Update README.md

README.md

@@ -17,7 +17,44 @@ datasets:
 - **Developed by:** Wajdi1976
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/qwen2.5-3b-bnb-4bit
+### First, Load the Model
+```python
+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Wajdi1976/alpaca_arabic_Qwen2.5-3B",
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+```
 
+### Second, Try the model 
+```python
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{}
+### Input:
+{}
+### Response:
+{}"""
+# alpaca_prompt = Copied from above
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+       "استخدم البيانات المعطاة لحساب الوسيط.", # instruction
+        "[2 ، 3 ، 7 ، 8 ، 10]", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
+tokenizer.batch_decode(outputs)
+```
 This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)