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

eeef13a

1 Parent(s): 972b33c

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@@ -206,17 +206,17 @@ The training software is built on top of HuggingFace Transformers + Accelerate,
 Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
 As a derivative of such a language model, IDEFICS can produce texts that include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
-Moreover, IDEFICS can produce factually incorrect texts, and should not be relied on to produce factually accurate information.
-Here are a few examples of outputs that could be categorized as factually incorrect, biased, or offensive:
-TODO: give 4/5 representative examples
 When prompted with a misleading image, the model's generations offer factually incorrect information. For example, the prompt:
-```"Who is the 46th President of the United States of America?" + and image of Donald Trump```
 Returns: `The 46th President of the United States of America is Donald Trump.`.
 ## Bias Evaluation

 Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
 As a derivative of such a language model, IDEFICS can produce texts that include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+Moreover, IDEFICS can produce factually incorrect texts and should not be relied on to produce factually accurate information.
+Here are a few examples of outputs that could be categorized as factually incorrect, biased, or offensive:
 When prompted with a misleading image, the model's generations offer factually incorrect information. For example, the prompt:
+```"Who is the 46th President of the United States of America?" + an image of Donald Trump```
 Returns: `The 46th President of the United States of America is Donald Trump.`.
+The model will offer a response when prompted with medical images, for example, an X-ray, and asked for a diagnosis. This behaviour occurs both with specific prompts i.e. does this image show X disease and asked for a generic diagnosis i.e. what disease does this image show.
 ## Bias Evaluation