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

Libraries

How to use HuggingFaceM4/tiny-random-idefics with Transformers:

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

pipe = pipeline("image-text-to-text", model="HuggingFaceM4/tiny-random-idefics")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/tiny-random-idefics")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/tiny-random-idefics")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/tiny-random-idefics with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HuggingFaceM4/tiny-random-idefics

SGLang

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

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

stas commited on Jul 26, 2023

Commit

884e26e

1 Parent(s): fbbdd26

add image_num_channels

Browse files

Files changed (2) hide show

config.json +1 -1
preprocessor_config.json +1 -0

config.json CHANGED Viewed

@@ -21,7 +21,7 @@
   "hidden_size": 16,
   "initializer_range": 0.02,
   "intermediate_size": 11008,
-  "max_new_tokens": 100,
   "max_position_embeddings": 128,
   "model_type": "idefics",
   "num_attention_heads": 4,

   "hidden_size": 16,
   "initializer_range": 0.02,
   "intermediate_size": 11008,
+  "max_new_tokens": 128,
   "max_position_embeddings": 128,
   "model_type": "idefics",
   "num_attention_heads": 4,

preprocessor_config.json CHANGED Viewed

@@ -1,4 +1,5 @@
 {
   "image_mean": [
     0.48145466,
     0.4578275,

 {
+  "image_num_channels": 3,
   "image_mean": [
     0.48145466,
     0.4578275,