Spaces:
Sleeping
Sleeping
File size: 2,816 Bytes
abaea54 c9c4d75 9c2ae81 c9c4d75 2918133 c9c4d75 2918133 c9c4d75 e4b60e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import gradio as gr
from huggingface_hub import login
import torch
# from datasets import Dataset
# from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig, TrainingArguments
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
import torch
# Define the repository where your model is saved
model_repo = "Dumele/viv-updated2" # Replace with your actual repository
# Load the tokenizer from the repository
tokenizer = AutoTokenizer.from_pretrained(model_repo)
# Define the configuration with `disable_exllama` set to True
quantization_config = GPTQConfig(bits=4, disable_exllama=True)
# Load the model with the custom configuration
model = AutoModelForCausalLM.from_pretrained(model_repo, quantization_config=quantization_config)
# Move the model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
from transformers import pipeline
# Create a text generation pipeline
text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
# Define a prompt
prompt = "###Human: Answer this question: What exactly does Viv do?\n###Assistant:"
# Generate text
generated_text = text_generator(prompt, max_length=100, num_return_sequences=1)
# Print the generated text
print(generated_text[0]['generated_text'])
# pip install gradio
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr
# Define the repository where your model is saved
model_repo = "Dumele/viv-updated2" # Replace with your actual repository name
# Load the tokenizer from the repository
tokenizer = AutoTokenizer.from_pretrained(model_repo)
# Define the configuration with `disable_exllama` set to True
quantization_config = GPTQConfig(bits=4, disable_exllama=True)
# Load the model with the custom configuration
model = AutoModelForCausalLM.from_pretrained(model_repo, quantization_config=quantization_config)
# Move the model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Create a text generation pipeline
text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
def generate_response(prompt):
generated_text = text_generator(prompt, max_length=100, num_return_sequences=1)
return generated_text[0]['generated_text']
# Create a Gradio interface
iface = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
outputs="text",
title="Chat with VivBeta",
description="Enter a prompt to interact with the fine-tuned model."
)
iface.launch()
|