Instructions to use mlx-community/codegemma-1.1-7b-it-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/codegemma-1.1-7b-it-4bit with Transformers:

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

pipe = pipeline("text-generation", model="mlx-community/codegemma-1.1-7b-it-4bit")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlx-community/codegemma-1.1-7b-it-4bit")
model = AutoModelForCausalLM.from_pretrained("mlx-community/codegemma-1.1-7b-it-4bit", device_map="auto")

MLX

How to use mlx-community/codegemma-1.1-7b-it-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/codegemma-1.1-7b-it-4bit")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use mlx-community/codegemma-1.1-7b-it-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/codegemma-1.1-7b-it-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/codegemma-1.1-7b-it-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mlx-community/codegemma-1.1-7b-it-4bit

SGLang

How to use mlx-community/codegemma-1.1-7b-it-4bit 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 "mlx-community/codegemma-1.1-7b-it-4bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/codegemma-1.1-7b-it-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "mlx-community/codegemma-1.1-7b-it-4bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/codegemma-1.1-7b-it-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use mlx-community/codegemma-1.1-7b-it-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "mlx-community/codegemma-1.1-7b-it-4bit" --prompt "Once upon a time"

Docker Model Runner
How to use mlx-community/codegemma-1.1-7b-it-4bit with Docker Model Runner:
```
docker model run hf.co/mlx-community/codegemma-1.1-7b-it-4bit
```
Atomic Chat

codegemma-1.1-7b-it-4bit / README.md

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---
license: gemma
library_name: transformers
tags:
- mlx
extra_gated_heading: Access CodeGemma on Hugging Face
extra_gated_prompt: To access CodeGemma on Hugging Face, you’re required to review
  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
pipeline_tag: text-generation
widget:
- text: '<start_of_turn>user Write a Python function to calculate the nth fibonacci
    number.<end_of_turn> <start_of_turn>model
    '
inference:
  parameters:
    max_new_tokens: 200
license_link: https://ai.google.dev/gemma/terms
---
# mlx-community/codegemma-1.1-7b-it-4bit
This model was converted to MLX format from [`google/codegemma-1.1-7b-it`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/google/codegemma-1.1-7b-it) for more details on the model.
## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/codegemma-1.1-7b-it-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```

	---
	license: gemma
	library_name: transformers
	tags:
	- mlx
	extra_gated_heading: Access CodeGemma on Hugging Face
	extra_gated_prompt: To access CodeGemma on Hugging Face, you’re required to review
	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
	pipeline_tag: text-generation
	widget:
	- text: '<start_of_turn>user Write a Python function to calculate the nth fibonacci
	number.<end_of_turn> <start_of_turn>model

	'
	inference:
	parameters:
	max_new_tokens: 200
	license_link: https://ai.google.dev/gemma/terms
	---

	# mlx-community/codegemma-1.1-7b-it-4bit
	This model was converted to MLX format from [`google/codegemma-1.1-7b-it`]() using mlx-lm version 0.12.0.
	Refer to the [original model card](https://huggingface.co/google/codegemma-1.1-7b-it) for more details on the model.
	## Use with mlx

	```bash
	pip install mlx-lm
	```

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("mlx-community/codegemma-1.1-7b-it-4bit")
	response = generate(model, tokenizer, prompt="hello", verbose=True)
	```