Instructions to use KRLabsOrg/squeez-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KRLabsOrg/squeez-2b with Transformers:

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

pipe = pipeline("text-generation", model="KRLabsOrg/squeez-2b")
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("KRLabsOrg/squeez-2b")
model = AutoModelForImageTextToText.from_pretrained("KRLabsOrg/squeez-2b")
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 KRLabsOrg/squeez-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KRLabsOrg/squeez-2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KRLabsOrg/squeez-2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KRLabsOrg/squeez-2b

SGLang

How to use KRLabsOrg/squeez-2b 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 "KRLabsOrg/squeez-2b" \
    --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": "KRLabsOrg/squeez-2b",
		"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 "KRLabsOrg/squeez-2b" \
        --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": "KRLabsOrg/squeez-2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KRLabsOrg/squeez-2b with Docker Model Runner:
```
docker model run hf.co/KRLabsOrg/squeez-2b
```

Add library_name to metadata

by nielsr HF Staff - opened Apr 12

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+21

-12

nielsr

Apr 12

Hi! I'm Niels from the community science team at Hugging Face.

I've opened this PR to add library_name: transformers to the model card metadata. This is based on the usage examples provided in your README, which demonstrate how to load and use the model using the Transformers library. Adding this metadata helps the Hugging Face Hub categorize the model correctly and enables the "Use in Transformers" button on the model page.

Add library_name to metadata07c66421

adaamko changed pull request status to merged Apr 13

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