Instructions to use HuggingFaceM4/Idefics3-8B-Llama3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceM4/Idefics3-8B-Llama3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="HuggingFaceM4/Idefics3-8B-Llama3") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3") model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HuggingFaceM4/Idefics3-8B-Llama3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceM4/Idefics3-8B-Llama3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceM4/Idefics3-8B-Llama3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/HuggingFaceM4/Idefics3-8B-Llama3
- SGLang
How to use HuggingFaceM4/Idefics3-8B-Llama3 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/Idefics3-8B-Llama3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceM4/Idefics3-8B-Llama3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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/Idefics3-8B-Llama3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceM4/Idefics3-8B-Llama3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use HuggingFaceM4/Idefics3-8B-Llama3 with Docker Model Runner:
docker model run hf.co/HuggingFaceM4/Idefics3-8B-Llama3
Potential Inconsistencies Model and Datasets License
#25 opened 12 months ago
by
yueyangchen
Question regarding license
#24 opened about 1 year ago
by
ir2718
Text only generation for Idefics3
3
#22 opened over 1 year ago
by
FreekCool
This model can enabling video understanding and multi-image understanding capabilities?
#21 opened over 1 year ago
by
xJohn
Considerable speed loss after Lora Finetuning
14
#14 opened over 1 year ago
by
ayyylemao
Releasing base model and combined SFT dataset
3
#13 opened over 1 year ago
by
SS12444
How to use history prompts on the same image?
3
#12 opened over 1 year ago
by
MotiHa
Image encoding / rescaling Question
1
#11 opened almost 2 years ago
by
ayyylemao
pretraining datasets
1
#8 opened almost 2 years ago
by
yasserDahou
gpu requirement
1
#7 opened almost 2 years ago
by
mdeniz1
Support for Llama.cpp
#5 opened almost 2 years ago
by
chibop
Any Idea When This Will Be Supported in TGI?
2
#3 opened almost 2 years ago
by
pr1me
AssertionError: Padding_idx must be within num_embeddings
5
#2 opened almost 2 years ago
by
ZeevRispler
Transformer Issue ?
5
#1 opened almost 2 years ago
by
jgsmcmahon