Instructions to use HuggingFaceM4/idefics-80b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/idefics-80b with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceM4/idefics-80b")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-80b")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/idefics-80b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/idefics-80b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/idefics-80b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceM4/idefics-80b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/idefics-80b

SGLang

How to use HuggingFaceM4/idefics-80b 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/idefics-80b" \
    --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/idefics-80b",
		"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/idefics-80b" \
        --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/idefics-80b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/idefics-80b with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/idefics-80b
```

davanstrien HF Staff commited on Aug 7, 2023

Commit

875c003

1 Parent(s): 1939e85

update nb link

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -238,7 +238,7 @@ To measure whether IDEFICS demonstrates bias across various protected characteri
 These prompts were run across two datasets [fairface](https://huggingface.co/datasets/HuggingFaceM4/FairFace) and [Stable Bias](yjernite/stable-bias_grounding-images_multimodel_3_12_22).
 The FairFace dataset is "a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labelled with race, gender, and age groups".
-The Stable Bias dataset is a dataset of synthetically generated images from the prompt "A photo portrait of a (ethnicity) (gender) at work.".
 Running the above prompts across both these datasets results in two datasets containing three generated responses for each image alongside information about the ascribed ethnicity and gender of the person depicted in each image.
 This allows for the generated response to each prompt to be compared across  gender and ethnicity axis.
@@ -253,7 +253,7 @@ To surface potential biases in the outputs, we consider the following simple [TF
 With this approach, we can see subtle differences in the frequency of terms across gender and ethnicity. For example, for the prompt related to resumes, we see that synthetic images generated for `non-binary` are more likely to lead to resumes that include **data** or **science** than those generated for `man` or `woman`.
 When looking at the response to the arrest prompt for the FairFace dataset, the term `theft` is more frequently associated with `East Asian`, `Indian`, `Black` and `Southeast Asian` than `White` and `Middle Eastern`.
-Comparing generated responses to the resume prompt by gender across both datasets, we see for FairFace that the terms `financial`, `development`, `product` and `software` appear  more frequently for `man`. For StableBias, the terms `data` and `science` appear more frequently for `non-binary`. This [notebook](https://huggingface.co/spaces/davanstrien/m4-bias/blob/main/m4_bias_eval.ipynb) offers a more comprehensive overview of this evaluation.
 ## Other limitations

 These prompts were run across two datasets [fairface](https://huggingface.co/datasets/HuggingFaceM4/FairFace) and [Stable Bias](yjernite/stable-bias_grounding-images_multimodel_3_12_22).
 The FairFace dataset is "a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labelled with race, gender, and age groups".
+The Stable Bias dataset is a dataset of synthetically generated images from the prompt "A photo portrait of a (ethnicity) (gender) at work".
 Running the above prompts across both these datasets results in two datasets containing three generated responses for each image alongside information about the ascribed ethnicity and gender of the person depicted in each image.
 This allows for the generated response to each prompt to be compared across  gender and ethnicity axis.
 With this approach, we can see subtle differences in the frequency of terms across gender and ethnicity. For example, for the prompt related to resumes, we see that synthetic images generated for `non-binary` are more likely to lead to resumes that include **data** or **science** than those generated for `man` or `woman`.
 When looking at the response to the arrest prompt for the FairFace dataset, the term `theft` is more frequently associated with `East Asian`, `Indian`, `Black` and `Southeast Asian` than `White` and `Middle Eastern`.
+Comparing generated responses to the resume prompt by gender across both datasets, we see for FairFace that the terms `financial`, `development`, `product` and `software` appear  more frequently for `man`. For StableBias, the terms `data` and `science` appear more frequently for `non-binary`. This [notebook](https://huggingface.co/spaces/HuggingFaceM4/m4-bias-eval/blob/main/m4_bias_eval.ipynb) offers a more comprehensive overview of this evaluation.
 ## Other limitations