Instructions to use quickstep3621/dippy-v3-1-11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use quickstep3621/dippy-v3-1-11 with Transformers:

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

pipe = pipeline("text-generation", model="quickstep3621/dippy-v3-1-11")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("quickstep3621/dippy-v3-1-11")
model = AutoModelForImageTextToText.from_pretrained("quickstep3621/dippy-v3-1-11")
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

vLLM

How to use quickstep3621/dippy-v3-1-11 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "quickstep3621/dippy-v3-1-11"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "quickstep3621/dippy-v3-1-11",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/quickstep3621/dippy-v3-1-11

SGLang

How to use quickstep3621/dippy-v3-1-11 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 "quickstep3621/dippy-v3-1-11" \
    --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": "quickstep3621/dippy-v3-1-11",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "quickstep3621/dippy-v3-1-11" \
        --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": "quickstep3621/dippy-v3-1-11",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use quickstep3621/dippy-v3-1-11 with Docker Model Runner:
```
docker model run hf.co/quickstep3621/dippy-v3-1-11
```

dippy-v3-1-11 / config.json

quickstep3621

Duplicate from pruning/sn11-gemma2-27b-v1

2ce5632 verified about 1 year ago

raw

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{"architectures": ["Gemma3ForConditionalGeneration"], "boi_token_index": 255999, "eoi_token_index": 256000, "eos_token_id": [1, 106], "image_token_index": 262144, "initializer_range": 0.02, "mm_tokens_per_image": 256, "model_type": "gemma3", "text_config": {"head_dim": 128, "hidden_size": 5376, "intermediate_size": 21504, "model_type": "gemma3_text", "num_attention_heads": 32, "num_hidden_layers": 62, "num_key_value_heads": 16, "query_pre_attn_scalar": 168, "rope_scaling": {"factor": 8.0, "rope_type": "linear"}, "sliding_window": 1024}, "torch_dtype": "bfloat16", "transformers_version": "4.50.0.dev0", "vision_config": {"hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14, "vision_use_head": false}}