largha:150m
4 TagsUpdated 144M params512 context100+ languagesApache-2.0by Phocinae
Largha v1.1 (mmBERT-small, 144M): 0.398 on Bespoke Labs' public benchmark; 18 ms for five questions on an RTX 5090.
multilingual144m
ollaya run largha:150m --preset triage "I was charged twice for my subscription this month and want a refund."curl http://localhost:11435/api/decide \
-H "Content-Type: application/json" \
-d '{
"model": "largha:150m",
"state": "I was charged twice for my subscription this month and want a refund.",
"questions": {
"department": {
"type": "choice",
"instructions": "Which team should handle this?",
"criteria": {
"billing": "Payments, invoices and refunds",
"technical": "Bugs, errors and outages",
"account": "Login, profile and settings"
}
},
"refund": {
"type": "noul",
"instructions": "Is the customer asking for a refund?"
}
}
}'# Already using a TypeSafe SDK? Set TYPESAFE_BASE_URL=http://localhost:11435 instead.
import requests
response = requests.post(
"http://localhost:11435/api/decide",
json={
"model": "largha:150m",
"state": "I was charged twice for my subscription this month and want a refund.",
"questions": {
"department": {
"type": "choice",
"instructions": "Which team should handle this?",
"criteria": {
"billing": "Payments, invoices and refunds",
"technical": "Bugs, errors and outages",
"account": "Login, profile and settings"
}
},
"refund": {
"type": "noul",
"instructions": "Is the customer asking for a refund?"
}
}
},
)
answers = response.json()["answers"]
print(answers["department"]["choice"], answers["refund"]["noul"])const response = await fetch("http://localhost:11435/api/decide", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
model: "largha:150m",
state: "I was charged twice for my subscription this month and want a refund.",
questions: {
department: {
type: "choice",
instructions: "Which team should handle this?",
criteria: {
billing: "Payments, invoices and refunds",
technical: "Bugs, errors and outages",
account: "Login, profile and settings"
}
},
refund: {
type: "noul",
instructions: "Is the customer asking for a refund?"
}
}
}),
});
const { answers } = await response.json();
console.log(answers.department.choice, answers.refund.noul);Details
- graph17d1a327ea4f · 2 MB
onnx · encoder · 144M · fp32 - graphaf013f7b3912 · 3 MB
onnx · encoder · 144M · fp16 - weightsb6472511eea3 · 289 MB
huggingface.co/Phocinae/Phocinae-Largha-150M-v1/resolve/5300e85…/model.safetensors - tokenizer609d8f4c067c · 34 MB
huggingface.co/Phocinae/Phocinae-Largha-150M-v1/resolve/5300e85…/tokenizer/tokenizer.json - decision80dc4d8477bd · 441 B
{"engine": "onnx", "family": "laya", "encoder": "encoder", "layout": "laya-markers-v1", …} - calibrationb9f584c723f3 · 129 B
{"temperature": [0.866, 0.808, 0.662]} - licensef326fcf01f00 · 10 KB
Phocinae-Largha-150M-v1 by Phocinae (https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1) - archd2da87032dbb · 1 KB
application/vnd.ollaya.arch
Every layer is checked against its sha256 when it is pulled. Weights and tokenizers download from the model author's Hugging Face repository at a pinned commit; Ollaya never re-hosts them.