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

Libraries

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

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

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

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b-base")
model = AutoModelForMultimodalLM.from_pretrained("HuggingFaceM4/idefics2-8b-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/idefics2-8b-base"
# 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-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

VictorSanh commited on Apr 13, 2024

Commit

65215b0

verified ·

1 Parent(s): dff8ac8

`eos_token_id`

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -158,7 +158,7 @@ inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
 # Generate
-generated_ids = model.generate(**inputs, bad_words_ids=BAD_WORDS_IDS, max_new_tokens=500)
 generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
 print(generated_texts)
@@ -209,7 +209,7 @@ inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
 # Generate
-generated_ids = model.generate(**inputs, bad_words_ids=BAD_WORDS_IDS, max_new_tokens=500)
 generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
 print(generated_texts)

 # Generate
+generated_ids = model.generate(**inputs, bad_words_ids=BAD_WORDS_IDS, eos_token_id=EOS_WORDS_IDS, max_new_tokens=500)
 generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
 print(generated_texts)
 # Generate
+generated_ids = model.generate(**inputs, bad_words_ids=BAD_WORDS_IDS, eos_token_id=EOS_WORDS_IDS, max_new_tokens=500)
 generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
 print(generated_texts)