Instructions to use ContentLens-AI/Video-optim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ContentLens-AI/Video-optim with Transformers:

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

pipe = pipeline("image-text-to-text", model="ContentLens-AI/Video-optim")
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("ContentLens-AI/Video-optim")
model = AutoModelForMultimodalLM.from_pretrained("ContentLens-AI/Video-optim", 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 ContentLens-AI/Video-optim with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ContentLens-AI/Video-optim"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ContentLens-AI/Video-optim",
		"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/ContentLens-AI/Video-optim

SGLang

How to use ContentLens-AI/Video-optim 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 "ContentLens-AI/Video-optim" \
    --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": "ContentLens-AI/Video-optim",
		"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 "ContentLens-AI/Video-optim" \
        --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": "ContentLens-AI/Video-optim",
		"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 ContentLens-AI/Video-optim with Docker Model Runner:
```
docker model run hf.co/ContentLens-AI/Video-optim
```

Parveshiiii commited on May 28

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config.json +184 -0
model.safetensors.index.json +0 -0
recipe.yaml +43 -0

config.json CHANGED Viewed

@@ -4,6 +4,190 @@
     ],
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         "attention_bias": false,
         "attention_dropout": 0.0,

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     "model_type": "qwen3_5",
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     "text_config": {
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model.safetensors.index.json CHANGED Viewed

The diff for this file is too large to render. See raw diff

recipe.yaml ADDED Viewed

	@@ -0,0 +1,43 @@

+default_stage:
+  default_modifiers:
+    AWQModifier:
+      config_groups:
+        group_1:
+          targets: [Linear]
+          weights:
+            num_bits: 4
+            type: int
+            symmetric: true
+            group_size: 32
+            strategy: group
+            block_structure: null
+            dynamic: false
+            actorder: null
+            scale_dtype: null
+            zp_dtype: null
+            observer: mse
+            observer_kwargs: {}
+          input_activations: null
+          output_activations: null
+          format: null
+      targets: [Linear]
+      ignore: ['re:.*embed_tokens', 're:.*linear_attn.*', 're:model[.]visual.*', 're:mtp.*',
+        lm_head]
+      bypass_divisibility_checks: false
+      mappings:
+      - smooth_layer: re:model.*layers[.](3|7|11|15|19|23|27|31)[.]input_layernorm
+        balance_layers: ['re:model.*layers[.](3|7|11|15|19|23|27|31)[.]self_attn[.]q_proj',
+          're:model.*layers[.](3|7|11|15|19|23|27|31)[.]self_attn[.]k_proj', 're:model.*layers[.](3|7|11|15|19|23|27|31)[.]self_attn[.]v_proj']
+        activation_hook_target: null
+        balance_exponent: 1
+      - smooth_layer: re:model.*layers[.](3|7|11|15|19|23|27|31)[.]self_attn[.]v_proj
+        balance_layers: ['re:model.*layers[.](3|7|11|15|19|23|27|31)[.]self_attn[.]o_proj']
+        activation_hook_target: null
+        balance_exponent: 1
+      - smooth_layer: re:model.*post_attention_layernorm
+        balance_layers: ['re:model.*mlp[.]gate_proj', 're:model.*mlp[.]up_proj']
+        activation_hook_target: null
+        balance_exponent: 1
+      offload_device: !!python/object/apply:torch.device [cpu]
+      duo_scaling: true
+      n_grid: 20