Instructions to use HuggingFaceM4/idefics-9b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/idefics-9b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceM4/idefics-9b-instruct")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-9b-instruct")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/idefics-9b-instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/idefics-9b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/idefics-9b-instruct"
# 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-9b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/idefics-9b-instruct

SGLang

How to use HuggingFaceM4/idefics-9b-instruct 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-9b-instruct" \
    --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-9b-instruct",
		"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-9b-instruct" \
        --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-9b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/idefics-9b-instruct with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/idefics-9b-instruct
```

VictorSanh

stas commited on Aug 28, 2023

Commit

57fcad7

1 Parent(s): 53f0739

set the correct vision_config.hidden_act (#2)

Browse files

- set the correct vision_config.hidden_act (8e7a7cf768892e7396a0f096c4326519b290bf51)

Co-authored-by: Stas Bekman <[email protected]>

Files changed (1) hide show

config.json +1 -0

config.json CHANGED Viewed

@@ -36,6 +36,7 @@
   "use_resampler": true,
   "vocab_size": 32000,
   "vision_config": {
     "embed_dim": 1280,
     "image_size": 224,
     "intermediate_size": 5120,

   "use_resampler": true,
   "vocab_size": 32000,
   "vision_config": {
+    "hidden_act": "gelu",
     "embed_dim": 1280,
     "image_size": 224,
     "intermediate_size": 5120,