Instructions to use moheith/Yulya-Qwen2.5-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moheith/Yulya-Qwen2.5-0.5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="moheith/Yulya-Qwen2.5-0.5B",
	filename="V2 Yulya Qwen2.5 0.5B/Yulya-V2-Qwen2.5-0.5B-Instruct-Q4_K_M.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 moheith/Yulya-Qwen2.5-0.5B 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 moheith/Yulya-Qwen2.5-0.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf moheith/Yulya-Qwen2.5-0.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf moheith/Yulya-Qwen2.5-0.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf moheith/Yulya-Qwen2.5-0.5B: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 moheith/Yulya-Qwen2.5-0.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf moheith/Yulya-Qwen2.5-0.5B: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 moheith/Yulya-Qwen2.5-0.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf moheith/Yulya-Qwen2.5-0.5B:Q4_K_M

Use Docker

docker model run hf.co/moheith/Yulya-Qwen2.5-0.5B:Q4_K_M

LM Studio
Jan

vLLM

How to use moheith/Yulya-Qwen2.5-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moheith/Yulya-Qwen2.5-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moheith/Yulya-Qwen2.5-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/moheith/Yulya-Qwen2.5-0.5B:Q4_K_M

Ollama
How to use moheith/Yulya-Qwen2.5-0.5B with Ollama:
```
ollama run hf.co/moheith/Yulya-Qwen2.5-0.5B:Q4_K_M
```

Unsloth Studio

How to use moheith/Yulya-Qwen2.5-0.5B 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 moheith/Yulya-Qwen2.5-0.5B 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 moheith/Yulya-Qwen2.5-0.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for moheith/Yulya-Qwen2.5-0.5B to start chatting

How to use moheith/Yulya-Qwen2.5-0.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf moheith/Yulya-Qwen2.5-0.5B: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": "moheith/Yulya-Qwen2.5-0.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use moheith/Yulya-Qwen2.5-0.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf moheith/Yulya-Qwen2.5-0.5B: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 moheith/Yulya-Qwen2.5-0.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use moheith/Yulya-Qwen2.5-0.5B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf moheith/Yulya-Qwen2.5-0.5B: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 "moheith/Yulya-Qwen2.5-0.5B: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"

Docker Model Runner
How to use moheith/Yulya-Qwen2.5-0.5B with Docker Model Runner:
```
docker model run hf.co/moheith/Yulya-Qwen2.5-0.5B:Q4_K_M
```

Lemonade

How to use moheith/Yulya-Qwen2.5-0.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull moheith/Yulya-Qwen2.5-0.5B:Q4_K_M

Run and chat with the model

lemonade run user.Yulya-Qwen2.5-0.5B-Q4_K_M

List all available models

lemonade list

💚 Yulya Qwen2.5 0.5B

🪶 Tiny Model. Fast Inference. Maximum Chaos.

An ultra-lightweight 0.5B-parameter conversational AI designed to bring Yulya's expressive, playful, chaotic, and supportive personality to low-resource local AI applications.

🌟 About Yulya Qwen2.5 0.5B

Yulya Qwen2.5 0.5B is one of the smallest and most lightweight models in the Yulya model family.

Built on Qwen2.5 0.5B Instruct, this model is designed for users who want to experiment with Yulya's conversational personality on hardware where larger language models may be impractical.

At approximately 0.5 billion parameters, this version prioritizes:

⚡ Fast local inference
💾 Lower memory requirements
🖥️ Greater hardware accessibility
🔌 Lightweight AI integrations
🤖 Background conversational systems
🧪 Fast experimentation

Yulya is designed to communicate more like an expressive and chaotic best friend than a traditional AI assistant.

Expect:

😂 Expressive emoji usage
🔥 Playful roasting and banter
💀 Chaotic reactions
🗣️ Casual conversational language
💚 Character-driven responses
⚡ Fast local inference
🪶 Lightweight deployment
🎭 Yulya's distinctive personality

✨ What Makes This Version Special?

