Instructions to use harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist",
	filename="qwen3.5-makeup-botox-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16
# Run inference directly in the terminal:
llama-cli -hf harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16
# Run inference directly in the terminal:
llama-cli -hf harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16

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 harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16
# Run inference directly in the terminal:
./llama-cli -hf harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16

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 harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16

Use Docker

docker model run hf.co/harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16

LM Studio
Jan
Ollama
How to use harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist with Ollama:
```
ollama run hf.co/harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16
```

Unsloth Studio

How to use harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist 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 harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist 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 harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist to start chatting

How to use harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16

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": "harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16

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 harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist with Docker Model Runner:
```
docker model run hf.co/harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16
```

Lemonade

How to use harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull harshitsiwach/qwen-3.5-0.8B-makeup-botox-specialist:F16

Run and chat with the model

lemonade run user.qwen-3.5-0.8B-makeup-botox-specialist-F16

List all available models

lemonade list

Qwen 3.5 0.8B Makeup & Botox Specialist

This is a fine-tuned version of the Qwen 3.5 0.8B Base model, specialized in aesthetic consultations, surgical procedures, and cosmetic products.

Model Highlights

Specialization: Comprehensive knowledge of 30+ surgical procedures and 20+ cosmetic products.
Consultative Logic: Capable of recommending non-surgical makeup alternatives for common aesthetic goals (e.g., jawline enhancement, cheek volume).
Format: Trained using the ChatML template.
Dataset: 803 high-quality instruction-tuning pairs covering technical methods, gender-specific focus, and experimental technologies (AI-guided contouring, gene therapy for hair).

Usage Examples

Makeup Alternative

User: "I want the look of jawline implants but I want to avoid surgery. What makeup can I use?" Assistant: "To achieve a look similar to jawline implants without surgery, you can use Contour Powder/Cream. By applying it to the jawline with an angular brush, you can create shadows that mimic structural definition."

Technical Inquiry

User: "What is the procedure for Botulinum Toxin Injections?" Assistant: "Botulinum Toxin Injections (Botox/Dysport) involve micro-injections into target muscles (like the forehead or glabella) using fine-gauge needles to reduce dynamic wrinkles and relax target muscles."

Training Details

Base Model: Qwen/Qwen3.5-0.8B-Base
LoRA Config: r=16, alpha=32, target_modules=all-linear.
Hardware: Trained on NVIDIA RTX 4090.
Epochs: 5
Final Loss: 0.0761

Deployment

This model is optimized for mobile deployment (iOS/WebGPU) due to its small size (~0.8B parameters).

Downloads last month: 45

Safetensors

Model size

0.8B params

Tensor type

BF16

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