryx AI Release

Overview

Model family: Qwen/Qwen3.5-0.8B
Project name: acnoryx
Model size: 0.8b
GGUF quantizations: F16, Q8_0, Q5_K_M, Q4_K_M, Q4_0, IQ4_NL, IQ4_XS
Domain: acne, acne-prone skin, skincare, and dermatology guidance

App

Google Play: https://play.google.com/store/apps/details?id=com.fivecanh.acnoryx

Default behavior

The saved tokenizer/chat template injects a short default system prompt when no system message is provided.
That means the model can still understand its identity as Acnoryx AI in chat mode without a long system prompt.
For best results, write Vietnamese with full accents, or use natural English.

Prompt examples

Tiếng Việt: Da em nhiều mụn viêm ở má, routine hiện tại chỉ có sữa rửa mặt và kem dưỡng. Em nên ưu tiên gì trước?
Tiếng Việt: Kết quả quét của tôi có mụn đầu đen 32%, mụn mủ 21%, thâm mụn 18%. Hãy tóm tắt đúng theo dữ liệu.
English: I have oily acne-prone skin with dark marks after breakouts. What should I prioritize first?

Included folders

gguf/: GGUF exports for llama.cpp runtimes
hf_transformers/: merged Hugging Face Transformers model

Training stack

Transformers + PEFT + TRL bf16 LoRA
Qwen3.5 hybrid architecture with fast linear path enabled when available

Prompting

See PROMPT_TEMPLATE.txt for usage guidance.

Evaluation Snapshot

Release GGUFs were retested on the curated release_eval_v1 set with 58 bilingual questions in both thinking and non-thinking modes.

Quant	Think	No-Think	Avg	Notes
Q8_0	86.2%	87.9%	87.0%	Best overall score in the current release rerun
Q5_K_M	89.7%	82.8%	86.2%	Strong think-mode quality
IQ4_NL	86.2%	86.2%	86.2%	Best balanced sub-500 MB option
F16	87.9%	81.0%	84.4%	Highest-fidelity source export
IQ4_XS	84.5%	81.0%	82.8%	Smaller release option
Q4_K_M	82.8%	81.0%	81.9%	Usable but clearly weaker than Q8_0 / Q5_K_M
Q4_0	77.6%	75.9%	76.8%	Lowest-quality release quant

Deployment Guidance

Recommended default release quant: Q8_0
Best size/quality trade-off under 500 MB: IQ4_NL
Keep Q4_0 only for constrained experiments, not as a primary deployment target
Current release family remains below the older internal 96% gate, so these artifacts should be treated as interim bundles rather than a final quality-signoff build

Test Results

Latest automated GGUF test results are below:

acnoryx-0.8b-f16: Think mode 87.9%, No-Think mode 81.0%
acnoryx-0.8b-iq4_nl: Think mode 86.2%, No-Think mode 86.2%
acnoryx-0.8b-iq4_xs: Think mode 84.5%, No-Think mode 81.0%
acnoryx-0.8b-q4_0: Think mode 77.6%, No-Think mode 75.9%
acnoryx-0.8b-q4_k_m: Think mode 82.8%, No-Think mode 81.0%
acnoryx-0.8b-q5_k_m: Think mode 89.7%, No-Think mode 82.8%
acnoryx-0.8b-q8_0: Think mode 86.2%, No-Think mode 87.9%

Full detailed results in results/release_gguf_0.8b/TEST_RESULTS.json.

For cross-family comparison with research quants, see results/COMPARISON.md in the workspace.

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GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for Acnoryx/Airy-Core-0.8B

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Quantized

(106)

this model