Update README.md

README.md

@@ -39,20 +39,67 @@ If you would like to learn more about the Machine_Mindset open-source model, we
 * CUDA 11.4 and above are recommended (this is for GPU users, flash-attention users, etc.)
   <br>
 
-### Dependency
-
-
 <br>
 
 ### Quickstart
 
+* Using the HuggingFace Transformers library (single-turn dialogue):
+  ```bash
+  import torch
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  from transformers.generation.utils import GenerationConfig
+  
+  tokenizer = AutoTokenizer.from_pretrained("FarReelAILab/Machine_Mindset_en_ENFP", use_fast=False, trust_remote_code=True)
+  model = AutoModelForCausalLM.from_pretrained("FarReelAILab/Machine_Mindset_en_ENFP", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True)
+  model.generation_config = GenerationConfig.from_pretrained("FarReelAILab/Machine_Mindset_en_ENFP")
+  
+  messages = []
+  messages.append({"role": "user", "content": "What is your MBTI personality type?"})
+  response = model.chat(tokenizer, messages)
+  print(response)
+  
+  messages.append({'role': 'assistant', 'content': response})
+  messages.append({"role": "user", "content": "After spending a day with a group of people, how do you feel when you return home?"})
+  response = model.chat(tokenizer, messages)
+  print(response)
+
+  ```
+  ```python
+
+* Using the HuggingFace Transformers library (multi-turn dialogue):
+  ```bash
+  import torch
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  from transformers.generation.utils import GenerationConfig
+  tokenizer = AutoTokenizer.from_pretrained("FarReelAILab/Machine_Mindset_en_ENFP", use_fast=False, trust_remote_code=True)
+  model = AutoModelForCausalLM.from_pretrained("FarReelAILab/Machine_Mindset_en_ENFP", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True)
+  model.generation_config = GenerationConfig.from_pretrained("FarReelAILab/Machine_Mindset_en_ENFP")
+  messages = []
+  print("####Enter 'exit' to exit.")
+  print("####Enter 'clear' to clear the chat history.")
+  while True:
+      user=str(input("User:"))
+      if user.strip()=="exit":
+          break
+      elif user.strip()=="clear":
+          messages=[]
+          continue
+      messages.append({"role": "user", "content": user})
+      response = model.chat(tokenizer, messages)
+      print("Assistant:", response)
+      messages.append({"role": "assistant", "content": str(response)})
+
+  ```
+  ```python
+
+
 * Use LLaMA-Factory (multi-round conversation)
   ```bash
   git clone https://github.com/hiyouga/LLaMA-Factory.git
   cd LLaMA-Factory
   python ./src/cli_demo.py \
         --model_name_or_path /path_to_your_local_model \
-        --template baichuan2
+        --template llama2
   ```
   ```python
   #Conversation records: