- Libraries
- MLX
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine with MLX:
# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
# Load the model
model, processor = load("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine")
config = load_config("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine")
# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."
# Apply chat template
formatted_prompt = apply_chat_template(
processor, config, prompt, num_images=1
)
# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output) - Transformers
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine")
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
{"type": "text", "text": "What animal is on the candy?"}
]
},
]
pipe(text=messages) # Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine")
model = AutoModelForMultimodalLM.from_pretrained("zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine")
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
{"type": "text", "text": "What animal is on the candy?"}
]
},
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
]
}'Use Docker
docker model run hf.co/zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine
- SGLang
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine 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 "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine" \
--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": "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
]
}'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 "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine" \
--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": "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
]
}' - Unsloth Studio
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine",
max_seq_length=2048,
) - Pi
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine with Pi:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine"
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": "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Hermes Agent new
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine 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 "zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine"
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 zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine
Run Hermes
hermes
- Docker Model Runner
How to use zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine with Docker Model Runner:
docker model run hf.co/zecanard/gemma-4-31B-it-Claude-Opus-Distilled-v2-MLX-3bit-int3-affine
