chores: clean code + discord update

README.md

@@ -22,7 +22,7 @@
     <a href="https://github.com/csunny/DB-GPT/issues">
       <img alt="Open Issues" src="https://img.shields.io/github/issues-raw/csunny/DB-GPT" />
     </a>
-    <a href="https://discord.gg/jDD5FwHh">
+    <a href="https://discord.gg/BSt7kJBu">
       <img alt="Discord" src="https://dcbadge.vercel.app/api/server/jDD5FwHh?compact=true&style=flat" />
     </a>
     <a href="https://codespaces.new/csunny/DB-GPT">
@@ -31,7 +31,7 @@
   </p>
 
 
-[**简体中文**](README.zh.md) |[**Discord**](https://discord.gg/jDD5FwHh) |[**Documents**](https://db-gpt.readthedocs.io/en/latest/)|[**Wechat**](https://github.com/csunny/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC) 
+[**简体中文**](README.zh.md) |[**Discord**](https://discord.gg/BSt7kJBu) |[**Documents**](https://db-gpt.readthedocs.io/en/latest/)|[**Wechat**](https://github.com/csunny/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC) 
 
 </div>
 
@@ -167,7 +167,7 @@ The MIT License (MIT)
 
 ## Contact Information
 We are working on building a community, if you have any ideas about building the community, feel free to contact us. 
-[![](https://dcbadge.vercel.app/api/server/jDD5FwHh?compact=true&style=flat)](https://discord.gg/jDD5FwHh)
+[![](https://dcbadge.vercel.app/api/server/jDD5FwHh?compact=true&style=flat)](https://discord.gg/BSt7kJBu)
 
 <p align="center">
   <img src="./assets/wechat.jpg" width="300px" />

README.zh.md

@@ -22,7 +22,7 @@
     <a href="https://github.com/csunny/DB-GPT/issues">
       <img alt="Open Issues" src="https://img.shields.io/github/issues-raw/csunny/DB-GPT" />
     </a>
-    <a href="https://discord.gg/jDD5FwHh">
+    <a href="https://discord.gg/BSt7kJBu">
       <img alt="Discord" src="https://dcbadge.vercel.app/api/server/rBgtJW8U?compact=true&style=flat" />
     </a>
     <a href="https://codespaces.new/csunny/DB-GPT">
@@ -30,7 +30,7 @@
     </a>
   </p>
 
-[**English**](README.md)|[**Discord**](https://discord.gg/jDD5FwHh) |[**Documents**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/)|[**微信**](https://github.com/csunny/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC)
+[**English**](README.md)|[**Discord**](https://discord.gg/BSt7kJBu) |[**Documents**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/)|[**微信**](https://github.com/csunny/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC)
 
