This is a simplified guide to an AI model called Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning maintained by DavidAU. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.
Model overview
Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning combines an unreleased Llama 3.3 8B base model with extended 128k context capabilities and specialized thinking/reasoning training. The model was fine-tuned using Unsloth over three epochs with the Claude 4.5-Opus High Reasoning dataset, creating a hybrid instruct and thinking model. Created by DavidAU, this model activates extended reasoning modes based on prompt content while maintaining instruction-following capabilities. The approach differs from similar models like Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-GGUF and Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF by focusing on reasoning activation rather than mixture-of-experts scaling.
Model inputs and outputs
The model accepts text prompts and generates responses with automatic thinking tag insertion when reasoning tasks are detected. Context windows of 8k or larger are recommended, though the model functions with 4k minimum. Specific trigger phrases such as "Think Deeply," "Explain," and "Tell me a horror story" activate extended thinking modes where the model performs multi-stage reasoning before generating output.
Inputs
- Text prompts with or without explicit reasoning indicators
- Context: Supports up to 128k token context window
- Configuration parameters: Temperature (suggested 0.7), repetition penalty (1.05), top-p (0.95), min-p (0.05), top-k (40)
Outputs
- Thinking sequences: Tagged reasoning blocks that show internal problem-solving steps
- Final responses: Generated text following thinking phases or direct instruction responses
- Extended format: Supports creative writing, technical explanations, and narrative content
Capabilities
The model demonstrates strong performance in technical reasoning tasks, generating detailed mathematical explanations and examples with proper derivations. When given prompts requesting deep analysis, it produces extended thinking sequences before formulating responses. Creative writing and story generation benefit from the reasoning training, producing narratives with coherent structure and thematic development. The 128k context enables handling of substantial documents and complex multi-part requests. Quantization options like Q4KS and IQ3_M maintain reasoning activation, though lower quantizations may degrade thinking performance.
What can I use it for?
Technical education and explanation generation work well for subjects like orbital mechanics, physics, and mathematics where step-by-step derivations enhance understanding. Creative fiction and storytelling benefit from the thinking phase, producing more carefully constructed narratives. Research and analysis tasks leverage the extended reasoning capability to break down complex problems. The model suits local deployment scenarios where users need reasoning without external API calls. Unlike models purely focused on creative outputs like L3.2-Rogue-Creative-Instruct-Uncensored-Abliterated-7B-GGUF, this model balances reasoning depth with instruction adherence.
Things to try
Experiment with prompts that include phrases like "Think deeply" or "Explain in detail" to activate the reasoning mode and observe the thinking process for complex problems. Test the model's performance on multi-step mathematical problems where the reasoning output illuminates the solution path. Try creative writing prompts without thinking activation indicators to see how instruction mode alone handles narrative generation. Adjust the smoothing factor to 1.5 in supported interfaces like KoboldCpp or text-generation-webui for improved chat and roleplay consistency. Test different quantization levels starting with Q4KS to find the balance between reasoning quality and inference speed on your hardware.