Instructions to use aaravriyer193/MonkeGpt-Vivace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aaravriyer193/MonkeGpt-Vivace with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aaravriyer193/MonkeGpt-Vivace")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aaravriyer193/MonkeGpt-Vivace")
model = AutoModelForCausalLM.from_pretrained("aaravriyer193/MonkeGpt-Vivace")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aaravriyer193/MonkeGpt-Vivace with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aaravriyer193/MonkeGpt-Vivace"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aaravriyer193/MonkeGpt-Vivace",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aaravriyer193/MonkeGpt-Vivace

SGLang

How to use aaravriyer193/MonkeGpt-Vivace with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aaravriyer193/MonkeGpt-Vivace" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aaravriyer193/MonkeGpt-Vivace",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aaravriyer193/MonkeGpt-Vivace" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aaravriyer193/MonkeGpt-Vivace",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aaravriyer193/MonkeGpt-Vivace with Docker Model Runner:
```
docker model run hf.co/aaravriyer193/MonkeGpt-Vivace
```

🐵 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)

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. 🚀

Downloads last month: 15

Safetensors

Model size

0.5B params

Tensor type

BF16

aaravriyer193
/

MonkeGpt-Vivace