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# 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'])
def generate_text(prompt):
# output = model.generate(input_text)
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(f"<s>[INST] {prompt} [/INST]")
return result
text_generation_interface = gr.Interface(
fn=generate_text,
inputs=[
gr.inputs.Textbox(label="Input Text"),
],
outputs=gr.outputs.Textbox(label="Generated Text"),
title="GPT-4 Text Generation",
).launch()
# model_name = ""
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