Update app.py

app.py

@@ -1,132 +1,13 @@
-# import gradio as gr
-# from transformers import AutoModelForCausalLM, AutoTokenizer
-
 from gpt4all import GPT4All
-model = GPT4All("wizardlm-13b-v1.1-superhot-8k.ggmlv3.q4_0.bin")
-
-# #----------------------------------------------------------------------------------------------------------------------------
-# # !pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7
-# # import os
-# import torch
-# from datasets import load_dataset
-# from transformers import (
-#     AutoModelForCausalLM,
-#     AutoTokenizer,
-#     BitsAndBytesConfig,
-#     HfArgumentParser,
-#     TrainingArguments,
-#     pipeline,
-#     logging,
-# )
-# from peft import LoraConfig, PeftModel
-# from trl import SFTTrainer
-#  # -----------------------------------------------------------------------------------------------------------------------------------------------------------------
-
-# # LoRA attention dimension
-# lora_r = 64
-
-# # Alpha parameter for LoRA scaling
-# lora_alpha = 16
-
-# # Dropout probability for LoRA layers
-# lora_dropout = 0.1
-
-# ################################################################################
-# # bitsandbytes parameters
-# ################################################################################
-
-# # Activate 4-bit precision base model loading
-# use_4bit = True
-
-# # Compute dtype for 4-bit base models
-# bnb_4bit_compute_dtype = "float16"
-
-# # Quantization type (fp4 or nf4)
-# bnb_4bit_quant_type = "nf4"
-
-# # Activate nested quantization for 4-bit base models (double quantization)
-# use_nested_quant = False
-
-# # Load the entire model on the GPU 0
-# device_map = {"": 0}
-
-# #----------------------------------------------------------------------------------------------------------------------------------------------------------------------
-# model_name = "DR-DRR/Model_001"
-# model_basename = "pytorch_model-00001-of-00002.bin" # the model is in bin format
 
-# #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-
-# # Load tokenizer and model with QLoRA configuration
-# compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
-
-# bnb_config = BitsAndBytesConfig(
-#     load_in_4bit=use_4bit,
-#     bnb_4bit_quant_type=bnb_4bit_quant_type,
-#     bnb_4bit_compute_dtype=compute_dtype,
-#     bnb_4bit_use_double_quant=use_nested_quant,
-# )
-
-# # Check GPU compatibility with bfloat16
-# if compute_dtype == torch.float16 and use_4bit:
-#     major, _ = torch.cuda.get_device_capability()
-#     if major >= 8:
-#         print("=" * 80)
-#         print("Your GPU supports bfloat16: accelerate training with bf16=True")
-#         print("=" * 80)
-
-# # Load base model
-# model = AutoModelForCausalLM.from_pretrained(
-#     model_name,
-#     quantization_config=bnb_config,
-#     device_map=device_map
-# )
-# model.config.use_cache = False
-# model.config.pretraining_tp = 1
-
-# # Load LLaMA tokenizer
-# tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-# tokenizer.pad_token = tokenizer.eos_token
-# tokenizer.padding_side = "right" # Fix weird overflow issue with fp16 training
-
-# # Load LoRA configuration
-# peft_config = LoraConfig(
-#     lora_alpha=lora_alpha,
-#     lora_dropout=lora_dropout,
-#     r=lora_r,
-#     bias="none",
-#     task_type="CAUSAL_LM",
-# )
-
-# #---------------------------------------------------------------------------------------------------------------------------------------------------------------------
-# # Ignore warnings
-# logging.set_verbosity(logging.CRITICAL)
-
-# Run text generation pipeline with our next model
-# prompt = "What is a large language model?"
-# pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
-# result = pipe(f"<s>[INST] {prompt} [/INST]")
-# print(result[0]['generated_text'])
-
-# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------
-# Ignore warnings
-# logging.set_verbosity(logging.CRITICAL)
-
-# Run text generation pipeline with our next model
-# prompt = "What is a large language model?"
-# pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
-# result = pipe(f"<s>[INST] {prompt} [/INST]")
-# print(result[0]['generated_text'])
+# Specify the local path to the downloaded model file
+model_path = "wizardlm-13b-v1.1-superhot-8k.ggmlv3.q4_0.bin"
 
+# Initialize the model using the local path
+model = GPT4All(model_path)
 
 def generate_text(prompt):
     result = model.generate(prompt)
-    # pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
-    # result = pipe(f"<s>[INST] {prompt} [/INST]")
-    # # prompt = "What is a large language model?"
-    # # input_ids = tokenizer.encode(prompt, return_tensors="pt")
-
-    # output = model.generate(input_ids, max_length=200, num_return_sequences=1)
-    # result = tokenizer.decode(output[0], skip_special_tokens=True)
     return result
 
 text_generation_interface = gr.Interface(