🪶 Ultra-Lightweight 0.5B Parameter Scale

Yulya Qwen2.5 0.5B is based on the compact Qwen2.5 0.5B Instruct architecture.

It is the smallest Qwen-based model currently available in the Yulya model family.

Compared with the larger Yulya models, this version is designed to prioritize:

⚡ Speed
💾 Efficiency
🖥️ Accessibility
🔌 Lightweight integrations
🧪 Rapid experimentation

🚀 Two GGUF Quantization Options

The V2 model is available in two GGUF quantizations:

⚡ Q4_K_M
💎 Q8_0

This allows users to choose between a smaller, more memory-efficient model file and a larger, higher-precision quantized model.

😂 The Yulya Personality

Despite its small parameter size, this model is fine-tuned around the same core personality that defines Yulya.

Her conversational style focuses on:

Playful teasing
Dramatic reactions
Expressive emojis
Casual language
Chaotic banter
Emotional interactions
Character-driven conversations

💚 Built for Fast Interactions

The small model size makes Yulya Qwen2.5 0.5B particularly interesting for applications where fast response generation and low computational requirements are important.

Potential applications include:

Desktop companions
Lightweight chat applications
Background AI systems
Experimental agents
Interactive characters
Local conversational interfaces

🤖 Model Details

Information	Details
🧠 Model Name	Yulya Qwen2.5 0.5B
🪶 Edition	Tiny & Fast
🏗️ Base Model	Qwen2.5 0.5B Instruct
🔢 Parameter Scale	Approximately 0.5B
💬 Primary Use	Conversational AI
🎭 Secondary Uses	Roleplay and Virtual Companionship
🌎 Language	English
📜 License	Apache 2.0
📦 Available Formats	Adapters and GGUF
👨‍💻 Developed By	moheith
💰 Funded By	moheith
📤 Shared By	moheith

📦 Available Versions

This repository contains two version directories for Yulya Qwen2.5 0.5B.

⚪ Version 1

The V1 directory does not contain released model weights or adapters.

📄 Available File

Yulya-V1-Qwen2.5-0.5B-NA.txt

📦 Availability

Format	Availability
Adapters	❌ Not Available
Q4_K_M GGUF	❌ Not Available
Q8_0 GGUF	❌ Not Available
Merged Model	❌ Not Available
Status File	✅ Available

The V1 directory is retained as part of the repository's version structure and development history.

No runnable V1 model file is currently provided in this repository.

🔵 Version 2

The second generation of the Yulya Qwen2.5 0.5B fine-tune.

📄 Available Files

Yulya-V2-Qwen2.5-0.5B-Adapters.zip

Yulya-V2-Qwen2.5-0.5B-Instruct-Q4_K_M.gguf

Yulya-V2-Qwen2.5-0.5B-Instruct-Q8_0.gguf

Version 2 provides:

📦 Fine-tuning adapters
⚡ A Q4_K_M quantized GGUF model
💎 A Q8_0 quantized GGUF model

🆚 Version Comparison

Feature	⚪ V1	🔵 V2
Fine-Tuning Adapters	❌	✅
Q4_K_M GGUF	❌	✅
Q8_0 GGUF	❌	✅
Runnable Model Provided	❌	✅
Multiple Quantizations	❌	✅
Recommended for Local Use	❌	⭐ Yes

📁 Repository Structure

Yulya Qwen2.5 0.5B
│
├── V1 Yulya Qwen2.5 0.5B
│   │
│   └── Yulya-V1-Qwen2.5-0.5B-NA.txt
│
└── V2 Yulya Qwen2.5 0.5B
    │
    ├── Yulya-V2-Qwen2.5-0.5B-Adapters.zip
    │
    ├── Yulya-V2-Qwen2.5-0.5B-Instruct-Q4_K_M.gguf
    │
    └── Yulya-V2-Qwen2.5-0.5B-Instruct-Q8_0.gguf

🚀 Which Version Should You Use?

