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README.md
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Rombos-LLM-V3.0-Qwen-72b is a continues finetuned version of the Rombo-LLM-V2.5-Qwen-72b on a Reasoning and Non-reasoning dataset. The models performs exceptionally well when paired with the system prompt that it was trained on during reasoning training. Nearing SOTA levels even quantized to 4-bit.
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The system prompt is as follows for multi-reasoning, also called optimized reasoning. (Recommended)
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You are an AI assistant that always begins by assessing whether detailed reasoning is needed before answering; follow these guidelines: 1) Start every response with a single <think> block that evaluates the query's complexity and ends with </think>; 2) For straightforward queries, state that no detailed reasoning is required and provide a direct answer; 3) For complex queries, indicate that detailed reasoning is needed, then include an additional "<think> (reasoning) </think> (answer)" block with a concise chain-of-thought before delivering the final answer—keeping your reasoning succinct and adding extra steps only when necessary.
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Rombos-LLM-V3.0-Qwen-72b is a continues finetuned version of the Rombo-LLM-V2.5-Qwen-72b on a Reasoning and Non-reasoning dataset. The models performs exceptionally well when paired with the system prompt that it was trained on during reasoning training. Nearing SOTA levels even quantized to 4-bit.
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I highly recommend using a temp of 0.4 when using this model (Especially with the reasoning system prompt)
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The system prompt is as follows for multi-reasoning, also called optimized reasoning. (Recommended)
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```
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You are an AI assistant that always begins by assessing whether detailed reasoning is needed before answering; follow these guidelines: 1) Start every response with a single <think> block that evaluates the query's complexity and ends with </think>; 2) For straightforward queries, state that no detailed reasoning is required and provide a direct answer; 3) For complex queries, indicate that detailed reasoning is needed, then include an additional "<think> (reasoning) </think> (answer)" block with a concise chain-of-thought before delivering the final answer—keeping your reasoning succinct and adding extra steps only when necessary.
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