Instructions to use gnitoahc/ceed-b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gnitoahc/ceed-b2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="gnitoahc/ceed-b2")
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("gnitoahc/ceed-b2")
model = AutoModelForMultimodalLM.from_pretrained("gnitoahc/ceed-b2", 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 gnitoahc/ceed-b2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gnitoahc/ceed-b2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gnitoahc/ceed-b2",
		"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/gnitoahc/ceed-b2

SGLang

How to use gnitoahc/ceed-b2 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 "gnitoahc/ceed-b2" \
    --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": "gnitoahc/ceed-b2",
		"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 "gnitoahc/ceed-b2" \
        --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": "gnitoahc/ceed-b2",
		"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 gnitoahc/ceed-b2 with Docker Model Runner:
```
docker model run hf.co/gnitoahc/ceed-b2
```

ceed-b2

File size: 785 Bytes

184c94d

{
  "produced_by": "scripts/merge_adapter.py",
  "base_model": "google/gemma-4-e4b-it",
  "source_checkpoint": "runs/checkpoints/b2-b7a6479b056a/checkpoint",
  "group_code": "B2",
  "seed": 0,
  "param_efficiency": "lora",
  "config_hash": "575ea8aeee645401277edca0c6555326879f72291a6ac8330f8170dd80b922ee",
  "extraction_fingerprint": "096f4ab01658552ff4198436a3e4809264dcc0ab2dd2c60865b41c6c4b3c6cd9",
  "metrics": {
    "train.final_loss": 3.3668014369904995,
    "train.cross_entropy": 0.9815970882773399,
    "train.kd": 2.385204304009676,
    "train.lora_rank": 4.0,
    "train.epochs": 2.6894523042442917,
    "docvqa": 0.8506125879412143,
    "gqa": 0.6190944881889764,
    "chartqa": 0.5783132530120482,
    "docvqa.n": 565.0,
    "gqa.n": 1016.0,
    "chartqa.n": 249.0
  }
}