simplescaling/s1.1-3B
The simplescaling/s1.1-3B is a 3.1 billion parameter causal language model, based on the Qwen2.5-3B-Instruct architecture. It has been fine-tuned on the s1K-1.1 dataset and supports a context length of 32768 tokens. This model is part of the SimpleScaling series, with the developers recommending the s1.1-32B variant for general use.
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Model Overview
The simplescaling/s1.1-3B is a 3.1 billion parameter language model built upon the Qwen2.5-3B-Instruct architecture. It has been specifically fine-tuned using the s1K-1.1 dataset, indicating a specialized training focus. The model supports a substantial context window of 32768 tokens, allowing for processing longer inputs and generating more extensive outputs.
Key Characteristics
- Base Architecture: Qwen2.5-3B-Instruct
- Parameter Count: 3.1 billion
- Context Length: 32768 tokens
- Fine-tuning Dataset: s1K-1.1
Important Considerations
- Evaluation Status: The model has not been formally evaluated by its creators, meaning its performance characteristics are currently undocumented.
- Developer Recommendation: The developers explicitly recommend using the s1.1-32B model from the same series over this 3B variant, suggesting the larger model offers superior performance or broader applicability.
When to Consider Using This Model
Given the developer's recommendation and lack of evaluation, simplescaling/s1.1-3B might be suitable for:
- Experimental purposes: Exploring the impact of the s1K-1.1 fine-tuning on the Qwen2.5-3B-Instruct base.
- Resource-constrained environments: If the 32B model is too large, this 3B variant could serve as a lighter alternative, though with potentially reduced performance.
- Specific research: If your use case directly aligns with the s1K-1.1 dataset's characteristics and you are prepared to conduct your own evaluations.