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aaravriyer193/
MonkeGpt-Vivace

Name: MonkeGpt-Vivace
Rating: 2.5 (10 reviews)

text-generation-inference

endpoints_compatible

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🐵 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.

🍌 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.

🎼 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)

🎸 Quick Start (Inference)

python

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))

🥁 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.

🐒 About the Creator

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