Discover how you can harness the power of lmsys/fastchat-t5-3b-v1.0 language model and leverage Intel CPUs to build an advanced AI-powered chatbot. Let's dive in!

Python Code:

# Installing the Intel® Extension for PyTorch* CPU version
python -m pip install intel_extension_for_pytorch

# Importing the required libraries
import torch
from transformers import T5Tokenizer, AutoModelForSeq2SeqLM
import intel_extension_for_pytorch as ipex

# Loading the T5 model and tokenizer
tokenizer = T5Tokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0")
model = AutoModelForSeq2SeqLM.from_pretrained("lmsys/fastchat-t5-3b-v1.0", low_cpu_mem_usage=True)

# Setting up the conversation prompt
prompt = """\
### Human: Write a Python script for Factorial of a number.
### Assistant:\
"""

# Tokenizing the prompt
inputs = tokenizer(prompt, return_tensors='pt')

# Generating the response using the T5 model
tokens = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=1.0,
    top_p=1.0,
)

# Printing the generated response
print(tokenizer.decode(tokens[0], skip_special_tokens=True))

By utilizing the powerful lmsys/fastchat-t5-3b-v1.0 language model and the optimized performance of Intel CPUs, you can create an intelligent chatbot capable of providing accurate and insightful responses.

For more information about the lmsys/fastchat-t5-3b-v1.0 model, please visit the lmsys/fastchat-t5-3b-v1.0 GitHub repository. To explore the benefits of using Intel CPUs for AI applications, check out the Intel® Extension for PyTorch* CPU version documentation.

If you are looking for chat models to enhance your conversational AI applications, there are several options available. Two popular models worth exploring are rwkv/raven 1.5B and fastchat-t5 3B .

rwkv/raven 1.5B is a powerful model that can generate responses for conversations. You can find the model as ggml, which stands for "generalized generative model language." It offers an extensive corpus and has a context … click here to read