 </div>
 

pilot/pturning/lora/finetune.py

@@ -1,183 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import json
-import os
-
-import pandas as pd
-import torch
-import transformers
-from datasets import load_dataset
-from peft import (
-    LoraConfig,
-    get_peft_model,
-    get_peft_model_state_dict,
-    prepare_model_for_int8_training,
-)
-from transformers import LlamaForCausalLM, LlamaTokenizer
-
-from pilot.configs.config import Config
-from pilot.configs.model_config import DATA_DIR, LLM_MODEL_CONFIG
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-CUTOFF_LEN = 50
-
-df = pd.read_csv(os.path.join(DATA_DIR, "BTC_Tweets_Updated.csv"))
-
-CFG = Config()
-
-
-def sentiment_score_to_name(score: float):
-    if score > 0:
-        return "Positive"
-    elif score < 0:
-        return "Negative"
-    return "Neutral"
-
-
-dataset_data = [
-    {
-        "instruction": "Detect the sentiment of the tweet.",
-        "input": row_dict["Tweet"],
-        "output": sentiment_score_to_name(row_dict["New_Sentiment_State"]),
-    }
-    for row_dict in df.to_dict(orient="records")
-]
-
-with open(os.path.join(DATA_DIR, "alpaca-bitcoin-sentiment-dataset.json"), "w") as f:
-    json.dump(dataset_data, f)
-
-
-data = load_dataset(
-    "json", data_files=os.path.join(DATA_DIR, "alpaca-bitcoin-sentiment-dataset.json")
-)
-print(data["train"])
-
-BASE_MODEL = LLM_MODEL_CONFIG[CFG.LLM_MODEL]
-model = LlamaForCausalLM.from_pretrained(
-    BASE_MODEL,
-    torch_dtype=torch.float16,
-    device_map="auto",
-    offload_folder=os.path.join(DATA_DIR, "vicuna-lora"),
-)
-
-tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
-tokenizer.pad_token_id = 0
-tokenizer.padding_side = "left"
-
-
-def generate_prompt(data_point):
-    return f"""Blow is an instruction that describes a task, paired with an input that provide future context.
-    Write a response that appropriately completes the request. #noqa:
-
-    ### Instruct:
-    {data_point["instruction"]}
-    ### Input
-    {data_point["input"]}
-    ### Response
-    {data_point["output"]}
-    """
-
-
-def tokenize(prompt, add_eos_token=True):
-    result = tokenizer(
-        prompt,
-        truncation=True,
-        max_length=CUTOFF_LEN,
-        padding=False,
-        return_tensors=None,
-    )
-
-    if (
-        result["input_ids"][-1] != tokenizer.eos_token_id
-        and len(result["input_ids"]) < CUTOFF_LEN
-        and add_eos_token
-    ):
-        result["input_ids"].append(tokenizer.eos_token_id)
-        result["attention_mask"].append(1)
-
-    result["labels"] = result["input_ids"].copy()
-    return result
-
-
-def generate_and_tokenize_prompt(data_point):
-    full_prompt = generate_prompt(data_point)
-    tokenized_full_prompt = tokenize(full_prompt)
-    return tokenized_full_prompt
-
-
-train_val = data["train"].train_test_split(test_size=200, shuffle=True, seed=42)
-
-train_data = train_val["train"].map(generate_and_tokenize_prompt)
-
-val_data = train_val["test_py"].map(generate_and_tokenize_prompt)
-
-# Training
-LORA_R = 8
-LORA_ALPHA = 16
-LORA_DROPOUT = 0.05
-LORA_TARGET_MODULES = [
-    "q_proj",
-    "v_proj",
-]
-
-BATCH_SIZE = 128
-MICRO_BATCH_SIZE = 4
-GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE
-LEARNING_RATE = 3e-4
-TRAIN_STEPS = 300
-OUTPUT_DIR = "experiments"
-
-# We can now prepare model for training
-model = prepare_model_for_int8_training(model)
-config = LoraConfig(
-    r=LORA_R,
-    lora_alpha=LORA_ALPHA,
-    target_modules=LORA_TARGET_MODULES,
-    lora_dropout=LORA_DROPOUT,
-    bias="none",
-    task_type="CAUSAL_LM",
-)
-
-model = get_peft_model(model, config)
-model.print_trainable_parameters()
-
-training_arguments = transformers.TrainingArguments(
-    per_device_train_batch_size=MICRO_BATCH_SIZE,
-    gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
-    warmup_steps=100,
-    max_steps=TRAIN_STEPS,
-    no_cuda=True,
-    learning_rate=LEARNING_RATE,
-    logging_steps=10,
-    optim="adamw_torch",
-    evaluation_strategy="steps",
-    save_strategy="steps",
-    eval_steps=50,
-    save_steps=50,
-    output_dir=OUTPUT_DIR,
-    save_total_limit=3,
-    load_best_model_at_end=True,
-    report_to="tensorboard",
-)
-
-data_collector = transformers.DataCollatorForSeq2Seq(
-    tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
-)
-
-trainer = transformers.Trainer(
-    model=model,
-    train_dataset=train_data,
-    eval_dataset=val_data,
-    args=training_arguments,
-    data_collector=data_collector,
-)
-
-model.config.use_cache = False
-old_state_dict = model.state_dict
-model.state_dict = (
-    lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
-).__get__(model, type(model))
-
-trainer.train()
-model.save_pretrained(OUTPUT_DIR)