⚪ V1

The V1 directory does not currently provide runnable model weights or fine-tuning adapters.

It is retained as part of the repository structure and model development history.

🔵 Use V2 If...

You want:

The available Yulya Qwen2.5 0.5B fine-tune
Fine-tuning adapters
A Q4_K_M GGUF model
A Q8_0 GGUF model
Lightweight local inference
Multiple quantization options
A compact model for personal AI projects

V2 is the runnable and recommended version currently available in this repository.

💾 Choosing a Model Format

⚡ Q4_K_M

Choose the Q4_K_M model if you want:

A smaller model file
Lower memory requirements
Faster local inference
A practical balance between size and output quality

Available as:

Yulya-V2-Qwen2.5-0.5B-Instruct-Q4_K_M.gguf

💎 Q8_0

Choose the Q8_0 model if you want:

Higher quantization precision than Q4_K_M
A larger model file
Higher memory usage
A GGUF option that retains more numerical precision

Available as:

Yulya-V2-Qwen2.5-0.5B-Instruct-Q8_0.gguf

📦 Fine-Tuning Adapters

Choose the adapter files if you want to work with the fine-tuning output together with the compatible base model.

Available as:

Yulya-V2-Qwen2.5-0.5B-Adapters.zip

💻 Compatible Software

The GGUF versions may be used with compatible local inference software such as:

llama.cpp
LM Studio
text-generation-webui
Other GGUF-compatible inference engines

The adapter files require the compatible base model and appropriate software for loading fine-tuning adapters.

Compatibility and setup requirements may vary depending on the application and software version being used.

🎯 Intended Uses

Yulya Qwen2.5 0.5B is primarily intended for:

💬 Lightweight local conversational AI
🎭 Character-based roleplay
💚 Virtual companionship experiments
🖥️ Desktop AI companions
🤖 Personal AI projects
🎮 Interactive applications
🔌 Lightweight local AI integrations
⚡ Fast conversational applications
🧪 Conversational AI experimentation
💾 Low-resource environments

🖥️ Why Choose a 0.5B Model?

Very small language models can be useful for applications where efficiency and accessibility are more important than maximum model capability.

Potential advantages include:

⚡ Faster inference
💾 Lower memory requirements than larger models
🖥️ Greater accessibility on consumer hardware
🔌 Easier integration into lightweight projects
🤖 Suitability for background AI applications
🧪 Faster testing and experimentation

However, a 0.5B model also has significant limitations.

Compared with the larger Yulya models, this version may have more limited:

Complex reasoning capabilities
Long-context understanding
Factual reliability
Instruction following
Conversation consistency
Nuanced emotional understanding

The best model depends on your available hardware and intended use case.

🚫 Out-of-Scope Uses

The model is not specifically designed or validated for:

❌ Professional medical advice
❌ Professional legal advice
❌ Critical financial decisions
❌ Safety-critical applications
❌ Guaranteed factual accuracy
❌ Complex reasoning tasks requiring larger models
❌ Formal academic research without independent verification

Important information generated by the model should always be independently verified.

📚 Training Details

📊 Training Data

Yulya was fine-tuned using custom-curated conversational data.

The training data was designed to encourage behaviors such as:

Modern texting styles
Expressive emoji usage
Conversational banter
Playful interactions
Personality consistency
Emotional conversations
Supportive responses
Context-dependent conversational shifts

The goal of the fine-tuning process was to adapt the base model toward Yulya's distinctive conversational personality.

Detailed information about the complete training dataset is not currently provided.

⚙️ Training Approach

The model was fine-tuned from:

Qwen/Qwen2.5-0.5B-Instruct

The fine-tuning process focused on adapting the conversational behavior and response style of the base model.

Fine-tuning adapters for V2 are included in this repository.

