Instructions to use HuggingFaceM4/idefics2-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/idefics2-8b with Transformers:

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

pipe = pipeline("image-text-to-text", model="HuggingFaceM4/idefics2-8b")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/idefics2-8b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/idefics2-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/idefics2-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceM4/idefics2-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/idefics2-8b

SGLang

How to use HuggingFaceM4/idefics2-8b 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 "HuggingFaceM4/idefics2-8b" \
    --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": "HuggingFaceM4/idefics2-8b",
		"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 "HuggingFaceM4/idefics2-8b" \
        --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": "HuggingFaceM4/idefics2-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/idefics2-8b with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/idefics2-8b
```

Few-shot in-context learning

#72

by andrewliao11-nv - opened Jun 29, 2024

Discussion

andrewliao11-nv

Jun 29, 2024

Does anyone has success perform few-shot in-context learning using idefics2-8b?

I have experimented in the following setting:

Task: image binary classification
Domain: iNaturist subset

Prompt example

You are tasked to answer the user question based on the image. Here are 2 demonstrations to help you answer the user question:

[image] The above image is a cat
[image] The above image is an elephant, not a cat

---

[image] User question: Does the above image contain a cat?
Answer with a single word. Yes or No.

The experiments show that 0-shot performances (>90) is way better than 2,4,8-shot performances (max around 82)

HugoLaurencon

Jul 4, 2024

•

edited Jul 4, 2024

It's normal if you're using the sft one (idefics2-8b) since it was fine-tuned only on Q/A pairs (with a 0 shot setting).
The more you add turns and images, the worse it gets.

Also, I think your template is not ideal for that and you can boost the performance with the official chat template (see the model card).
By doing the ICL wih multiple turns with the official chat template, I expect you can improve the performance compared that what you have currently

jafw9e84joifsdjo

Jul 12, 2024

I couldn't seem to find the official chat template in the tokenizer config.

I did my best to re-create what was detailed in the model card and added the chat_template to the tokenizer_config.json in this PR: https://huggingface.co/HuggingFaceM4/idefics2-8b/discussions/74

Kamichanw

Nov 1, 2024

You should use idefics2-8b-base instead of idefics2-8b.

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