Update README.md

README.md

@@ -74,6 +74,28 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import datasets
+
+# load model and tokenizer
+MODEL = "text2tech/mistral-7b-instruct-v0.2-NER-RE-qlora-1200docs"
+model = AutoModelForCausalLM.from_pretrained(MODEL, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(MODEL, padding_side="left", pad_token_id=0)
+
+# prepare example data
+data = datasets.load_dataset("text2tech/ner_re_1000_texts_GPT3.5labeled_chat_dataset")
+ex_user_prompt = [data['test']['NER_chats'][0][0]]
+ex = tokenizer.apply_chat_template(ex_user_prompt, add_generation_prompt=True, return_dict=True, return_tensors='pt')
+ex = {k: v.to(model.device) for k, v in ex.items()} 
+print(ex_user_prompt[0]['content'])
+
+# generate response
+response = model.generate(**ex, max_new_tokens=300, temperature=0.0)
+
+# print decoded 
+input_len = ex['input_ids'].shape[1]
+print(tokenizer.decode(response[0][input_len:], skip_special_tokens=True))
+
 [More Information Needed]
 
 ## Training Details