sugarknight / erabi-practical-v1-experimental

ERABI Practical V1 (experimental)

This is an experimental , uncalibrated choice-ranking model. It is not an official Jev model, a validated general-purpose reasoner, or an automatic decision-maker. The model ranks 2–16 user-supplied candidate texts for a natural-language context and question and returns all candidate probabilities through the ERABI code . Decisions should be reviewed by a person.

Provenance

• Base: knowledgator/gliclass-instruct-large-v1.0 , Apache-2.0, 438,672,897 parameters.
• Additional fine-tuning: one epoch on 2,414 Practical V1 training records, peak learning rate 2.5e-6, 151 optimizer steps, microbatch 2, gradient accumulation 8, fp16 AMP.
• Data: original synthetic Japanese, English, and Simplified Chinese examples in six task families: everyday arithmetic, tool choice, dialogue action, JSON conversation-log routing, reading inference, and original exam-style questions. Generated and answer-blind rejudged with DeepSeek V4.1 Flash via OrcaRouter. No actual entrance-exam questions or private chat logs were used.
• Data and training code: GitHub repository and training script . Labels remain unreviewed synthetic teacher agreement , not human gold.

Exploratory evaluation

The Practical V1 eval set was used once after selecting by dev and existing-bridge results. Reading inference regressed from 54/71 to 50/71 despite aggregate gains. Candidate-order consistency on the existing bridge was 97.50%. These figures are not a benchmark of real-world correctness or Jev parity, because Practical V1 questions and labels come from the same teacher family. There is no independent human-verified final test, temperature calibration, or formal release approval for this checkpoint.

Use

python -m pip install erabi
erabi predict --request request.json

The first invocation downloads the model; later invocations use the Hugging Face cache. Input and output JSON contracts are documented in the ERABI README . The input limit is 512 tokens; overlong inputs are rejected rather than silently truncated. The candidate probabilities are not calibrated confidence guarantees.

License and limitations

These fine-tuned weights derive from the Apache-2.0-licensed GLiClass base model and are distributed under Apache-2.0; see the Apache License 2.0 and the base model card . ERABI source code is separately MIT-licensed. Do not rely on this experimental model for high-stakes or unattended decisions.