Webe Tori Model 0105 Patched -

The patched tokenizer handles variable naming conventions across Python, JavaScript, and Go without breaking.

Independent benchmarks conducted on the HELM (Holistic Evaluation of Language Models) framework reveal measurable improvements:

| Metric | Original 0105 | Webe Tori Model 0105 Patched | |--------|----------------|------------------------------| | MMLU (5-shot) | 42.3 | 44.1 | | TruthfulQA | 51.7 | 54.2 | | GSM8K (Math reasoning) | 23.1 | 27.6 | | Multilingual NER (F1) | 68.4 | 81.3 | | Inference Time (100 tokens) | 2.1s | 1.6s | | Hallucination Rate | 12.4% | 6.8% | webe tori model 0105 patched

Benchmarks performed on an A100-40GB, batch size 1, FP16.

The most dramatic improvements are seen in multilingual tasks and numeric reasoning—areas where the original was notoriously weak. Against contemporary models of similar size (e

Against contemporary models of similar size (e.g., Phi-2, TinyLlama 1.1B), the webe tori model 0105 patched holds its own:

| Model | Size | MMLU | Speed (tok/s) | |--------|------|------|----------------| | TinyLlama 1.1B | 1.1B | 43.5 | 85 | | Webe Tori 0105 Patched | 1.2B | 44.1 | 92 | | Phi-2 | 2.7B | 56.0 | 68 | batch size 1

While Phi-2 offers higher accuracy, the Webe Tori patched model is faster and more memory-efficient, making it a viable choice for resource-constrained environments.

Given its improved stability and speed, the webe tori model 0105 patched is particularly well-suited for: