Instructions to use mlx-community/functiongemma-270m-it-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/functiongemma-270m-it-8bit 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("mlx-community/functiongemma-270m-it-8bit")

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
LM Studio

Pi new

How to use mlx-community/functiongemma-270m-it-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/functiongemma-270m-it-8bit"

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": "mlx-community/functiongemma-270m-it-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/functiongemma-270m-it-8bit 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 "mlx-community/functiongemma-270m-it-8bit"

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 mlx-community/functiongemma-270m-it-8bit

Run Hermes

hermes

MLX LM

How to use mlx-community/functiongemma-270m-it-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/functiongemma-270m-it-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/functiongemma-270m-it-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/functiongemma-270m-it-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

prince-canuma commited on Dec 18, 2025

Commit

4e52103

verified ·

1 Parent(s): 647d38e

Update README.md

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README.md +5 -5

README.md CHANGED Viewed

@@ -13,13 +13,13 @@ extra_gated_prompt: To access FunctionGemma on Hugging Face, you’re required t
   and agree to Google’s usage license. To do this, please ensure you’re logged in
   to Hugging Face and click below. Requests are processed immediately.
 extra_gated_button_content: Acknowledge license
-base_model: gg-hf-gm/functiongemma-270m-it
 ---
-# mlx-gg-hf/functiongemma-270m-it-8bit
-This model [mlx-gg-hf/functiongemma-270m-it-8bit](https://huggingface.co/mlx-gg-hf/functiongemma-270m-it-8bit) was
-converted to MLX format from [gg-hf-gm/functiongemma-270m-it](https://huggingface.co/gg-hf-gm/functiongemma-270m-it)
 using mlx-lm version **0.28.4**.
 ## Use with mlx
@@ -31,7 +31,7 @@ pip install mlx-lm
 ```python
 from mlx_lm import load, generate
-model, tokenizer = load("mlx-gg-hf/functiongemma-270m-it-8bit")
 prompt = "hello"

   and agree to Google’s usage license. To do this, please ensure you’re logged in
   to Hugging Face and click below. Requests are processed immediately.
 extra_gated_button_content: Acknowledge license
+base_model: google/functiongemma-270m-it
 ---
+# mlx-community/functiongemma-270m-it-8bit
+This model [mlx-community/functiongemma-270m-it-8bit](https://huggingface.co/mlx-community/functiongemma-270m-it-8bit) was
+converted to MLX format from [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it)
 using mlx-lm version **0.28.4**.
 ## Use with mlx
 ```python
 from mlx_lm import load, generate
+model, tokenizer = load("mlx-community/functiongemma-270m-it-8bit")
 prompt = "hello"