qwen2.5-omni-3b-cagatay

🧠 Qwen 2.5 Omni 3B — Cagatay LoRA

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A LoRA fine-tune of Qwen/Qwen2.5-3B-Instruct for robotics voice command understanding.

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🎯 What is this?

This is a LoRA adapter fine-tuned to understand and generate responses for robotics voice commands — pick & place, navigation, manipulation, multi-step tasks, spatial reasoning, and safety commands.

Trained on 106K examples from the cagataydev/omni-commands dataset covering 10 categories of human→robot interaction.

📊 Training Details

🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model + LoRA adapter
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-3B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "cagataydev/qwen2.5-omni-3b-cagatay")
tokenizer = AutoTokenizer.from_pretrained("cagataydev/qwen2.5-omni-3b-cagatay")

# Robotics command
messages = [{"role": "user", "content": "Pick up the red cube on the table and bring it to me"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🤖 Use Cases

• Robot instruction following — Parse natural language into actionable robot commands
• Voice-controlled robots — Understand spoken commands (pair with ASR)
• Embodied AI assistants — Multi-step task planning from human speech
• Neon VLA backbone — Powers the Neon VLA system

🗂️ Command Categories

📦 Related Resources

🏗️ Training Pipeline

omni-commands dataset (106K examples)
    ↓
Qwen2.5-3B-Instruct (base)
    ↓  LoRA r=64, α=128
Fine-tuned adapter (120M params)
    ↓
Merged into Neon VLA pipeline

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