tanquangduong/Qwen2.5-3B-Instruct-TinyStories
TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Nov 9, 2024License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Qwen2.5-3B-Instruct-TinyStories is a 3.1 billion parameter instruction-tuned causal language model developed by tanquangduong. Fine-tuned from unsloth/Qwen2.5-3B, this model was trained with Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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Overview
This model, tanquangduong/Qwen2.5-3B-Instruct-TinyStories, is a 3.1 billion parameter instruction-tuned causal language model. It was developed by tanquangduong and is fine-tuned from the unsloth/Qwen2.5-3B base model.
Key Characteristics
- Efficient Training: The model was trained significantly faster using Unsloth and Huggingface's TRL library, highlighting an optimized training approach.
- Instruction-Tuned: As an instruction-tuned model, it is designed to follow prompts and instructions effectively, making it suitable for a variety of conversational and task-oriented applications.
- Apache-2.0 License: The model is released under the permissive Apache-2.0 license, allowing for broad use and distribution.
Use Cases
- General Instruction Following: Ideal for applications requiring the model to respond to specific instructions or prompts.
- Research and Development: Its efficient training process makes it a good candidate for further experimentation and fine-tuning on specific datasets.