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-rwxr-xr-xava.py81
1 files changed, 0 insertions, 81 deletions
diff --git a/ava.py b/ava.py
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--- a/ava.py
+++ /dev/null
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-#!/usr/bin/env python
-# : out ava
-# : dep transformers
-# : dep torch
-# : dep accelerate
-# : dep bitsandbytes
-import transformers
-import torch
-import sys
-
-# import sleekxmpp
-
-
-model_name = "lmsys/vicuna-33b-v1.3"
-
-if torch.cuda.is_available():
- device = "cuda:0"
-else:
- raise ValueError("no cuda")
- sys.exit(1)
-
-tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
-model = transformers.AutoModelForCausalLM.from_pretrained(
- model_name,
- device_map="auto",
- load_in_8bit=True,
- pad_token_id=tokenizer.eos_token_id,
- revision="float16",
- torch_dtype=torch.float16,
- low_cpu_mem_usage=True,
-)
-
-# set attention_mask and pad_token_id
-
-
-def gen(txt):
- input_ids = tokenizer(txt, return_tensors="pt").input_ids.to("cuda")
- outputs = model.generate(
- input_ids=input_ids,
- max_length=1024,
- temperature=0.7,
- )
- result = tokenizer.batch_decode(outputs, skip_special_tokens=True)
- result = "".join(result)
- return result
-
-
-# Get user input and generate a response
-while True:
- user_input = input("ben: ")
- response = gen(user_input)
- print("bot: ", response)
-
-
-"""
-# Set up the XMPP client
-client = sleekxmpp.ClientXMPP(
- "ava@simatime.com",
- "test"
-)
-client.connect()
-client.process(block=True)
-
-# Define a function that takes in a user's input and returns a response
-def generate_response(input_text):
- # You would use your language model to generate a response here
- response = "This is a response to the user's input: " + input_text
- return response
-
-# Handle incoming messages
-@client.add_event_handler("message")
-def handle_message(message):
- # Get the user's input
- user_input = message["body"]
-
- # Generate a response
- response = generate_response(user_input)
-
- # Send the response to the user
- message.reply(response).send()
-"""