metadata
license: apache-2.0
base_model: google/functiongemma-270m-it
library_name: mlx
language:
- en
tags:
- quantllm
- mlx
- mlx-lm
- apple-silicon
- transformers
- q4_k_m
📖 About This Model
This model is google/functiongemma-270m-it converted to MLX format optimized for Apple Silicon (M1/M2/M3/M4) Macs with native acceleration.
| Property | Value |
|---|---|
| Base Model | google/functiongemma-270m-it |
| Format | MLX |
| Quantization | Q4_K_M |
| License | apache-2.0 |
| Created With | QuantLLM |
🚀 Quick Start
Generate Text with mlx-lm
from mlx_lm import load, generate
# Load the model
model, tokenizer = load("QuantLLM/functiongemma-270m-it-4bit-mlx")
# Simple generation
prompt = "Explain quantum computing in simple terms"
messages = [{"role": "user", "content": prompt}]
prompt_formatted = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True
)
# Generate response
text = generate(model, tokenizer, prompt=prompt_formatted, verbose=True)
print(text)
Streaming Generation
from mlx_lm import load, stream_generate
model, tokenizer = load("QuantLLM/functiongemma-270m-it-4bit-mlx")
prompt = "Write a haiku about coding"
messages = [{"role": "user", "content": prompt}]
prompt_formatted = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True
)
# Stream tokens as they're generated
for token in stream_generate(model, tokenizer, prompt=prompt_formatted, max_tokens=200):
print(token, end="", flush=True)
Command Line Interface
# Install mlx-lm
pip install mlx-lm
# Generate text
python -m mlx_lm.generate --model QuantLLM/functiongemma-270m-it-4bit-mlx --prompt "Hello!"
# Interactive chat
python -m mlx_lm.chat --model QuantLLM/functiongemma-270m-it-4bit-mlx
System Requirements
| Requirement | Minimum |
|---|---|
| Chip | Apple Silicon (M1/M2/M3/M4) |
| macOS | 13.0 (Ventura) or later |
| Python | 3.10+ |
| RAM | 8GB+ (16GB recommended) |
# Install dependencies
pip install mlx-lm
📊 Model Details
| Property | Value |
|---|---|
| Original Model | google/functiongemma-270m-it |
| Format | MLX |
| Quantization | Q4_K_M |
| License | apache-2.0 |
| Export Date | 2025-12-21 |
| Exported By | QuantLLM v2.0 |
🚀 Created with QuantLLM
Convert any model to GGUF, ONNX, or MLX in one line!
from quantllm import turbo
# Load any HuggingFace model
model = turbo("google/functiongemma-270m-it")
# Export to any format
model.export("mlx", quantization="Q4_K_M")
# Push to HuggingFace
model.push("your-repo", format="mlx")
