Webe Tori Model 0105 Patched ((new))

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

| Model | Size | MMLU | Speed (tok/s) | |--------|------|------|----------------| | TinyLlama 1.1B | 1.1B | 43.5 | 85 | | | 1.2B | 44.1 | 92 | | Phi-2 | 2.7B | 56.0 | 68 | webe tori model 0105 patched

The most dramatic improvements are seen in multilingual tasks and numeric reasoning—areas where the original was notoriously weak. For developers and researchers looking to implement the webe tori model 0105 patched , here is a standard loading procedure using Python and the Transformers library (assuming the model is hosted on Hugging Face or a local path): print(tokenizer

| Metric | Original 0105 | Webe Tori Model 0105 Patched | |--------|----------------|------------------------------| | | 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