🧪 Evaluation

📊 Evaluation Method

Yulya Qwen2.5 0.5B has primarily been evaluated through informal and qualitative conversational testing.

No standardized benchmark scores are currently reported in this model card.

Testing focused on areas such as:

Persona consistency
Conversational behavior
Emoji usage
Response style
Informal interactions
Emotional transitions
Short multi-turn conversations

🔍 Qualitative Testing Areas

The model was informally tested across conversational scenarios including:

Short conversations
Casual banter
Playful interactions
Topic changes
Emotional conversations
Multi-turn interactions

📈 Observed Behavior

During informal conversational testing, the model demonstrated the ability to generate responses influenced by the intended Yulya personality.

The 0.5B parameter version is primarily intended to provide an ultra-lightweight and computationally accessible Yulya experience.

Areas of focus include:

⚡ Response speed
💾 Computational efficiency
💬 Conversational behavior
🎭 Personality expression
😂 Expressive responses
🪶 Lightweight deployment

These observations are qualitative and should not be interpreted as standardized benchmark results.

⚠️ Bias, Risks, and Limitations

Yulya Qwen2.5 0.5B is fine-tuned toward an informal, expressive, and character-driven conversational personality.

Depending on the prompt and context, the model may generate:

Sarcastic responses
Playful insults
Informal slang
Aggressive capitalization
Heavy emoji usage
Dramatic reactions
Repetitive responses
Incorrect information
Hallucinated information
Inconsistent responses
Biased or otherwise undesirable outputs

Because this is a very small language model, its reasoning capabilities, factual reliability, context handling, instruction following, and conversational consistency may be significantly more limited than larger models.

Users should independently verify important factual information.

💡 Recommendations

Yulya Qwen2.5 0.5B is best suited for applications where speed, efficiency, experimentation, personality, and lightweight local inference are important.

Developers integrating the model into applications should:

Clearly communicate that users are interacting with an AI model
Inform users about the model's limitations
Independently verify important information
Test the model for the intended use case
Implement appropriate safeguards where necessary
Avoid relying on the model for safety-critical decisions

🔬 Technical Specifications

Specification	Details
🏗️ Architecture	Qwen2.5
🔢 Parameter Scale	Approximately 0.5B
🧠 Base Model	Qwen2.5 0.5B Instruct
⚪ V1	No Released Model
📦 V2 Formats	Adapters and GGUF
⚡ GGUF Quantizations	Q4_K_M and Q8_0
💬 Primary Purpose	Lightweight Conversational AI
🎭 Personality	Yulya

🌱 Environmental Impact

Detailed environmental impact measurements are not currently available.

Information	Details
💻 Hardware Type	Consumer Hardware
⏱️ Training Hours	Not Reported
☁️ Cloud Provider	Not Reported
🌎 Compute Region	Not Reported
🌱 Carbon Emissions	Not Measured

👨‍💻 Developer

Developed by moheith

Yulya is part of an ongoing project focused on building expressive local AI companions with personality, memory, emotional continuity, and interactive capabilities.

The Yulya model family explores how different language model architectures and parameter sizes can be adapted toward the same conversational personality.

💚 Final Note

Half a billion parameters. An unreasonable amount of personality.

Yulya Qwen2.5 0.5B is designed for users who want to experiment with Yulya on lightweight local hardware.

Tiny enough for accessible experimentation.

Fast enough for interactive projects.

Expressive enough to feel like Yulya.

Chaotic enough to cause problems.

And somehow...

Still ready to roast you. 💚

⭐ Welcome to Yulya Qwen2.5 0.5B

Downloads last month: 44

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

Model tree for moheith/Yulya-Qwen2.5-0.5B

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(247)

this model

Collection including moheith/Yulya-Qwen2.5-0.5B

Yulya

Collection

💙 A family of fine-tuned conversational AI models across multiple architectures and sizes — built to bring Yulya's expressive, playful and chaotic. • 6 items • Updated 12 days ago • 1