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
```

VictorSanh commited on Aug 21, 2023

Commit

d135f3e

1 Parent(s): 9709489

Update readme and doc from the 80b repo

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -96,7 +96,7 @@ inputs = processor(prompts, return_tensors="pt").to(device)
 # inputs = processor(prompts[0], return_tensors="pt").to(device)
 # Generation args
-bad_words_ids = tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
 generated_ids = model.generate(**inputs, bad_words_ids=bad_words_ids, max_length=100)
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
@@ -141,7 +141,7 @@ inputs = processor(prompts, add_end_of_utterance_token=False, return_tensors="pt
 # Generation args
 exit_condition = processor.tokenizer("<end_of_utterance>", add_special_tokens=False).input_ids
-bad_words_ids = tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
 generated_ids = model.generate(**inputs, eos_token_id=exit_condition, bad_words_ids=bad_words_ids, max_length=100)
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)

 # inputs = processor(prompts[0], return_tensors="pt").to(device)
 # Generation args
+bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
 generated_ids = model.generate(**inputs, bad_words_ids=bad_words_ids, max_length=100)
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
 # Generation args
 exit_condition = processor.tokenizer("<end_of_utterance>", add_special_tokens=False).input_ids
+bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
 generated_ids = model.generate(**inputs, eos_token_id=exit_condition, bad_words_ids=bad_words_ids, max_length=100)
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)