Instructions to use HuggingFaceM4/idefics-80b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/idefics-80b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceM4/idefics-80b-instruct")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-80b-instruct")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/idefics-80b-instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/idefics-80b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/idefics-80b-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-80b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/idefics-80b-instruct

SGLang

How to use HuggingFaceM4/idefics-80b-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-80b-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-80b-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-80b-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-80b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/idefics-80b-instruct with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/idefics-80b-instruct
```

VictorSanh commited on Oct 12, 2023

Commit

a14d258

1 Parent(s): 4b5c078

text generation inference infos

Browse files

Files changed (1) hide show

README.md +34 -0

README.md CHANGED Viewed

@@ -151,6 +151,40 @@ for i, t in enumerate(generated_text):
     print(f"{i}:\n{t}\n")
 ```
 # Training Details
 ## IDEFICS

     print(f"{i}:\n{t}\n")
 ```
+## Text generation inference
+The hosted inference API is powered by [Text Generation Inference](https://github.com/huggingface/text-generation-inference). To query the model, you can use the following code snippet. The key is to pass images as fetchable URLs with the markdown syntax:
+```
+from text_generation import Client
+API_TOKEN = "<YOUR_API_TOKEN>"
+API_URL = "https://api-inference.huggingface.co/models/HuggingFaceM4/idefics-80b-instruct"
+DECODING_STRATEGY = "Greedy"
+QUERY = "User: What is in this image?![](https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG)<end_of_utterance>\nAssistant:"
+client = Client(
+    base_url=API_URL,
+    headers={"x-use-cache": "0", "Authorization": f"Bearer {API_TOKEN}"},
+)
+generation_args = {
+    "max_new_tokens": 256,
+    "repetition_penalty": 1.0,
+    "stop_sequences": ["<end_of_utterance>", "\nUser:"],
+}
+if DECODING_STRATEGY == "Greedy":
+    generation_args["do_sample"] = False
+elif DECODING_STRATEGY == "Top P Sampling":
+    generation_args["temperature"] = 1.
+    generation_args["do_sample"] = True
+    generation_args["top_p"] = 0.95
+generated_text = client.generate(prompt=QUERY, **generation_args)
+print(generated_text)
+```
+Note that we currently only host the inference for the instructed models.
 # Training Details
 ## IDEFICS