added usage

README.md

@@ -1,3 +1,64 @@
 ---
 license: apache-2.0
 ---
+How to use:
+```
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+# Load Base Model
+base_model_id = "mistralai/Mistral-7B-v0.1"
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config)
+
+eval_tokenizer = AutoTokenizer.from_pretrained(
+    base_model_id,
+    add_bos_token=True,
+    trust_remote_code=True,
+)
+eval_tokenizer.pad_token = eval_tokenizer.eos_token
+
+# Load Peft Weights
+from peft import PeftModel
+
+ft_model = PeftModel.from_pretrained(model, "mistral-samsum-finetune/checkpoint-100")
+
+# Format the Sample Input
+def formatting_func(example):
+    text = f"### Summarize this dialog:\n{example['dialogue']}\n### Summary:\n{example['summary']}"
+    return text
+
+max_length = 256
+eval_prompt = {'dialogue': "Amanda: I baked cookies. Do you want some? Jerry: Sure! Amanda: I'll bring you tomorrow :-)",
+                              'summary': ''}
+eval_prompt = formatting_func(eval_prompt)
+
+# Generate summary for sample Input
+model_input = eval_tokenizer(
+        eval_prompt,
+        truncation=True,
+        max_length=max_length,
+        padding="max_length",
+        return_tensors="pt").to("cuda")
+
+ft_model.eval()
+with torch.no_grad():
+    print(eval_tokenizer.decode(ft_model.generate(**model_input, 
+                                               max_new_tokens=256, 
+                                               repetition_penalty=1.15)[0], 
+                                skip_special_tokens=True))
+
+# here is the output:
+"""
+### Summarize this dialog:
+Amanda: I baked cookies. Do you want some? Jerry: Sure! Amanda: I'll bring you tomorrow :-)
+### Summary:
+Jerry will get some cookies from Amanda tomorrow.
+"""
+```