Instructions to use vi-c0de/gemmaiku-3-270m-it-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vi-c0de/gemmaiku-3-270m-it-experimental with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("vi-c0de/gemmaiku-3-270m-it-experimental") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use vi-c0de/gemmaiku-3-270m-it-experimental with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "vi-c0de/gemmaiku-3-270m-it-experimental"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "vi-c0de/gemmaiku-3-270m-it-experimental" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vi-c0de/gemmaiku-3-270m-it-experimental", "messages": [ {"role": "user", "content": "Hello"} ] }'
Gemmaiku 270M IT
A small model speaks,
Five, seven, and five again,
Haiku form is met.
Gemmaiku-3-270m-it is an experimental fine-tuned version of Google's Gemma-3-270m-it, specialized in generating traditional 5-7-5 syllable haikus on any topic.
It was trained locally using LoRA on Apple Silicon using the Apple MLX framework.
Syllable Strictness: The model is trained to target a strict 5-7-5 structure. However, due to its compact size (270M parameters), it may occasionally deviate slightly on complex or out-of-distribution prompts. For best results, keep temperatures low (
0.1-0.3) and use the correct chat template.
Usage
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
# Load the model
model, tokenizer = load("vi-c0de/gemmaiku-3-270m-it-experimental")
# Keep temperature low for strict syllable adherence
sampler = make_sampler(temp=0.3)
# Format using the tokenizer chat template (ensures correct bos token)
messages = [{"role": "user", "content": "I want to become a hardware engineer"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Generate response
response = generate(model, tokenizer, prompt=prompt, sampler=sampler)
print(response.split("<end_of_turn>")[0].strip())
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Model size
0.3B params
Tensor type
BF16
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