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import streamlit as st | |
x = st.slider('Select a value') | |
st.write(x, 'squared is', x * x) | |
''' | |
!pip install git+https://github.com/huggingface/transformers | |
! pip install -q peft accelerate bitsandbytes safetensors | |
import torch | |
from peft import PeftModel | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
import transformers | |
adapters_name = "atharvapawar/flaskCodemistral-7b-mj-finetuned" | |
# model_name = "bn22/Mistral-7B-Instruct-v0.1-sharded" #"mistralai/Mistral-7B-Instruct-v0.1" | |
model_name = "bn22/Mistral-7B-Instruct-v0.1-sharded" | |
device = "cuda" # the device to load the model onto | |
bnb_config = transformers.BitsAndBytesConfig( | |
load_in_4bit=True, | |
bnb_4bit_use_double_quant=True, | |
bnb_4bit_quant_type="nf4", | |
bnb_4bit_compute_dtype=torch.bfloat16 | |
) | |
model = AutoModelForCausalLM.from_pretrained( | |
model_name, | |
load_in_4bit=True, | |
torch_dtype=torch.bfloat16, | |
quantization_config=bnb_config, | |
device_map='auto' | |
) | |
model = PeftModel.from_pretrained(model, adapters_name) | |
#model = model.merge_and_unload() | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
tokenizer.bos_token_id = 1 | |
stop_token_ids = [0] | |
print(f"Successfully loaded the model {model_name} into memory") | |
def MistralModel(prompt, tokenLimit): | |
# text = "Identify the changes made to the given code, Common Weakness Enumeration (CWE) associated with the code, and the severity level of the CWE." | |
# "task": "Translate","source_language": "English","target_language": "French","text_to_translate": "Hello, how are you?" | |
text = "[INST]" + prompt + "[/INST]" | |
# text = "[INST] find code vulnerability [cwe] analysis of following code " + text + "[/INST]" | |
encoded = tokenizer(text, return_tensors="pt", add_special_tokens=False) | |
model_input = encoded | |
model.to(device) | |
generated_ids = model.generate(**model_input, max_new_tokens=tokenLimit, do_sample=True) | |
decoded = tokenizer.batch_decode(generated_ids) | |
# print(decoded[0]) | |
return decoded[0] | |
responses = MistralModel(instruction, 250) | |
print(responses) | |
''' |