Instructions to use mlx-community/InternVL3-1B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/InternVL3-1B-4bit with Transformers:

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

pipe = pipeline("image-text-to-text", model="mlx-community/InternVL3-1B-4bit", trust_remote_code=True)
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 AutoModel
model = AutoModel.from_pretrained("mlx-community/InternVL3-1B-4bit", trust_remote_code=True, dtype="auto")

MLX

How to use mlx-community/InternVL3-1B-4bit 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("mlx-community/InternVL3-1B-4bit")
config = load_config("mlx-community/InternVL3-1B-4bit")

# 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)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use mlx-community/InternVL3-1B-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/InternVL3-1B-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/InternVL3-1B-4bit",
		"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/mlx-community/InternVL3-1B-4bit

SGLang

How to use mlx-community/InternVL3-1B-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/InternVL3-1B-4bit" \
    --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": "mlx-community/InternVL3-1B-4bit",
		"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 "mlx-community/InternVL3-1B-4bit" \
        --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": "mlx-community/InternVL3-1B-4bit",
		"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"
						}
					}
				]
			}
		]
	}'

How to use mlx-community/InternVL3-1B-4bit 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/InternVL3-1B-4bit"

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/InternVL3-1B-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/InternVL3-1B-4bit 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/InternVL3-1B-4bit"

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/InternVL3-1B-4bit

Run Hermes

hermes

Docker Model Runner
How to use mlx-community/InternVL3-1B-4bit with Docker Model Runner:
```
docker model run hf.co/mlx-community/InternVL3-1B-4bit
```

Improve language tag

by lbourdois - opened Apr 27, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+44

-32

Files changed (1) hide show

README.md +44 -32

README.md CHANGED Viewed

@@ -1,32 +1,44 @@
----
-license: other
-license_name: qwen
-license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
-pipeline_tag: image-text-to-text
-library_name: transformers
-base_model:
-- OpenGVLab/InternViT-300M-448px-V2_5
-- Qwen/Qwen2.5-0.5B
-base_model_relation: merge
-datasets:
-- OpenGVLab/MMPR-v1.2
-language:
-- multilingual
-tags:
-- internvl
-- custom_code
-- mlx
----
-# mlx-community/InternVL3-1B-4bit
-This model was converted to MLX format from [`InternVL3-1B`]() using mlx-vlm version **0.1.22**.
-Refer to the [original model card](https://huggingface.co/InternVL3-1B) for more details on the model.
-## Use with mlx
-```bash
-pip install -U mlx-vlm
-```
-```bash
-python -m mlx_vlm.generate --model mlx-community/InternVL3-1B-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
-```

+---
+license: other
+license_name: qwen
+license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
+pipeline_tag: image-text-to-text
+library_name: transformers
+base_model:
+- OpenGVLab/InternViT-300M-448px-V2_5
+- Qwen/Qwen2.5-0.5B
+base_model_relation: merge
+datasets:
+- OpenGVLab/MMPR-v1.2
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+tags:
+- internvl
+- custom_code
+- mlx
+---
+# mlx-community/InternVL3-1B-4bit
+This model was converted to MLX format from [`InternVL3-1B`]() using mlx-vlm version **0.1.22**.
+Refer to the [original model card](https://huggingface.co/InternVL3-1B) for more details on the model.
+## Use with mlx
+```bash
+pip install -U mlx-vlm
+```
+```bash
+python -m mlx_vlm.generate --model mlx-community/InternVL3-1B-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
+```