Delete app_8_5_2024.py

app_8_5_2024.py

@@ -1,150 +0,0 @@
-import gradio as gr
-import transformers 
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import accelerate 
-import einops 
-import langchain
-import xformers
-import os
-import bitsandbytes
-import sentence_transformers
-import huggingface_hub
-import torch
-from torch import cuda, bfloat16
-from transformers import StoppingCriteria, StoppingCriteriaList
-from langchain.llms import HuggingFacePipeline
-from langchain.document_loaders import TextLoader, DirectoryLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains import ConversationalRetrievalChain
-from huggingface_hub import InferenceClient
-
-# Login to Hugging Face using a token
-# huggingface_hub.login(HF_TOKEN)
-
-"""
-Loading of the LLama3 model
-"""
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-model_id = 'meta-llama/Meta-Llama-3-8B'
-device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
-
-
-"""set quantization configuration to load large model with less GPU memory
-this requires the `bitsandbytes` library"""
-bnb_config = transformers.BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type='nf4',
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_compute_dtype=bfloat16
-)
-
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct",token=HF_TOKEN)
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto",token=HF_TOKEN,quantization_config=bnb_config)  # to("cuda:0") 
-terminators = [
-    tokenizer.eos_token_id,
-    tokenizer.convert_tokens_to_ids("<|eot_id|>")
-]
-
-"""CPU"""
-
-# model_config = transformers.AutoConfig.from_pretrained(
-#             model_id,
-#             token=HF_TOKEN,
-#             # use_auth_token=hf_auth
-#         )
-# model = transformers.AutoModelForCausalLM.from_pretrained(
-#             model_id,
-#             trust_remote_code=True,
-#             config=model_config,
-#             # quantization_config=bnb_config,
-#             token=HF_TOKEN,
-#             # use_auth_token=hf_auth
-#         )
-# model.eval()
-# tokenizer = transformers.AutoTokenizer.from_pretrained(
-#             model_id,
-#             token=HF_TOKEN,
-#             # use_auth_token=hf_auth
-#         )
-# generate_text = transformers.pipeline(
-#             model=self.model, tokenizer=self.tokenizer,
-#             return_full_text=True,
-#             task='text-generation',
-#             temperature=0.01,
-#             max_new_tokens=512
-#         )
-
-"""
-Setting up the stop list to define stopping criteria.
-"""
-
-stop_list = ['\nHuman:', '\n```\n']
-
-stop_token_ids = [tokenizer(x)['input_ids'] for x in stop_list]
-stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]
-
-
-# define custom stopping criteria object
-class StopOnTokens(StoppingCriteria):
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        for stop_ids in stop_token_ids:
-            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
-                return True
-        return False
-
-stopping_criteria = StoppingCriteriaList([StopOnTokens()])
-
-
-generate_text = transformers.pipeline(
-    model=model,
-    tokenizer=tokenizer,
-    return_full_text=True,  # langchain expects the full text
-    task='text-generation',
-    # we pass model parameters here too
-    stopping_criteria=stopping_criteria,  # without this model rambles during chat
-    temperature=0.1,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
-    max_new_tokens=512,  # max number of tokens to generate in the output
-    repetition_penalty=1.1  # without this output begins repeating
-)
-
-llm = HuggingFacePipeline(pipeline=generate_text)
-
-loader = DirectoryLoader('data/text/', loader_cls=TextLoader)
-documents = loader.load()
-print('len of documents are',len(documents))
-
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=250)
-all_splits = text_splitter.split_documents(documents)
-
-model_name = "sentence-transformers/all-mpnet-base-v2"
-model_kwargs = {"device": "cuda"}
-
-embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
-
-# storing embeddings in the vector store
-vectorstore = FAISS.from_documents(all_splits, embeddings)
-
-chain = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)
-
-chat_history = []
-def qa_infer(query):
-    result = chain({"question": query, "chat_history": chat_history})
-    print(result['answer'])
-    return result['answer']
-
-# query = "What` is the best TS pin configuration for BQ24040 in normal battery charge mode"
-# qa_infer(query)
-
-EXAMPLES = ["What is the best TS pin configuration for BQ24040 in normal battery charge mode", 
-            "Can BQ25896 support I2C interface?", 
-            "Can you please provide me with Gerber/CAD file for UCC2897A"]
-
-demo = gr.Interface(fn=qa_infer, inputs="text",allow_flagging='never', examples=EXAMPLES,
-                    cache_examples=False,outputs="text")
-
-# launch the app!
-#demo.launch(enable_queue = True,share=True)
-#demo.queue(default_enabled=True).launch(debug=True,share=True)
-demo.launch()