Instructions to use huz-relay/idefics2-8b-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use huz-relay/idefics2-8b-ocr with Transformers:

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

pipe = pipeline("image-text-to-text", model="huz-relay/idefics2-8b-ocr")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("huz-relay/idefics2-8b-ocr")
model = AutoModelForMultimodalLM.from_pretrained("huz-relay/idefics2-8b-ocr", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use huz-relay/idefics2-8b-ocr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "huz-relay/idefics2-8b-ocr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huz-relay/idefics2-8b-ocr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/huz-relay/idefics2-8b-ocr

SGLang

How to use huz-relay/idefics2-8b-ocr 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 "huz-relay/idefics2-8b-ocr" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huz-relay/idefics2-8b-ocr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "huz-relay/idefics2-8b-ocr" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huz-relay/idefics2-8b-ocr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use huz-relay/idefics2-8b-ocr with Docker Model Runner:
```
docker model run hf.co/huz-relay/idefics2-8b-ocr
```

idefics2-8b-ocr / handler.py

huz-relay

Add user input

1b88ea1 over 2 years ago

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from typing import Any, Dict, List
from transformers import Idefics2Processor, Idefics2ForConditionalGeneration
import torch
import logging
from PIL import Image
import requests


class EndpointHandler:
    def __init__(self, path=""):
        # Preload all the elements you are going to need at inference.
        self.logger = logging.getLogger()
        self.logger.addHandler(logging.StreamHandler())
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.processor = Idefics2Processor.from_pretrained(path)
        self.model = Idefics2ForConditionalGeneration.from_pretrained(path)
        self.model.to(self.device)
        self.logger.info("Initialisation finished!")

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
         data args:
              inputs (:obj: `str` | `PIL.Image` | `np.array`)
              kwargs
        Return:
              A :obj:`list` | `dict`: will be serialized and returned
        """
        """image = data.pop("inputs", data)
        self.logger.info("image")
        # process image
        inputs = self.processor(images=image, return_tensors="pt").to(self.device)
        self.logger.info("inputs")
        self.logger.info(f"{inputs.input_ids}")
        generated_ids = self.model.generate(**inputs)
        self.logger.info("generated")
        # run prediction
        generated_text = self.processor.batch_decode(
            generated_ids, skip_special_tokens=True
        )
        self.logger.info("decoded")"""

        url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg"

        image_1 = data.pop("inputs", data)
        image_2 = Image.open(requests.get(url_2, stream=True).raw)
        images = [image_1, image_2]

        messages = [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "What’s the difference between these two images?",
                    },
                    {"type": "image"},
                    {"type": "image"},
                ],
            }
        ]

        self.model.to(self.device)

        # at inference time, one needs to pass `add_generation_prompt=True` in order to make sure the model completes the prompt
        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
        self.logger.info(text)
        # 'User: What’s the difference between these two images?<image><image><end_of_utterance>\nAssistant:'

        inputs = self.processor(images=images, text=text, return_tensors="pt").to(
            self.device
        )
        self.logger.info("inputs")

        generated_text = self.model.generate(**inputs, max_new_tokens=500)
        self.logger.info("generated")
        generated_text = self.processor.batch_decode(
            generated_text, skip_special_tokens=True
        )[0]
        self.logger.info(f"Generated text: {generated_text}")

        # decode output
        return generated_text

	from typing import Any, Dict, List
	from transformers import Idefics2Processor, Idefics2ForConditionalGeneration
	import torch
	import logging
	from PIL import Image
	import requests


	class EndpointHandler:
	def __init__(self, path=""):
	# Preload all the elements you are going to need at inference.
	self.logger = logging.getLogger()
	self.logger.addHandler(logging.StreamHandler())
	self.device = "cuda" if torch.cuda.is_available() else "cpu"
	self.processor = Idefics2Processor.from_pretrained(path)
	self.model = Idefics2ForConditionalGeneration.from_pretrained(path)
	self.model.to(self.device)
	self.logger.info("Initialisation finished!")

	def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
	"""
	data args:
	inputs (:obj: `str` \| `PIL.Image` \| `np.array`)
	kwargs
	Return:
	A :obj:`list` \| `dict`: will be serialized and returned
	"""
	"""image = data.pop("inputs", data)
	self.logger.info("image")

	# process image
	inputs = self.processor(images=image, return_tensors="pt").to(self.device)
	self.logger.info("inputs")
	self.logger.info(f"{inputs.input_ids}")
	generated_ids = self.model.generate(**inputs)
	self.logger.info("generated")

	# run prediction
	generated_text = self.processor.batch_decode(
	generated_ids, skip_special_tokens=True
	)
	self.logger.info("decoded")"""

	url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg"

	image_1 = data.pop("inputs", data)
	image_2 = Image.open(requests.get(url_2, stream=True).raw)
	images = [image_1, image_2]

	messages = [
	{
	"role": "user",
	"content": [
	{
	"type": "text",
	"text": "What’s the difference between these two images?",
	},
	{"type": "image"},
	{"type": "image"},
	],
	}
	]

	self.model.to(self.device)

	# at inference time, one needs to pass `add_generation_prompt=True` in order to make sure the model completes the prompt
	text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
	self.logger.info(text)
	# 'User: What’s the difference between these two images?<image><image><end_of_utterance>\nAssistant:'

	inputs = self.processor(images=images, text=text, return_tensors="pt").to(
	self.device
	)
	self.logger.info("inputs")

	generated_text = self.model.generate(**inputs, max_new_tokens=500)
	self.logger.info("generated")
	generated_text = self.processor.batch_decode(
	generated_text, skip_special_tokens=True
	)[0]
	self.logger.info(f"Generated text: {generated_text}")

	# decode output
	return generated_text