Instructions to use SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4")
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("SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4")
model = AutoModelForMultimodalLM.from_pretrained("SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4", 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 SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4",
		"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/SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4

SGLang

How to use SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4 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 "SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4" \
    --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": "SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4",
		"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 "SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4" \
        --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": "SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4",
		"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 SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4 with Docker Model Runner:
```
docker model run hf.co/SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4
```

Converted from meta-llama/Llama-3.2-11B-Vision-Instruct using BitsAndBytes with NF4 (4-bit) quantization. Not using double quantization. Requires bitsandbytes to load.

Example usage for image captioning:

from transformers import MllamaForConditionalGeneration, AutoProcessor, BitsAndBytesConfig
from PIL import Image
import time

# Load model
model_id = "SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4"
model = MllamaForConditionalGeneration.from_pretrained(
    model_id,
    use_safetensors=True,
    device_map="cuda:0"
)
# Load tokenizer
processor = AutoProcessor.from_pretrained(model_id)

# Caption a local image (could use a more specific prompt)
IMAGE = Image.open("test.png").convert("RGB")
PROMPT = """<|begin_of_text|><|start_header_id|>user<|end_header_id|>
Caption this image:
<|image|><|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""

inputs = processor(IMAGE, PROMPT, return_tensors="pt").to(model.device)
prompt_tokens = len(inputs['input_ids'][0])
print(f"Prompt tokens: {prompt_tokens}")

t0 = time.time()
generate_ids = model.generate(**inputs, max_new_tokens=256)
t1 = time.time()
total_time = t1 - t0
generated_tokens = len(generate_ids[0]) - prompt_tokens
time_per_token = generated_tokens/total_time
print(f"Generated {generated_tokens} tokens in {total_time:.3f} s ({time_per_token:.3f} tok/s)")

output = processor.decode(generate_ids[0][prompt_tokens:]).replace('<|eot_id|>', '')
print(output)

You can get a set of ComfyUI custom nodes for running this model here: https://github.com/SeanScripts/ComfyUI-PixtralLlamaVision

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Safetensors

Model size

11B params

Tensor type

F32

F16

Model tree for SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4

Base model

meta-llama/Llama-3.2-11B-Vision-Instruct

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(20)

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