Instructions to use techwarriors/Molmo2-O-7B-4bit-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use techwarriors/Molmo2-O-7B-4bit-GPTQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="techwarriors/Molmo2-O-7B-4bit-GPTQ", 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 AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("techwarriors/Molmo2-O-7B-4bit-GPTQ", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use techwarriors/Molmo2-O-7B-4bit-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "techwarriors/Molmo2-O-7B-4bit-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "techwarriors/Molmo2-O-7B-4bit-GPTQ",
		"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/techwarriors/Molmo2-O-7B-4bit-GPTQ

SGLang

How to use techwarriors/Molmo2-O-7B-4bit-GPTQ 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 "techwarriors/Molmo2-O-7B-4bit-GPTQ" \
    --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": "techwarriors/Molmo2-O-7B-4bit-GPTQ",
		"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 "techwarriors/Molmo2-O-7B-4bit-GPTQ" \
        --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": "techwarriors/Molmo2-O-7B-4bit-GPTQ",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use techwarriors/Molmo2-O-7B-4bit-GPTQ with Docker Model Runner:
```
docker model run hf.co/techwarriors/Molmo2-O-7B-4bit-GPTQ
```

Molmo2-O-7B-4bit-GPTQ

This is a selectively quantized 4-bit version of allenai/Molmo2-O-7B.

Text Backbone: Quantized to 4-bit (W4A16) using GPTQ (llmcompressor).
Vision Backbone: Kept strictly at 16-bit to preserve vision capabilities.
Format: compressed-tensors
VRAM Required: ~5.1 GB

Usage with vLLM / SGLang

This model is packaged in the unified compressed-tensors format. Ensure you have trust_remote_code=True enabled when loading!

Downloads last month: 6

Safetensors

Model size

2B params

Tensor type

I64

F32

I32

Model tree for techwarriors/Molmo2-O-7B-4bit-GPTQ

Base model

allenai/Olmo-3-1025-7B

Finetuned

allenai/Olmo-3-7B-Instruct-SFT

Finetuned

allenai/Olmo-3-7B-Instruct-DPO

Finetuned

allenai/Olmo-3-7B-Instruct

Finetuned

allenai/Molmo2-O-7B

Quantized

(1)

this model

techwarriors
/

Molmo2-O-7B-4bit-GPTQ

Molmo2-O-7B-4bit-GPTQ

Usage with vLLM / SGLang

Model tree for techwarriors/Molmo2-O-7B-4bit-GPTQ

Dataset used to train techwarriors/Molmo2-O-7B-4bit-GPTQ