prithivMLmods/Q3.5-9B-OpusGLM-MAX-0731-ablated

VISIONConcurrent Unit Cost:1Model Size:9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 30, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

prithivMLmods/Q3.5-9B-OpusGLM-MAX-0731-ablated is a 9-billion parameter language model built upon Qwen/Qwen3.5-9B, featuring a 32,768 token context length. It was developed through multi-stage supervised fine-tuning using general-purpose GLM and Opus reasoning traces. This model is specifically optimized for long-form reasoning, mathematical problem-solving, scientific analysis, and complex multi-step instruction following, making it suitable for research and experimentation in advanced reasoning tasks.

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Overview

prithivMLmods/Q3.5-9B-OpusGLM-MAX-0731-ablated is a 9-billion parameter experimental language model, leveraging the Qwen/Qwen3.5-9B foundation. It has been developed using a multi-stage supervised fine-tuning (SFT) approach, incorporating high-quality GLM and Opus reasoning traces. This training methodology aims to significantly enhance its capabilities in complex reasoning, mathematical problem-solving, scientific analysis, and multi-step instruction following, with a maximum sequence length of 32,768 tokens.

Key Capabilities

  • Enhanced Reasoning: Optimized for long-form, structured reasoning across various domains including mathematics, science, and analytical tasks.
  • Instruction Following: Improved ability to understand and execute complex, multi-step instructions.
  • Qwen 3.5 Foundation: Benefits from the robust architecture and pre-training of the Qwen/Qwen3.5-9B base model.
  • Efficient Deployment: Designed for efficient local inference and research environments due to its 9B parameter size.

Good For

  • Reasoning Research: Ideal for studying advanced reasoning techniques and multi-stage training methodologies.
  • Complex Problem Solving: Suitable for tackling intricate mathematical, scientific, and analytical challenges.
  • Code Generation & Analysis: Supports multi-step code generation and analytical reasoning tasks.
  • Experimental Use: A valuable tool for evaluating and improving instruction-following and reasoning capabilities in research settings.

Finetunes
2 models