Instructions to use Xkev/Llama-3.2V-11B-cot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xkev/Llama-3.2V-11B-cot with Transformers:

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

pipe = pipeline("image-text-to-text", model="Xkev/Llama-3.2V-11B-cot")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Xkev/Llama-3.2V-11B-cot")
model = AutoModelForMultimodalLM.from_pretrained("Xkev/Llama-3.2V-11B-cot", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Xkev/Llama-3.2V-11B-cot with vLLM:

Install from pip and serve model

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

SGLang

How to use Xkev/Llama-3.2V-11B-cot 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 "Xkev/Llama-3.2V-11B-cot" \
    --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": "Xkev/Llama-3.2V-11B-cot",
		"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 "Xkev/Llama-3.2V-11B-cot" \
        --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": "Xkev/Llama-3.2V-11B-cot",
		"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 Xkev/Llama-3.2V-11B-cot with Docker Model Runner:
```
docker model run hf.co/Xkev/Llama-3.2V-11B-cot
```

Xkev

sabato-nocera commited on Nov 16, 2025

Commit

7ee7328

verified ·

1 Parent(s): dfdcec1

add AIBOM (#14)

Browse files

- add AIBOM (67b45bb0284faed40d1e022b273d04e9c9d98035)

Co-authored-by: Sabato Nocera <sabato-nocera@users.noreply.huggingface.co>

Files changed (1) hide show

Xkev_Llama-3.2V-11B-cot.json +123 -0

Xkev_Llama-3.2V-11B-cot.json ADDED Viewed

	@@ -0,0 +1,123 @@

+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:4ec612d6-0e01-4883-8931-409c560fd885",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-07-21T08:19:51.846071+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "Xkev/Llama-3.2V-11B-cot-9618fe23-9615-538b-9533-c062d681ea6b",
+            "licenses": [
+                {
+                    "license": {
+                        "id": "Apache-2.0",
+                        "url": "https://spdx.org/licenses/Apache-2.0.html"
+                    }
+                }
+            ],
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/Xkev/Llama-3.2V-11B-cot",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "datasets": [
+                        {
+                            "ref": "Xkev/LLaVA-CoT-100k-c9c723f9-0fc7-5fcc-a916-d80870eaabe0"
+                        }
+                    ],
+                    "task": "image-text-to-text",
+                    "architectureFamily": "mllama",
+                    "modelArchitecture": "MllamaForConditionalGeneration"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    },
+                    {
+                        "name": "base_model",
+                        "value": "meta-llama/Llama-3.2-11B-Vision-Instruct"
+                    }
+                ]
+            },
+            "name": "Xkev/Llama-3.2V-11B-cot",
+            "authors": [
+                {
+                    "name": "Xkev"
+                }
+            ],
+            "description": "<!-- Provide a longer summary of what this model is. -->- **License:** apache-2.0- **Finetuned from model:** meta-llama/Llama-3.2-11B-Vision-Instruct",
+            "tags": [
+                "transformers",
+                "safetensors",
+                "mllama",
+                "image-to-text",
+                "image-text-to-text",
+                "conversational",
+                "en",
+                "dataset:Xkev/LLaVA-CoT-100k",
+                "arxiv:2411.10440",
+                "base_model:meta-llama/Llama-3.2-11B-Vision-Instruct",
+                "base_model:finetune:meta-llama/Llama-3.2-11B-Vision-Instruct",
+                "license:apache-2.0",
+                "text-generation-inference",
+                "endpoints_compatible",
+                "region:us"
+            ]
+        }
+    },
+    "components": [
+        {
+            "type": "data",
+            "bom-ref": "Xkev/LLaVA-CoT-100k-c9c723f9-0fc7-5fcc-a916-d80870eaabe0",
+            "name": "Xkev/LLaVA-CoT-100k",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "Xkev/LLaVA-CoT-100k-c9c723f9-0fc7-5fcc-a916-d80870eaabe0",
+                    "name": "Xkev/LLaVA-CoT-100k",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/Xkev/LLaVA-CoT-100k",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "visual-question-answering"
+                            },
+                            {
+                                "name": "language",
+                                "value": "en"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "10K<n<100K"
+                            },
+                            {
+                                "name": "pretty_name",
+                                "value": "LLaVA-CoT"
+                            },
+                            {
+                                "name": "license",
+                                "value": "apache-2.0"
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\tDataset Card for LLaVA-CoT\n\t\n\n\n\t\n\t\t\n\t\tDataset Sources\n\t\n\n\nRepository: [https://github.com/PKU-YuanGroup/LLaVA-CoT]\nPaper: [https://arxiv.org/abs/2411.10440]\n\n\n\t\n\t\t\n\t\tDataset Structure\n\t\n\nThe repository includes image.zip.part-{aa-ap}, which you need to merge manually. Use the following command to combine them:\ncat image.zip.part-* > image.zip\nunzip image.zip\n\nThe train.jsonl file contains the question-answering data in the following format:\n{\n\"id\": ID,\n\"image\": IMAGE_PATH\u2026 See the full description on the dataset page: https://huggingface.co/datasets/Xkev/LLaVA-CoT-100k.",
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "Xkev",
+                                    "url": "https://huggingface.co/Xkev"
+                                }
+                            }
+                        ]
+                    }
+                }
+            ]
+        }
+    ]
+}