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
```

Upload tokenizer.json

by Narsil - opened Oct 5, 2023

base: refs/heads/main

←

from: refs/pr/8

Discussion Files changed

-2

Files changed (1) hide show

tokenizer.json +8 -2

tokenizer.json CHANGED Viewed

@@ -45,7 +45,7 @@
       "single_word": false,
       "lstrip": false,
       "rstrip": false,
-      "normalized": false,
       "special": true
     },
     {
@@ -71,6 +71,12 @@
           "String": " "
         },
         "content": "▁"
       }
     ]
   },
@@ -93415,4 +93421,4 @@
       "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁"
     ]
   }
-}

       "single_word": false,
       "lstrip": false,
       "rstrip": false,
+      "normalized": true,
       "special": true
     },
     {
           "String": " "
         },
         "content": "▁"
+      },
+      {
+          "type":"Replace",
+          "pattern":{
+              "Regex":"!\\[\\]([^\\]]*)"},
+              "content":"<image>"
       }
     ]
   },
       "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁"
     ]
   }
+}