kev:4b-fp16
6 TagsUpdated 4.2B params8192 contextEnglishApache-2.0by Jared Palmer
Kev on Qwen3.5-4B base (LoRA and pointer head): more accurate than 0.8b out of domain. Needs about 9 GB of memory. This tag computes in fp16 on NVIDIA GPUs: faster than kev:4b, with at least 99.5 % of its decisions the same. On a CPU it runs, slowly; use kev:4b there.
4.2b
ollaya run kev:4b-fp16 --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": "kev:4b-fp16",
"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": "kev:4b-fp16",
"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: "kev:4b-fp16",
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
- graph504883970526 · 15 MB
onnx · kev · 4.2B · fp16 - weightsdf547074dce7 · 5.3 GB
huggingface.co/Qwen/Qwen3.5-4B-Base/resolve/1001bb4…/model.safetensors-00001-of-00002.safetensors - weights590fbaac095d · 4.0 GB
huggingface.co/Qwen/Qwen3.5-4B-Base/resolve/1001bb4…/model.safetensors-00002-of-00002.safetensors - weights90e817356246 · 130 MB
huggingface.co/jaredpalmer/kev-4b/resolve/139fdd9…/adapter_model.safetensors - weightsdd633435998e · 5 MB
huggingface.co/jaredpalmer/kev-4b/resolve/139fdd9…/head.pt - tokenizer06b9509352d2 · 20 MB
huggingface.co/jaredpalmer/kev-4b/resolve/139fdd9…/tokenizer.json - decision27e748d8c47f · 3 KB
{"engine": "onnx", "family": "kev", "encoder": "", "layout": "kev-pointer-v1", …} - calibrationa51c52e256db · 252 B
{"temperature": [2.406, 2.406, 2.406]} - license5ddbf0af0c82 · 10 KB
Kev by Jared Palmer (https://huggingface.co/jaredpalmer/kev-4b)
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.