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

Libraries

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

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

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

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

stas commited on Jul 12, 2023

Commit

1c0bf47

1 Parent(s): 280387a

rename

Browse files

Files changed (1) hide show

config.json +2 -2

config.json CHANGED Viewed

@@ -5,7 +5,7 @@
   "alpha_type": "float",
   "alphas_initializer_range": 0.0,
   "architectures": [
-    "VLlamaForCausalLM"
   ],
   "bos_token_id": 1,
   "cross_layer_activation_function": "swiglu",
@@ -23,7 +23,7 @@
   "intermediate_size": 11008,
   "max_position_embeddings": 2048,
   "max_sequence_length": 2048,
-  "model_type": "vllama",
   "num_attention_heads": 32,
   "num_hidden_layers": 32,
   "pad_token_id": 0,

   "alpha_type": "float",
   "alphas_initializer_range": 0.0,
   "architectures": [
+    "IdeficsForCausalLM"
   ],
   "bos_token_id": 1,
   "cross_layer_activation_function": "swiglu",
   "intermediate_size": 11008,
   "max_position_embeddings": 2048,
   "max_sequence_length": 2048,
+  "model_type": "idefics",
   "num_attention_heads": 32,
   "num_hidden_layers": 32,
   "pad_token_id": 0,