A Practical Approach to Enhance the Output Accuracy of Three-Valued Weighting Models
Although Bonsai uses parametric compression techniques, the generation can be significantly improved by the following methods:
- Parameter tuning combinations::
- Temperature coefficient (0.3-0.7 range suitable for factual Q&A)
- Top-p sampling (0.9-0.95 recommended to balance diversity and accuracy)
- Repeat Penalty (Settings)
repetition_penalty=1.2(Avoid cyclic output)
- Input Preprocessing::
- Add task prefixes such as
[问答],[摘要]Clarification of intent - For specialty area questions, provide 3-5 keyword contexts in the prompt
- Add task prefixes such as
- Post-processing techniques::
- expense or outlay
skip_special_tokens=TrueFiltering control characters - Combined with a rules engine to validate factual content (e.g., dates, place names, etc.)
- expense or outlay
Empirical tests show that the optimized Bonsai improves accuracy by 18-23% on general knowledge quiz tasks.
This answer comes from the articleBonsai: A three-valued weighted language model suitable for operation on edge devicesThe
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