Bilingual Chat Assistant Implementation
In this tutorial, we will implement a Bilingual Chat Assistant using the Meraj-Mini model from Arcee AI. The assistant will be seamlessly deployed on Google Colab using T4 GPU, demonstrating the capabilities of open-source language models and offering a hands-on experience in deploying advanced AI solutions within free cloud resources.
Tools Required
We will utilize the following tools:
- Arcee’s Meraj-Mini model
- Transformers library for model loading and tokenization
- Accelerate and bitsandbytes for efficient quantization
- PyTorch for deep learning computations
- Gradio for creating an interactive web interface
Enable GPU Acceleration
Start by enabling GPU acceleration and installing necessary libraries:
!nvidia-smi --query-gpu=name,memory.total --format=csv
!pip install -qU transformers accelerate bitsandbytes
!pip install -q gradio
Model Configuration
Next, we will configure settings for 4-bit quantization and load the model:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True
)
model = AutoModelForCausalLM.from_pretrained(
"arcee-ai/Meraj-Mini",
quantization_config=quant_config,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Meraj-Mini")
Creating the Chat Pipeline
We will set up a text generation pipeline suitable for chat interactions:
chat_pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.1,
do_sample=True
)
Defining Chat Functions
We will create two functions to manage chat interactions:
def format_chat(messages):
prompt = ""
for msg in messages:
prompt += f"<|im_start|>{msg['role']}n{msg['content']}<|im_end|>n"
prompt += "<|im_start|>assistantn"
return prompt
def generate_response(user_input, history=[]):
history.append({"role": "user", "content": user_input})
formatted_prompt = format_chat(history)
output = chat_pipeline(formatted_prompt)[0]['generated_text']
assistant_response = output.split("<|im_start|>assistantn")[-1].split("<|im_end|>")[0]
history.append({"role": "assistant", "content": assistant_response})
return assistant_response, history
Building the Chat Interface
Finally, we will create a user interface for the chat using Gradio:
import gradio as gr
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Message")
clear = gr.Button("Clear History")
def respond(message, chat_history):
response, _ = generate_response(message, chat_history.copy())
return response, chat_history + [(message, response)]
msg.submit(respond, [msg, chatbot], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch(share=True)
Next Steps
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