tokenizer = AutoTokenizer.from_pretrained(model_name) model.config.attention_dropout = 0.0 model = model.to("cuda") Inference example input_text = "Explain the concept of a 'patched model' in AI." inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate( **inputs, max_new_tokens=256, temperature=0.7, do_sample=True, repetition_penalty=1.1 ) webe tori model 0105 patched
| 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% | tokenizer = AutoTokenizer
Always validate the model’s output for production use—especially in critical systems—and stay tuned for the upcoming 0200 release. For now, download the safetensors, run your benchmarks, and enjoy a faster, safer webe tori experience. Have you tested the webe tori model 0105 patched? Share your results and use cases in the comments below. For more AI model deep dives, subscribe to our newsletter. Share your results and use cases in the comments below
import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "webe/tori-0105-patched" # Hypothetical HF path model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, use_safetensors=True, device_map="auto" )
| 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 |