Spaces:
Sleeping
Sleeping
File size: 2,331 Bytes
1a3530a 58bdce3 acd4286 1a3530a 58bdce3 1a3530a 104faa8 1a3530a 8166990 1a3530a |
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 |
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import os
from huggingface_hub import login
import spaces
import gradio as gr
import subprocess
# Ensure the required libraries are installed
def install(package):
subprocess.check_call([os.sys.executable, "-m", "pip", "install", package])
# Install transformers, huggingface_hub
install("transformers")
install("huggingface_hub")
token = os.environ.get("HF_TOKEN_READ_LLAMA")
login(token)
model_name = 'meta-llama/Meta-Llama-3.1-8B-Instruct'
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype = torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_name)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = model.to(device)
@spaces.GPU
def response(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
for value in history:
if value[0]:
messages.append({"role": "user", "content": value[0]})
if value[1]:
messages.append({"role": "assistant", "content": value[1]})
messages.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors='pt'
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=max_tokens,
eos_token_id=terminators,
do_sample=True,
temperature=temperature,
top_p=top_p
)
response = ''
for message in tokenizer.decode(
outputs[0][input_ids.shape[-1]:],
skip_special_tokens=True
):
response += message
yield response
demo = gr.ChatInterface(
response,
additional_inputs = [
gr.Textbox(value="You are a friendly assistant", label="System Message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1, value=0.9, step=0.05, label="Top_p"),
]
)
if __name__ == "__main__":
demo.launch() |