metadata

license: apache-2.0
library_name: transformers
base_model:
  - Rombo-Org/Rombo-LLM-V2.5-Qwen-72b
datasets:
  - Rombo-Org/Optimized_Reasoning
  - NovaSky-AI/Sky-T1_data_17k
tags:
  - unsloth

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Rombo-LLM-V3.0-Qwen-72b

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.

I highly recommend using a temp of 0.4 when using this model (Especially with the reasoning system prompt)

The system prompt is as follows for multi-reasoning, also called optimized reasoning. (Recommended)

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.

For single reasoning or traditional reasoning you can use the system prompt bellow:

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.

For non-reasoning use cases no system prompt is needed (Not recommended)

Quantized versions:

Model Evaluation: (Coming soon)