## Turn 1 We need to produce a long answer (~4500 tokens). That's huge. We need to produce a detailed support answer covering all sections. We need to keep it accessible but technical. Provide examples of good and bad prompt shapes. Provide practical defaults. Provide benchmark design. Provide flight recorder. Provide settings to check. Provide when to increase budgets, when to stop a run. Provide short diagnosis, why this happens, etc. We need to produce a long answer. 4500 tokens is about 3500 words. That's a lot. We need to produce a comprehensive answer. We need to keep it structured. Provide sections. Provide examples. Provide explanation of llama.cpp behind a proxy. The user says: "My local model seems smart, but sometimes it spends all its tokens thinking and never answers. I am using llama.cpp behind a proxy. I want a practical explanation of what is happening, what settings to check, and how to design prompts so I still get useful final answers." We need to explain that the model may be generating a lot of tokens before producing a final answer due to the prompt design, token budget, temperature, top_p, top_k, presence penalty, frequency penalty, etc. Also the proxy may have a timeout or token limit. The model may be generating a long chain of reasoning tokens but not outputting final answer due to the prompt not specifying a final answer or due to the model being stuck in a loop. Provide guidance on setting max tokens, stop sequences, etc. Provide guidance on designing prompts: use explicit instructions, ask for concise answer, use "Answer in 2 sentences" etc. Provide examples of good and bad prompt shapes. Provide how to design prompts to get useful final answers. Provide settings to check: max tokens, temperature, top_p, top_k, presence penalty, frequency penalty, stop sequences, context length, memory usage, etc. Provide practical defaults: e.g., temperature 0.7, top_p 0.9, top_k 40, max tokens 512, etc. Provide when to increase budgets: if you need more context or more tokens. Provide when to stop a run: if the model is stuck, or if the token budget is exceeded, or if the answer is not relevant. Also mention the flight recorder: what to look for: token usage, time per token, latency, memory usage, CPU/GPU usage, etc. Also mention benchmark design: how to test the model's performance: measure latency, token generation speed, memory usage, etc. Provide guidelines for designing benchmarks: use consistent prompts, measure average latency, measure token generation time, measure CPU usage, measure GPU usage, measure memory usage, measure error rates, etc. Also mention that the model may be generating tokens but not outputting final answer due to the proxy not sending them or due to the model not encountering a stop sequence. Provide guidance on using stop sequences like "\n\n" or "Answer:" etc. Also mention that the model may be generating a long chain of reasoning tokens due to high temperature or low top_p, causing it to wander. Provide guidance on controlling temperature, top_p, top_k. Also mention that the model may be generating tokens but not outputting final answer due to the prompt not being explicit enough. Provide guidance on prompt design: ask for final answer explicitly, use "Please answer in 3 bullet points" etc. Also mention that the model may be generating tokens but not outputting final answer due to the model's internal logic: it may be generating a chain-of-thought but not concluding. Provide guidance on using "Answer:" or "Final answer:" as a marker. Also mention that the model may be generating tokens but not outputting final answer due to the proxy's token limit or timeouts. Provide guidance on adjusting the proxy's token limit, timeouts, etc. Also mention that the model may be generating tokens but not outputting final answer due to the model's internal logic: it may be generating a chain-of-thought but not concluding. Provide guidance on using "Answer:" or "Final answer:" as a marker. Also mention that the model may be generating tokens but not outputting final answer due to the proxy's token limit or timeouts. Provide guidance on adjusting the proxy's token limit, timeouts, etc. Also mention that the model may be generating tokens but not outputting final answer due to the model's internal logic: it may be generating a chain-of-thought but not concluding. Provide guidance on using "Answer:" or "Final answer:" as a marker. Also mention that the model may be generating tokens but not outputting final answer due to the proxy's token limit or timeouts. Provide guidance on adjusting the proxy's token limit, timeouts, etc. Also mention that the model may be generating tokens but not outputting final answer due to the model's internal logic: it may be generating a chain-of-thought but not concluding. Provide guidance on using "Answer:" or "Final answer:" as a marker. Also mention that the model may be generating tokens but not outputting final answer due to the proxy's token limit or timeouts. Provide guidance. We need to produce a long answer. Let's outline sections: # Short Diagnosis - Summarize: The model is generating tokens but not outputting final answer due to prompt design, token budget, or proxy settings. # Why This Happens - Explanation: Llama.cpp uses token generation; if no stop sequence, it may keep generating. The model may be stuck in chain-of-thought. The proxy may have a token limit or timeout. The model may be using high temperature causing wandering. The model may be using low top_p causing high variance. The model may be using high presence penalty causing it to avoid repeating tokens. The model may be using high frequency penalty causing it to avoid repeating tokens. The model may be using a context window that is too small for the prompt. The model may be using a large context but not enough tokens for final answer. # Settings To Check - Temperature - Top_p - Top_k - Max tokens - Stop sequences - Presence penalty - Frequency penalty - Context length - Memory usage - Proxy timeouts - Proxy token limit - CPU/GPU usage - Logging # Prompt Design - Good prompt shapes: explicit instructions, short, ask for final answer, use "Answer:" marker, use bullet points, ask for "concise" answer, ask for "final answer" after chain-of-thought, use "Please answer in 3 sentences." Provide examples of good and bad prompts. # Benchmark Design - How to design benchmarks: measure latency, token generation speed, memory usage, CPU usage, GPU usage, error rates, etc. Use consistent prompts, measure average latency, measure token generation time, measure CPU usage, measure GPU usage, measure memory usage, measure error rates, etc. # What The Flight Recorder Should Show - Flight recorder logs: token usage, time per token, latency, memory usage, CPU usage, GPU usage, errors, etc. # Practical Defaults - Provide recommended defaults: temperature 0.7, top_p 0.9, top_k 40, max tokens 512, presence penalty 0.0, frequency penalty 0.0, context length 2048 tokens, etc. # When To Increase Budgets - When you need more context, more tokens, more memory, more GPU, etc. # When To Stop A Run - When the model is stuck, when token budget is exceeded, when latency is too high, when memory usage is high, when errors occur, etc. We need to produce a long answer. Let's produce a thorough answer with examples. We'll need to produce about 4500 tokens. That's about 3500 words. We'll produce a long answer. We'll produce sections with subpoints, examples, code snippets, etc. We'll produce a thorough answer. We need to keep it accessible but technical. We'll produce a thorough answer. We'll produce a final answer with all sections. We'll produce a final answer. We'll produce a final answer. We'll produce a final answer. Let's produce the final answer.