Instructions to use LiquidAI/d1-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Add evaluation results

by SaylorTwift HF Staff - opened about 8 hours ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-0

SaylorTwift

about 8 hours ago

Hugging Face CLI

Add evaluation results for LiquidAI/d1-2B

Summary

This PR adds the benchmark results published on the model card for LiquidAI/d1-2B to .eval_results/, following the Hugging Face Hub evaluation-results specification.

Benchmarks Added

Decision Index (public suite) — 51.1

Benchmarks Skipped (Not Registered on Hub)

These benchmarks are on the model card but have no registered eval.yaml on the Hub:

DecisionBench (Hanno Labs) v1, Decision Bench (decisionbench.ai) v4, Fast Decisions, JevBench, ImajevBench, WorkflowEvals, Liquid's internal board
The image benchmarks (AI2D, BLINK, MMBench, MME, ...)

They can be added once their authors register an eval.yaml on the Hub.

Mapping notes

Typed Decisions (61.3): the card's metric and scale are unclear (the Hub task is accuracy on a 0-1 scale), so it's better to score it independently as was done for d1-3B.
MMMU-Pro (26.4): scored as a decision over the options on at most 1,000 rows, which isn't the standard setup, and the variant isn't stated.

Source

Model card: https://huggingface.co/LiquidAI/d1-2B
Blog: https://www.liquid.ai/blog/open-d1

Files Added

.eval_results/d1-2B.yaml

Verification

The scores come from the benchmark tables on the model card, as measured by Liquid AI. No verified token is included, because these were not run via HF Jobs with inspect-ai.

To upload:

hf upload LiquidAI/d1-2B .eval_results .eval_results --create-pr --commit-message "Add evaluation results"

Add evaluation results8c92cf1b

SaylorTwift changed pull request status to open about 7 hours ago

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Ready to merge

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