MonkeGpt-Vivace/README.md

library_name, inference, pipeline_tag

library_name	inference	pipeline_tag
transformers	true	text-generation

🐵 MonkeGpt-Vivace 🎶

🎹 Overview

MonkeGpt-Vivace is a high-speed, instruction-tuned language model based on the Qwen2.5-0.5B architecture. It has been fine-tuned using the UltraChat 200k dataset to transform it from a base autocomplete engine into a snappy, conversational assistant.

The name "Vivace" reflects its performance: lightweight, lively, and incredibly fast. At 0.5 billion parameters, it is optimized for edge deployment and serverless CPU inference.

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🍌 Features

• Instruction Following: Unlike the base model, Vivace understands user/assistant roles.
• Lightweight Brain: Fits into ~1.1GB of VRAM or System RAM.
• High Tempo: Designed for low-latency responses (30-50 tokens/sec on modern CPUs).
• Clean Dialogue: Fine-tuned to avoid the "hallucination loops" common in small base models.

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🎼 Training Details

• Base Model: Qwen/Qwen2.5-0.5B
• Dataset: HuggingFaceH4/ultrachat_200k (SFT subset)
• Hardware: 1x NVIDIA H100 (Lightning AI)
• Epochs: 2 (Optimized for balance and credit conservation)
• Precision: BFloat16
• Optimizer: AdamW (Fused)

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🎸 Quick Start (Inference)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "aaravriyer193/MonkeGpt-Vivace"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "Explain why monkeys like music in three short sentences."
messages = [{"role": "user", "content": prompt}]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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🥁 Limitations

While MonkeGpt-Vivace is smart for its size, it is still a 0.5B parameter model. It may struggle with complex mathematical proofs, deep philosophical reasoning, or very long-term memory across thousands of tokens.

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🐒 About the Creator

Developed by aaravriyer193 during a high-intensity 4-hour sprint on Lightning AI. 🚀