Image-Text-to-Text
Transformers
Safetensors
lfm3_vl
feature-extraction
liquid
lfm3
edge
decision
classification
extraction
calibration
system-one
multimodal
decision-model
custom_code
conversational
Eval Results
Instructions to use LiquidAI/d1-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/d1-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="LiquidAI/d1-2B", trust_remote_code=True) 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LiquidAI/d1-2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LiquidAI/d1-2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LiquidAI/d1-2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LiquidAI/d1-2B", "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/LiquidAI/d1-2B
- SGLang
How to use LiquidAI/d1-2B 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 "LiquidAI/d1-2B" \ --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": "LiquidAI/d1-2B", "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 "LiquidAI/d1-2B" \ --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": "LiquidAI/d1-2B", "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 LiquidAI/d1-2B with Docker Model Runner:
docker model run hf.co/LiquidAI/d1-2B
Check that torch has flash attention before taking the flash path (fixes CUDA on Windows)
#6
by YAKA001 - opened
- attention.py +2 -0
attention.py
CHANGED
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@@ -42,6 +42,8 @@ def _fused(q: torch.Tensor) -> bool:
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"""Whether the flash kernels take these queries: a GPU in half precision, ROCm or CUDA from Ampere on."""
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if not q.is_cuda or q.dtype not in (torch.float16, torch.bfloat16):
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return False
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return bool(torch.version.hip) or torch.cuda.get_device_capability(q.device)[0] >= 8
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"""Whether the flash kernels take these queries: a GPU in half precision, ROCm or CUDA from Ampere on."""
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if not q.is_cuda or q.dtype not in (torch.float16, torch.bfloat16):
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return False
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+
if not torch.backends.cuda.is_flash_attention_available():
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return False
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return bool(torch.version.hip) or torch.cuda.get_device_capability(q.device)[0] >= 8
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