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
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
File size: 1,268 Bytes
97c5704 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | """LFM3's transformers classes, one module a model type: `native_lfm3` (text), `native_lfm3_vl` (pictures),
`native_lfm3_omni` (pictures and audio), each importing the one before it, so a Hub repository serves only its
model's files. transformers' own classes once a release ships them; until then copies of transformers' LFM3 code
beside these (each names its commit), registered with the Auto classes, so a checkpoint's config, tokenizer and
processor load and the composite models build their parts.
Shipping LFM3, transformers makes the fallbacks dead: delete them and the copies (the files headed "Vendored from"),
nothing else."""
from __future__ import annotations
try:
from transformers import Lfm3Config, Lfm3ForCausalLM, Lfm3Model
except ImportError:
from transformers import AutoConfig, AutoModel
from .configuration_lfm3 import Lfm3Config
from .modeling_lfm3 import Lfm3ForCausalLM, Lfm3Model
# exist_ok: a second copy of these files in one process (two snapshots) registers again
AutoConfig.register(Lfm3Config.model_type, Lfm3Config, exist_ok=True)
AutoModel.register(Lfm3Config, Lfm3Model, exist_ok=True) # the composite models build their text model by config
__all__ = ["Lfm3Config", "Lfm3ForCausalLM", "Lfm3Model"]
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