ertghiu256/qwen3.5-2b-glm-deepseek-distillation
VISIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2.3BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 18, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold
The ertghiu256/qwen3.5-2b-glm-deepseek-distillation is a 2.3 billion parameter language model developed by ertghiu256. This model is a finetuned version of an existing qwen3.5 base, optimized for faster training using Unsloth and Huggingface's TRL library. Its primary characteristic is its efficient training process, making it suitable for applications requiring rapid iteration and deployment of specialized language models.
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ertghiu256/qwen3.5-2b-glm-deepseek-distillation Overview
This model, developed by ertghiu256, is a 2.3 billion parameter language model that has been finetuned for specific applications. It is based on a qwen3.5 architecture and distinguishes itself through its training methodology.
Key Capabilities
- Efficient Finetuning: The model was trained significantly faster using Unsloth and Huggingface's TRL library, indicating an optimization for rapid adaptation and deployment.
- Base Model Derivation: It is a finetuned version of an existing qwen3.5 base model, suggesting it inherits core language understanding and generation capabilities from its foundation.
Good For
- Rapid Prototyping: Ideal for developers and researchers who need to quickly finetune and iterate on language models for specific tasks.
- Resource-Constrained Environments: The efficient training process implies potential benefits for environments with limited computational resources, allowing for faster experimentation.
- Specialized Applications: Suitable for creating custom language models tailored to niche domains or specific instruction-following tasks, leveraging the finetuning approach.