Update README.md

README.md

@@ -12,7 +12,9 @@ base_model:
 
 This is quantized adapters trained on the Ultrachat 200k dataset for the TinyLlama-1.1B Intermediate Step 1431k 3T model.
 
-adapter_name = TinyLlama-1.1B-intermediate-1431k-3T-adapters-ultrachat
+```python
+adapter_name = 'iqbalamo93/TinyLlama-1.1B-intermediate-1431k-3T-adapters-ultrachat'
+```
 
 ## Model Details
 
@@ -32,4 +34,31 @@ bnb_config = BitsAndBytesConfig(
 ### Model Description
 This is quantized adapters trained on the Ultrachat 200k dataset for the TinyLlama-1.1B Intermediate Step 1431k 3T model.
 
-- Finetuned from model : [TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T)
+- Finetuned from model : [TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T)
+
+### How to use
+
+#### Method 1: Direct loading
+```python
+from peft import PeftModel, AutoPeftModelForCausalLM
+from transformers import pipeline, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+adapter_name = 'iqbalamo93/TinyLlama-1.1B-intermediate-1431k-3T-adapters-ultrachat'
+model = AutoPeftModelForCausalLM.from_pretrained(
+    adapter_name,
+    device_map="auto"
+)
+model = model.merge_and_unload()
+
+prompt = """<|user|>
+Tell me something about Large Language Models.</s>
+<|assistant|>
+"""
+
+pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
+print(pipe(prompt)[0]["generated_text"])
+
+```
+#### Method 2: Merging with base mode explicitly
+todo