Instructions to use Syed-Hasan-8503/Idefics2-8B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Syed-Hasan-8503/Idefics2-8B-SFT with Transformers:

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

pipe = pipeline("image-text-to-text", model="Syed-Hasan-8503/Idefics2-8B-SFT")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Syed-Hasan-8503/Idefics2-8B-SFT")
model = AutoModelForMultimodalLM.from_pretrained("Syed-Hasan-8503/Idefics2-8B-SFT", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Syed-Hasan-8503/Idefics2-8B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Syed-Hasan-8503/Idefics2-8B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Syed-Hasan-8503/Idefics2-8B-SFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Syed-Hasan-8503/Idefics2-8B-SFT

SGLang

How to use Syed-Hasan-8503/Idefics2-8B-SFT 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 "Syed-Hasan-8503/Idefics2-8B-SFT" \
    --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": "Syed-Hasan-8503/Idefics2-8B-SFT",
		"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 "Syed-Hasan-8503/Idefics2-8B-SFT" \
        --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": "Syed-Hasan-8503/Idefics2-8B-SFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Syed-Hasan-8503/Idefics2-8B-SFT with Docker Model Runner:
```
docker model run hf.co/Syed-Hasan-8503/Idefics2-8B-SFT
```

Idefics2-8B-SFT

Idefics2-8B-SFT is SFT fine-tune of HuggingFaceM4/idefics2-8b on 35k TextVQA dataset. Training was performed on RTX A5000 for 10 hrs. Wandb report:

This fine-tuned model achieves a Levenshtein score of 82.29%.

Model Summary

Developed by: Hugging Face
Model type: Multi-modal model (image+text)
Language(s) (NLP): en
License: Apache 2.0
Parent Models: google/siglip-so400m-patch14-384 and mistralai/Mistral-7B-v0.1

💻 Usage

processor = AutoProcessor.from_pretrained("Syed-Hasan-8503/Idefics2-8B-SFT")
model = AutoModelForVision2Seq.from_pretrained("Syed-Hasan-8503/Idefics2-8B-SFT",).to(DEVICE)

# Create inputs
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "What do we see in this image?"},
        ]
    },
    {
        "role": "assistant",
        "content": [
            {"type": "text", "text": "In this image, we can see the city of New York, and more specifically the Statue of Liberty."},
        ]
    },
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "And how about this image?"},
        ]
    },       
]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=[image1, image2], return_tensors="pt")
inputs = {k: v.to(DEVICE) for k, v in inputs.items()}


# Generate
generated_ids = model.generate(**inputs, max_new_tokens=500)
generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)

print(generated_texts)
# ['User: What do we see in this image? \nAssistant: In this image, we can see the city of New York, and more specifically the Statue of Liberty. \nUser: And how about this image? \nAssistant: In this image we can see buildings, trees, lights, water and sky.']

🏆 Evaluation

Coming Soon!

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Safetensors

Model size

8B params

Tensor type

F16

Inference Providers NEW

Image-Text-to-Text

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Syed-Hasan-8503
/

Idefics2-8B-SFT

Idefics2-8B-SFT

Model Summary

💻 Usage

🏆 Evaluation

Dataset used to train Syed-Hasan-8503/Idefics2-8B-SFT

Collection including Syed-Hasan-8503/Idefics2-8B-SFT

Vision LLMs