Instructions to use HuggingFaceM4/idefics2-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceM4/idefics2-8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="HuggingFaceM4/idefics2-8b")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b") model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/idefics2-8b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HuggingFaceM4/idefics2-8b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceM4/idefics2-8b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceM4/idefics2-8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceM4/idefics2-8b
- SGLang
How to use HuggingFaceM4/idefics2-8b 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/idefics2-8b" \ --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/idefics2-8b", "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/idefics2-8b" \ --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/idefics2-8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceM4/idefics2-8b with Docker Model Runner:
docker model run hf.co/HuggingFaceM4/idefics2-8b
Bug in attention mask
The perceiver atgtention mask, flash atten and eager are different behavior.
normally, the anttionmask to Attentionin
if attention_mask != None:
latent_attention_mask = torch.ones(
(attention_mask.size(0), latents.size(1)),
dtype=attention_mask.dtype,
device=attention_mask.device,
)
attention_mask = torch.cat([attention_mask, latent_attention_mask], dim=-1)
attention_mask = (
_prepare_4d_attention_mask(
attention_mask, latents.dtype, tgt_len=self.n_latents
)
if not self._use_flash_attention_2
else attention_mask
)
it should be 0 reperesnets padding, and 1 presents normal values, this is right when using flash attention, but _prepare_4d_attention_mask actually will invert it.
make the attention mask be, 0 represnets normalvalues, and 1 represents padding value.
Then in attention forward:
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
this catually make the score hgiher for padding valeus after softmax.
Have guys tested eager behaivor>?
Can u confirm it is a bug or am wrong? thanks@
