Instructions to use ZiboPeng/Yuna-1.7B-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZiboPeng/Yuna-1.7B-v0 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B")
model = PeftModel.from_pretrained(base_model, "ZiboPeng/Yuna-1.7B-v0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ZiboPeng/Yuna-1.7B-v0 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 ZiboPeng/Yuna-1.7B-v0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZiboPeng/Yuna-1.7B-v0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ZiboPeng/Yuna-1.7B-v0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZiboPeng/Yuna-1.7B-v0: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 ZiboPeng/Yuna-1.7B-v0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ZiboPeng/Yuna-1.7B-v0: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 ZiboPeng/Yuna-1.7B-v0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZiboPeng/Yuna-1.7B-v0:Q4_K_M

Use Docker

docker model run hf.co/ZiboPeng/Yuna-1.7B-v0:Q4_K_M

LM Studio
Jan

vLLM

How to use ZiboPeng/Yuna-1.7B-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZiboPeng/Yuna-1.7B-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZiboPeng/Yuna-1.7B-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZiboPeng/Yuna-1.7B-v0:Q4_K_M

Ollama
How to use ZiboPeng/Yuna-1.7B-v0 with Ollama:
```
ollama run hf.co/ZiboPeng/Yuna-1.7B-v0:Q4_K_M
```

Unsloth Studio

How to use ZiboPeng/Yuna-1.7B-v0 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 ZiboPeng/Yuna-1.7B-v0 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 ZiboPeng/Yuna-1.7B-v0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZiboPeng/Yuna-1.7B-v0 to start chatting

How to use ZiboPeng/Yuna-1.7B-v0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZiboPeng/Yuna-1.7B-v0: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": "ZiboPeng/Yuna-1.7B-v0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ZiboPeng/Yuna-1.7B-v0 with Docker Model Runner:
```
docker model run hf.co/ZiboPeng/Yuna-1.7B-v0:Q4_K_M
```

Lemonade

How to use ZiboPeng/Yuna-1.7B-v0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZiboPeng/Yuna-1.7B-v0:Q4_K_M

Run and chat with the model

lemonade run user.Yuna-1.7B-v0-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ZiboPeng/Yuna-1.7B-v0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZiboPeng/Yuna-1.7B-v0: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 ZiboPeng/Yuna-1.7B-v0:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ZiboPeng/Yuna-1.7B-v0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZiboPeng/Yuna-1.7B-v0: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 "ZiboPeng/Yuna-1.7B-v0: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"

Yuna-1.7B-v0

Yuna is a compact Chinese conversational persona: gentle, perceptive, and mature, but direct when a clear recommendation is needed.

What this release is

This repository contains a LoRA adapter trained on top of Qwen/Qwen3-1.7B. It is not a standalone base model and does not claim to have been trained from scratch.

The adapter steers conversational style toward practical empathy: acknowledge the person, identify the core issue, then offer a clear next step. It is designed to avoid both empty reassurance and performative cuteness.

Persona

Warm and attentive without encouraging dependence.
Mature, concise, and willing to disagree when the facts point elsewhere.
Uses Chinese naturally; no forced roleplay, slogans, or repeated self-introduction.
Yuna's fictional origin is part of the character setting, not a claim about a real person.

Training

Base: Qwen/Qwen3-1.7B
Method: QLoRA, 4-bit NF4
Adapter rank: 16
Training data: 41 original Chinese supervised dialogue examples
Epochs: 5
Intended scope: personal chat, early persona research, and further curated fine-tuning

This is a small v0 release. It should be evaluated carefully before any high-stakes or production use.

Load with Transformers + PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "Qwen/Qwen3-1.7B"
adapter = "ZiboPeng/Yuna-1.7B-v0"

tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

Use the Qwen chat template with thinking disabled for regular Yuna chat.

License and attribution

Please follow the license and acceptable-use terms of the Qwen base model. This repository adds only the Yuna adapter, tokenizer files, chat template, and documentation.

Downloads last month: 43

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for ZiboPeng/Yuna-1.7B-v0

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Adapter

(640)

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