Instructions to use optimum-intel-internal-testing/tiny-random-molmo2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use optimum-intel-internal-testing/tiny-random-molmo2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="optimum-intel-internal-testing/tiny-random-molmo2", 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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("optimum-intel-internal-testing/tiny-random-molmo2", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("optimum-intel-internal-testing/tiny-random-molmo2", trust_remote_code=True, device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use optimum-intel-internal-testing/tiny-random-molmo2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "optimum-intel-internal-testing/tiny-random-molmo2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "optimum-intel-internal-testing/tiny-random-molmo2",
		"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/optimum-intel-internal-testing/tiny-random-molmo2

SGLang

How to use optimum-intel-internal-testing/tiny-random-molmo2 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 "optimum-intel-internal-testing/tiny-random-molmo2" \
    --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": "optimum-intel-internal-testing/tiny-random-molmo2",
		"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 "optimum-intel-internal-testing/tiny-random-molmo2" \
        --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": "optimum-intel-internal-testing/tiny-random-molmo2",
		"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 optimum-intel-internal-testing/tiny-random-molmo2 with Docker Model Runner:
```
docker model run hf.co/optimum-intel-internal-testing/tiny-random-molmo2
```

tiny-random-molmo2

File size: 2,522 Bytes

ae23feb

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  "adapter_config": {
    "attention_dropout": 0.0,
    "attn_implementation": "sdpa",
    "float32_attention": true,
    "head_dim": 32,
    "hidden_act": "silu",
    "hidden_size": 64,
    "image_feature_dropout": 0.0,
    "initializer_range": 0.02,
    "intermediate_size": 128,
    "model_type": "molmo2",
    "num_attention_heads": 2,
    "num_key_value_heads": 2,
    "pooling_attention_mask": true,
    "residual_dropout": 0.0,
    "text_hidden_size": 128,
    "vit_layers": [
      -1,
      -2
    ]
  },
  "architectures": [
    "Molmo2ForConditionalGeneration"
  ],
  "auto_map": {
    "AutoConfig": "configuration_molmo2.Molmo2Config",
    "AutoModelForImageTextToText": "modeling_molmo2.Molmo2ForConditionalGeneration"
  },
  "dtype": "float32",
  "frame_end_token_id": 151944,
  "frame_start_token_id": 151943,
  "image_col_id": 151939,
  "image_end_token_id": 151937,
  "image_high_res_id": 151938,
  "image_low_res_id": 151942,
  "image_patch_id": 151938,
  "image_start_token_id": 151936,
  "initializer_range": 0.02,
  "low_res_image_start_token_id": 151940,
  "model_type": "molmo2",
  "text_config": {
    "additional_vocab_size": 128,
    "attention_dropout": 0.0,
    "attn_implementation": "sdpa",
    "embedding_dropout": 0.0,
    "head_dim": 32,
    "hidden_act": "silu",
    "hidden_size": 128,
    "initializer_range": 0.02,
    "intermediate_size": 256,
    "layer_norm_eps": 1e-06,
    "max_position_embeddings": 10240,
    "model_type": "molmo2_text",
    "norm_after": false,
    "num_attention_heads": 4,
    "num_hidden_layers": 3,
    "num_key_value_heads": 2,
    "qk_norm_type": "qwen3",
    "qkv_bias": false,
    "residual_dropout": 0.0,
    "rope_scaling": null,
    "rope_scaling_layers": null,
    "rope_theta": 1000000.0,
    "use_cache": true,
    "use_qk_norm": true,
    "vocab_size": 151936
  },
  "tie_word_embeddings": false,
  "transformers_version": "4.57.6",
  "use_cache": true,
  "use_frame_special_tokens": false,
  "vit_config": {
    "attention_dropout": 0.0,
    "attn_implementation": "sdpa",
    "float32_attention": true,
    "head_dim": 32,
    "hidden_act": "gelu_pytorch_tanh",
    "hidden_size": 64,
    "image_default_input_size": [
      378,
      378
    ],
    "image_num_pos": 729,
    "image_patch_size": 14,
    "initializer_range": 0.02,
    "intermediate_size": 128,
    "layer_norm_eps": 1e-06,
    "model_type": "molmo2",
    "num_attention_heads": 2,
    "num_hidden_layers": 3,
    "num_key_value_heads": 2,
    "residual_dropout": 0.0
  }
}