README.md

metadata

license: apache-2.0
base_model: jaredpalmer/kev-0.8b
library_name: onnxruntime
pipeline_tag: text-classification
tags:
  - kev
  - decision-model
  - typesafe
  - qwen3.5
  - onnx
  - webgpu

kev-0.8b-ONNX

kev-0.8b (revision 9a45d25eb2ab761841196625383fa1dff0e56c1e) by Jared Palmer, packaged for the browser by runonweb (runonweb/classify).

Kev is a Jev-style decision model: typed questions about one text (noul yes/no, choice, score) answered with calibrated probabilities, no text generation. Requests follow TypeSafe's System One API.

What changed from the original

• The rank-16 LoRA is merged into Qwen/Qwen3.5-0.8B-Base (revision dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68) in fp32.
• The backbone is exported with the onnxruntime-genai builder without the LM head and MTP layer: the graph returns hidden_states plus the recurrent/conv/KV cache, so the state is encoded once and each question runs as its own row on that cache (Kev's row form).
• Weights are int4 (RTN, block 32) with the Gated DeltaNet projections and their MLPs in int8; activations fp32. Needs LinearAttention / CausalConvWithState and 8-bit MatMulNBits: ONNX Runtime Web's native WebGPU build (onnxruntime-web/webgpu) or its WASM backend.
• The pointer head is head.bin (fp32: q.weight, q.bias, k.weight, k.bias); kev.json holds the calibration temperature, delimiter token ids and cache layout.

On 318 questions from Kev's development suites, probabilities differ from Kev's PyTorch fp32 path by 0.03 on average (the fp32 export matches to 2e-5); 16 answers change, none with a margin above 0.2. Recipe: training/kev-onnx in the runonweb repo.

Use

import { Classifier } from 'runonweb/classify'

const classifier = new Classifier({ model: 'midudev/kev-0.8b-ONNX' })
const { answers } = await classifier.classify({
  state: 'I was charged twice. Please fix this ASAP.',
  questions: { billing: { type: 'noul', instructions: 'Is this ticket about billing?' } },
})

License

Apache-2.0, like Kev and the Qwen3.5 base. Kev's training datasets have their own licenses; see the original model card.