Running local LLMs like Qwen3.5 4B requires careful measurement and verification, as results can be inconsistent. Enabling 'thinking' in some models can lead to empty responses if the token budget is exceeded, necessitating reading both response and thinking fields or disabling 'thinking'. Self-critique features do not reliably improve accuracy and can even degrade performance, highlighting the need for external, robust verification methods. Precise prompt engineering, specifying exact output formats and naming conventions, proved more effective than descriptive prompts in achieving accurate results. AI
IMPACT Highlights the need for rigorous testing and verification of local LLMs, as built-in features like self-critique can be unreliable.
RANK_REASON The item discusses findings from running and measuring local LLMs, including issues with specific model behaviors and prompt engineering techniques, akin to a research or technical report. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv:2310.01798
- GPT-4
- GSM8K
- Large Language Models Cannot Self-Correct Reasoning Yet
- Ollama
- Qwen3.5 4B
- Through the Valley: Path to Effective Long CoT Training for Small Language Models
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
