Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
🚀 New: Open d1 — run decision models locally with d1-3B and d1-omni-600M! Learn more →
3B parameter multimodal decision model for text and images, built on LFM2.5-VL-3B
| Property | Value |
|---|---|
| Parameters | 3.12B |
| Context Length | 32K tokens |
| Architecture | LFM2.5-VL (Dense) — LFM2.5-2.6B backbone + SigLIP2 NaFlex 400M vision encoder |
pip install "transformers>=5.14" torch torchvision pillow
from transformers import AutoModel
model_id = "LiquidAI/d1-3B"
model = AutoModel.from_pretrained(model_id, trust_remote_code=True, dtype="bfloat16", device_map="auto")
state = "I was charged twice this month, please refund one of them."
questions = {
"refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"},
"team": {"type": "choice", "instructions": "Which team should handle this?",
"criteria": {"billing": "Charges, refunds, invoices", "technical": "App or site faults",
"fraud": "Suspected unauthorised use"}},
"urgency": {"type": "score", "instructions": "How urgent is this?",
"criteria": ["Can wait", "Today", "Blocking the customer now"]},
}
output = model.system_one(state, questions)
print(output)
import io
import urllib.request
from PIL import Image
from transformers import AutoModel
model_id = "LiquidAI/d1-3B"
model = AutoModel.from_pretrained(model_id, trust_remote_code=True, dtype="bfloat16", device_map="auto")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
photo = Image.open(io.BytesIO(urllib.request.urlopen(url).read()))
questions = {"cats": {"type": "choice", "instructions": "How many cats are there?",
"criteria": {"one": "One", "two": "Two", "more": "Three or more"}}}
output = model.system_one(None, questions, images=[photo])
print(output)
llama-server -hf LiquidAI/d1-3B-GGUF:Q8_0
curl http://127.0.0.1:8080/v1/systemone -H "Content-Type: application/json" -d '{
"state": "I was charged twice this month, please refund one of them.",
"questions": {
"refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"},
"team": {"type": "choice", "instructions": "Which team should handle this?",
"criteria": {"billing": "Charges, refunds, invoices", "technical": "App or site faults",
"fraud": "Suspected unauthorised use"}},
"urgency": {"type": "score", "instructions": "How urgent is this?",
"criteria": ["Can wait", "Today", "Blocking the customer now"]}
}
}'
curl -sL -o cats.jpg http://images.cocodataset.org/val2017/000000039769.jpg
curl http://127.0.0.1:8080/v1/systemone -H "Content-Type: application/json" -d @- <<JSON
{
"images": ["data:image/jpeg;base64,$(base64 < cats.jpg | tr -d '\n')"],
"questions": {
"cats": {"type": "choice", "instructions": "How many cats are there?",
"criteria": {"one": "One", "two": "Two", "more": "Three or more"}}
}
}
JSON
Was this page helpful?