Update README.md

README.md

@@ -134,18 +134,15 @@ Or, English.
 
 ### Let the assistant become an expert, and more.
 
+The challenge of the model's ability to understand the language.
 <img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/N0RJUFFf1t8QRg8AVyxNj.png" width="600" align="center" />
 
 Challenge the model's reasoning ability, in Vietnamese language.
-
 <img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/KUXjV2XJK5vNy7genVtfN.png" width="600" align="center" />
-
 <img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/ngX6unqUNnnBGq4R1gYY2.png" width="600" align="center" />
 
 In case of using Vietnamese language, it lacks accents, abbreviations or uses slang.
-
 <img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/xSL8WErn5girbKxUbEOsh.png" width="600" align="center" />
-
 <img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/-IXPjLL_QGb_5frOKftUW.png" width="600" align="center" />
 
 ## 📚 Model Details
@@ -159,16 +156,89 @@ The approach here uses QLora for training then merges them. Also, I am very than
 
 ## Uses
 
+### Online using Google Colab
+
 To make it easier to play around with the model, I created a notebook in [Google Colab](https://drive.google.com/file/d/1jVZuQ2QbMxLMJDKjpCRDKQaIxNXNpWI-/view?usp=sharing) so people can start experimenting.
 
-Although the amount of training data is small, it is "great". You don't need to worry too much that it won't be able to meet some of your requirements. Instead, try experimenting with the model of what you want.
+### Directly
+
+For direct use, you can easily get started with the following steps.
+
+* Firstly, you need to install **transformers** via the command below with `pip`.
+  ```bash
+  pip install -U transformers
+  ```
+
+* Right now, you can start using the model directly.
+  ```python
+  import torch
+  from transformers import (
+      AutoModelForCausalLM,
+      AutoTokenizer,
+  )
+
+  base_model = "lamhieu/ghost-7b-v0.9.1"
+  model = AutoModelForCausalLM.from_pretrained(
+      base_model,
+      torch_dtype=torch.bfloat16,
+      trust_remote_code=True,
+      device_map="auto",
+  )
+  tokenizer = AutoTokenizer.from_pretrained(base_model)
+  
+  messages = [
+      {"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate"},
+      {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
+  ]
+  prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+  tokenized = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+  outputs = model.generate(**tokenized, max_new_tokens=512)
+  results = tokenizer.batch_decode(outputs)[0]
+  print(results)
+  ```
+  
+* Additionally, you can also use a model with **4bit quantization** to reduce the required resources at least. You can start with the code below.
+  ```python
+  import torch
+  from transformers import (
+      AutoModelForCausalLM,
+      AutoTokenizer,
+      BitsAndBytesConfig,
+  )
+
+  base_model = "lamhieu/ghost-7b-v0.9.1"
+  bnb_config = BitsAndBytesConfig(
+      load_in_4bit=True,
+      bnb_4bit_quant_type="nf4",
+      bnb_4bit_compute_dtype=torch.bfloat16,
+      bnb_4bit_use_double_quant=False,
+  )
+  model = AutoModelForCausalLM.from_pretrained(
+      base_model,
+      quantization_config=bnb_config,
+      trust_remote_code=True,
+      device_map="auto",
+  )
+  tokenizer = AutoTokenizer.from_pretrained(base_model)
+  
+  messages = [
+      {"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate"},
+      {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
+  ]
+  prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+  tokenized = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+  outputs = model.generate(**tokenized, max_new_tokens=512)
+  results = tokenizer.batch_decode(outputs)[0]
+  print(results)
+  ```
 
-One more thing, use it like you would **ChatGPT**, I've purposely tweaked it to be able to replace my app (for some tasks, and it does a good job). It's okay with both Vietnamese and English languages. It would be great to hear feedback about the experience, feel free to leave information in the discussion section.
 
 ### Summary
 
-Setting up the system prompt will have a great impact on the performance and quality of the content generated by the model. Keep this in mind to always ensure the model is used for your intended purpose, the goal is to achieve good results but.
+Although the amount of training data is small, it is "great". You don't need to worry too much that it won't be able to meet some of your requirements. Instead, try experimenting with the model of what you want.
+One more thing, use it like you would **ChatGPT**, I've purposely tweaked it to be able to replace my app (for some tasks, and it does a good job). It's okay with both Vietnamese and English languages. It would be great to hear feedback about the experience, feel free to leave information in the discussion section.
 
+Setting up the system prompt will have a great impact on the performance and quality of the content generated by the model. Keep this in mind to always ensure the model is used for your intended purpose, the goal is to achieve good results but.
 It's best to always set system, you can still leave it empty if you always want to set it.
 
 ## 🥇 Evaluation