Smruthi3/Qwen2.5-1.5B-KTO-Finetuning
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Oct 6, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Smruthi3/Qwen2.5-1.5B-KTO-Finetuning is a 1.5 billion parameter Qwen2.5 model, finetuned by Smruthi3. This model was optimized for faster training using Unsloth and Huggingface's TRL library. It is designed for general instruction-following tasks, building upon the base capabilities of the Qwen2.5 architecture.
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Model Overview
Smruthi3/Qwen2.5-1.5B-KTO-Finetuning is a 1.5 billion parameter language model developed by Smruthi3. It is finetuned from the unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit base model, leveraging the Qwen2.5 architecture.
Key Characteristics
- Parameter Count: 1.5 billion parameters, making it a relatively compact model suitable for resource-constrained environments.
- Training Optimization: This model was trained significantly faster using the Unsloth library in conjunction with Huggingface's TRL library. This indicates an emphasis on efficient finetuning.
- Base Model: Built upon the Qwen2.5-Instruct series, suggesting strong instruction-following capabilities.
Potential Use Cases
- Instruction Following: Suitable for tasks requiring the model to adhere to specific instructions.
- Efficient Deployment: Its smaller size (1.5B parameters) makes it a candidate for applications where computational resources or inference speed are critical.
- Further Finetuning: Can serve as a strong base for additional domain-specific finetuning due to its optimized training process.