Instructions to use QuantLLM/functiongemma-270m-it-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantLLM/functiongemma-270m-it-4bit-mlx 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("QuantLLM/functiongemma-270m-it-4bit-mlx")

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)

Transformers

How to use QuantLLM/functiongemma-270m-it-4bit-mlx with Transformers:

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

pipe = pipeline("text-generation", model="QuantLLM/functiongemma-270m-it-4bit-mlx")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantLLM/functiongemma-270m-it-4bit-mlx")
model = AutoModelForCausalLM.from_pretrained("QuantLLM/functiongemma-270m-it-4bit-mlx", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use QuantLLM/functiongemma-270m-it-4bit-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantLLM/functiongemma-270m-it-4bit-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantLLM/functiongemma-270m-it-4bit-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantLLM/functiongemma-270m-it-4bit-mlx

SGLang

How to use QuantLLM/functiongemma-270m-it-4bit-mlx 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 "QuantLLM/functiongemma-270m-it-4bit-mlx" \
    --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": "QuantLLM/functiongemma-270m-it-4bit-mlx",
		"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 "QuantLLM/functiongemma-270m-it-4bit-mlx" \
        --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": "QuantLLM/functiongemma-270m-it-4bit-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use QuantLLM/functiongemma-270m-it-4bit-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "QuantLLM/functiongemma-270m-it-4bit-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantLLM/functiongemma-270m-it-4bit-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use QuantLLM/functiongemma-270m-it-4bit-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "QuantLLM/functiongemma-270m-it-4bit-mlx"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantLLM/functiongemma-270m-it-4bit-mlx" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use QuantLLM/functiongemma-270m-it-4bit-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "QuantLLM/functiongemma-270m-it-4bit-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "QuantLLM/functiongemma-270m-it-4bit-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "QuantLLM/functiongemma-270m-it-4bit-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use QuantLLM/functiongemma-270m-it-4bit-mlx with Docker Model Runner:
```
docker model run hf.co/QuantLLM/functiongemma-270m-it-4bit-mlx
```

Hermes Agent

How to use QuantLLM/functiongemma-270m-it-4bit-mlx with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "QuantLLM/functiongemma-270m-it-4bit-mlx"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantLLM/functiongemma-270m-it-4bit-mlx

Run Hermes

hermes

functiongemma-270m-it-4bit-mlx

File size: 4,151 Bytes

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---
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
---

<div align="center">

# 🍎 functiongemma-270m-it-4bit-mlx

**google/functiongemma-270m-it** converted to **MLX** format

[![QuantLLM](https://img.shields.io/badge/🚀_Made_with-QuantLLM-orange?style=for-the-badge)](https://github.com/codewithdark-git/QuantLLM)
[![Format](https://img.shields.io/badge/Format-MLX-blue?style=for-the-badge)]()
[![Quantization](https://img.shields.io/badge/Quant-Q4_K_M-green?style=for-the-badge)]()

<a href="https://github.com/codewithdark-git/QuantLLM">⭐ Star QuantLLM on GitHub</a>

</div>

---


## 📖 About This Model

This model is **[google/functiongemma-270m-it](https://huggingface.co/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](https://huggingface.co/google/functiongemma-270m-it) |
| **Format** | MLX |
| **Quantization** | Q4_K_M |
| **License** | apache-2.0 |
| **Created With** | [QuantLLM](https://github.com/codewithdark-git/QuantLLM) |


## 🚀 Quick Start

### Generate Text with mlx-lm

```python
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

```python
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

```bash
# 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) |

```bash
# Install dependencies
pip install mlx-lm
```


## 📊 Model Details

| Property | Value |
|----------|-------|
| **Original Model** | [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) |
| **Format** | MLX |
| **Quantization** | Q4_K_M |
| **License** | `apache-2.0` |
| **Export Date** | 2025-12-21 |
| **Exported By** | [QuantLLM v2.0](https://github.com/codewithdark-git/QuantLLM) |



---

## 🚀 Created with QuantLLM

<div align="center">

[![QuantLLM](https://img.shields.io/badge/🚀_QuantLLM-Ultra--fast_LLM_Quantization-orange?style=for-the-badge)](https://github.com/codewithdark-git/QuantLLM)

**Convert any model to GGUF, ONNX, or MLX in one line!**

```python
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")
```

<a href="https://github.com/codewithdark-git/QuantLLM">
  <img src="https://img.shields.io/github/stars/codewithdark-git/QuantLLM?style=social" alt="GitHub Stars">
</a>

**[📚 Documentation](https://github.com/codewithdark-git/QuantLLM#readme)** · 
**[🐛 Report Issue](https://github.com/codewithdark-git/QuantLLM/issues)** · 
**[💡 Request Feature](https://github.com/codewithdark-git/QuantLLM/issues)**

</div>