Create README.md
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README.md
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---
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license: apache-2.0
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library_name: transformers
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base_model:
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- Rombo-Org/Rombo-LLM-V2.5-Qwen-72b
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datasets:
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- Rombo-Org/Optimized_Reasoning
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- NovaSky-AI/Sky-T1_data_17k
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tags:
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- unsloth
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---
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# Like my work? Support me on patreon for only $5 a month and get to vote on what model's i make next as well as get access to this org's private repo's
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Subscribe bellow:
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- Patreon.com/Rombodawg
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__________________________________________________
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# Rombo-LLM-V3.0-Qwen-72b
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
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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. Nearning SOTA levels even quantzied to 4-bit.
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The recommended system prompt is as followed 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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```
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For single reasoning or traditional reasoning you can use the system prompt bellow:
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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> (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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```
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For non-reasoning use cases no system prompt is needed (Not recommended)
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Quantized versions: (Coming soon)
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-
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Model Evaluation: (Coming soon)
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