File size: 2,421 Bytes
5fd0c28 f613acc 5fd0c28 8a91905 f613acc 5fd0c28 f613acc 5fd0c28 f613acc 5fd0c28 f613acc 5fd0c28 f613acc 5fd0c28 f613acc 5fd0c28 f613acc 5fd0c28 f613acc 5fd0c28 f613acc 5fd0c28 f613acc 5fd0c28 f613acc 5fd0c28 f613acc |
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 87 88 89 |
import gradio as gr
from unsloth import FastLanguageModel
import torch
# Load your model and tokenizer (make sure to adjust the path to where your model is stored)
max_seq_length = 2048 # Adjust as necessary
load_in_4bit = True # Enable 4-bit quantization for reduced memory usage
model_path = "/content/drive/My Drive/llama_lora_model_1" # Path to your custom model
# Load the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_path,
max_seq_length=max_seq_length,
load_in_4bit=load_in_4bit,
)
# Move model to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# Respond function
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Prepare the system message
messages = [{"role": "system", "content": system_message}]
# Add history to the messages
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Add the current message from the user
messages.append({"role": "user", "content": message})
# Prepare the inputs for the model
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to(device)
# Generate the response using your model
outputs = model.generate(
input_ids=inputs["input_ids"],
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
use_cache=True,
)
# Decode the generated output
response = tokenizer.batch_decode(outputs, skip_special_tokens=True)
# Return the response
return response[0]
# Gradio interface setup
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch()
|