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adapter_model.safetensors – Fine-tuned LoRA weightsadapter_config.json – LoRA configurationtokenizer.json – Tokenizer JSONtokenizer_config.json – Tokenizer configurationvocab.json – Vocabularyspecial_tokens_map.json – Special token mappingsadded_tokens.json – Custom tokens added during fine-tuningREADME.md – This filechat_template.jinja – Optional chat formatting templatemerges.txt – Tokenizer merge rules1from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
2from peft import PeftModel
3
4# Load base model
5base_model_name = "your-base-model-name"
6tokenizer = AutoTokenizer.from_pretrained(base_model_name)
7base_model = AutoModelForCausalLM.from_pretrained(base_model_name, device_map="auto")
8
9# Load fine-tuned AnovX LoRA adapter
10model = PeftModel.from_pretrained(base_model, "anoof/anovx-finetuned")
11
12# Quick test
13pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
14print(pipe("Who is AnovX?", max_new_tokens=100)[0]['generated_text'])"Who is AnovX?""Explain photosynthesis.""What is the capital of Sri Lanka?""Generate a quiz question for Grade 8 Science."