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Principle:Iamhankai Forest of Thought LLM Pipeline Loading

From Leeroopedia
Knowledge Sources
Domains NLP, Model_Loading
Last Updated 2026-02-14 03:00 GMT

Overview

A pattern for loading large language models into memory with architecture-specific inference routing and optional confidence-based self-correction.

Description

LLM Pipeline Loading abstracts the complexity of initializing different LLM architectures (Llama, Qwen, GLM, DeepSeek, Mistral) behind a unified interface. The Pipeline class auto-detects model architecture from the checkpoint name, loads tokenizer and model weights onto GPU with bfloat16 precision, and provides a consistent get_respond() method that routes to architecture-specific generation code. This pattern enables the Forest-of-Thought framework to work with any supported model without changing the calling code.

A key feature is self-correction: the Pipeline can measure generation confidence via log-probability scoring and automatically re-prompt the model when confidence falls below a threshold, improving answer quality at the cost of additional inference.

Usage

Use this principle when initializing the LLM backend for any FoT workflow. The Pipeline is instantiated once at startup and shared across all tree searches as a global client object. Required for benchmark evaluation, Game24 solving, and CGDM post-processing judge models.

Theoretical Basis

The Pipeline pattern implements architecture polymorphism: a single interface dispatches to model-specific implementations. Key design:

  • Auto-detection: Model type is inferred from checkpoint path substrings (qwen, llama, glm, deepseek, mistral)
  • Chat formatting: Each model architecture has its own chat template and system prompt formatting
  • Confidence scoring: Log-probability of generated tokens measures generation confidence:

confidence=1Ni=1NlogP(ti|t1,,ti1)

Where N is the number of generated tokens and P(t_i) is the probability of token i given the preceding context.

  • Self-correction loop: If confidence < threshold, the model is re-prompted with a correction instruction

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