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
Download json_spans.py from LiquidAI/d1-2B: direct link, hf CLI and curl.
- Browser
- Download file 2.06 kB
-
https://huggingface.co/LiquidAI/d1-2B/resolve/main/json_spans.py
- Command line
-
hf download hf://LiquidAI/d1-2B/json_spans.py
-
curl -L -o json_spans.py https://huggingface.co/LiquidAI/d1-2B/resolve/main/json_spans.py
2.06 kB
| """Spans of a rendered JSON state: where its strings sit and what an escaped slice of one says, without torch.""" | |
| from __future__ import annotations | |
| import json | |
| def string_paths(text: str) -> list[tuple[int, int, str]]: | |
| """String-value spans in rendered JSON, excluding quotes; paths use RFC6901 escaping.""" | |
| decoder = json.JSONDecoder() | |
| ranges = [] | |
| def skip(at): | |
| while at < len(text) and text[at].isspace(): | |
| at += 1 | |
| return at | |
| def value(at, path): | |
| at = skip(at) | |
| if text[at] == "{": | |
| at = skip(at + 1) | |
| while text[at] != "}": | |
| key, end = decoder.raw_decode(text, at) | |
| if not isinstance(key, str) or text[skip(end)] != ":": | |
| raise ValueError("invalid JSON object") | |
| name = key.replace("~", "~0").replace("/", "~1") | |
| at = skip(value(skip(end) + 1, path + "/" + name)) | |
| if text[at] == ",": | |
| at = skip(at + 1) | |
| elif text[at] != "}": | |
| raise ValueError("invalid JSON object separator") | |
| return at + 1 | |
| if text[at] == "[": | |
| at, index = skip(at + 1), 0 | |
| while text[at] != "]": | |
| at = skip(value(at, path + "/" + str(index))) | |
| index += 1 | |
| if text[at] == ",": | |
| at = skip(at + 1) | |
| elif text[at] != "]": | |
| raise ValueError("invalid JSON array separator") | |
| return at + 1 | |
| item, end = decoder.raw_decode(text, at) | |
| if isinstance(item, str): | |
| ranges.append((at + 1, end - 1, path)) | |
| return end | |
| end = value(0, "") | |
| if skip(end) != len(text): | |
| raise ValueError("trailing JSON text") | |
| return ranges | |
| def json_text(escaped: str) -> str: | |
| """A slice of a rendered JSON string as the characters it encodes (unchanged when it cuts an escape).""" | |
| try: | |
| return json.loads(f'"{escaped}"') | |
| except ValueError: | |
| return escaped | |