Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
+base_model: unsloth/qwen3-14b
 tags:
 - text-generation-inference
 - transformers
@@ -36,8 +36,46 @@ Quantisation: 4-bit NF4 (base), fp16 (adapter)
 Training platform: Google Colab free tier (Tesla T4 16 GB) using Unsloth 2025-04-07
 
 
-
-
+# Usage 📊
+```python
+  pip install unsloth
+  pip install torch
+  pip install peft
+```
+
+```python
+from unsloth import FastLanguageModel
+import torch, re
+
+BASE_ID    = "unsloth/Qwen3-14B"   # base model
+ADAPTER_ID = "cihanunlu/qwen3-ner-lora"     # Lora Adapter model         
+
+# 1️⃣  Load the base model
+model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name     = BASE_ID,
+        load_in_4bit   = True,
+        max_seq_length = 2048,
+)
+
+# 2️⃣  Turn it into a PEFT container and add the adapter
+model = FastLanguageModel.get_peft_model(model)    
+model.load_adapter(ADAPTER_ID, adapter_name="ner")  
+model.set_adapter("ner")                         
+```
+```python
+def ner(sentence):
+    prompt = [ {"role":"user",
+                "content":f"Label this sentence {sentence}"} ]
+    chat = tokenizer.apply_chat_template(prompt, tokenize=False,
+                                         add_generation_prompt=True,
+                                         enable_thinking=False)
+    out  = model.generate(**tokenizer(chat, return_tensors="pt").to(model.device),
+                          max_new_tokens=64, do_sample=False)[0]
+    print(tokenizer.decode(out, skip_special_tokens=True))
+
+ner("Emin Bey’in kuklaları Tepebaşı’nda oynuyor.")
+# → B-PER I-PER O O O B-LOC O
+```
 
 # Evaluation 📊