Update README.md

README.md

@@ -14,13 +14,48 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# outputs
-
-This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on an unknown dataset.
-
 ## Model description
 
-More information needed
+GOOGLEGEMMA modelini UZB datasetga fine-tuned qilindi  PEFT bilan. natijasi yaxshi deyishish qiyin. 
+Shuning uchun PEFT siz qilishni tafsiya qilaman .
+
+**Agarda siz PEFT bilan fine-tuned qilingan modellarni ishlatishni bilmasangiz, exmaple codega qarang**
+
+```
+    import torch
+    from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer
+    
+    model_name = "google/gemma-7b"
+    
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.float16,
+    )
+    
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        quantization_config=bnb_config,
+        trust_remote_code=True
+    )
+    model.config.use_cache = False
+
+    ##### yuqoridagi code hamma PEFT bilan qilingan modellarni reduced par qilish orqali free GPU Notebooklarda foydalanish imkoni beradi.
+
+    from peft import PeftModel, PeftConfig
+    from transformers import AutoModelForCausalLM,AutoTokenizer
+    
+    config = PeftConfig.from_pretrained("ai-nightcoder/outputs")
+    tokenizer = AutoTokenizer.from_pretrained('ai-nightcoder/outputs')
+
+    inputs = tokenizer("Xorijiy mamlakatlar", return_tensors="pt")
+    outputs = model(**inputs, labels=inputs["input_ids"])
+    predicted_token_class_ids = outputs.logits.argmax(-1)
+
+    generated_text = tokenizer.batch_decode(predicted_token_class_ids, skip_special_tokens=True)
+    print(generated_text)
+
+```
 
 ## Intended uses & limitations