palmer-003 is a 1.1 billion parameter general language model developed by appvoid, designed to be a highly capable small model. It features a 2048-token context length and demonstrates competitive performance against other models in its size class across various benchmarks, including MMLU, ARC-C, and HellaSwag. This model is optimized for general language understanding and generation tasks, aiming for broad applicability in resource-constrained environments.
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palmer-003: A Capable Small Language Model
palmer-003 is appvoid's latest 1.1 billion parameter general language model, developed with the goal of creating a highly effective small-scale model. It is designed for broad applicability in various language understanding and generation tasks.
Key Capabilities & Performance
This model demonstrates competitive performance across several common benchmarks when compared to other models of similar or slightly larger parameter counts. Its evaluation scores include:
- MMLU: 0.2523
- ARC-C: 0.3439
- OBQA: 0.3740
- HellaSwag: 0.6208
- PIQA: 0.7524
- Winogrande: 0.6590
These results indicate that palmer-003 achieves an average score of 0.5004 across these benchmarks, positioning it favorably against models like tinyllama-3t and zyte-1b, and closely approaching the performance of the larger qwen-1.8b in some areas. The model operates with a context length of 2048 tokens.
Ideal Use Cases
palmer-003 is well-suited for applications requiring a compact yet capable language model, particularly where computational resources or deployment size are constraints. Its general-purpose nature makes it adaptable for tasks such as text generation, summarization, and question answering in scenarios where larger models might be impractical.