_Train_Model.py

from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline, AutoConfig, BitsAndBytesConfig,AutoConfig
import time
import torch
torch.backends.cuda.matmul.allow_tf32 = True
import random
from datasets import load_dataset
from transformers import TrainingArguments
from trl import SFTTrainer
from peft import LoraConfig
# from accelerate import infer_auto_device_map, init_empty_weights, dispatch_model
from torch.nn import CrossEntropyLoss
torch.autograd.set_detect_anomaly(True)
random_seed = 42
torch.manual_seed(random_seed)
random.seed(random_seed)
# Set the device for each process
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# torch.cuda.set_device(device)



n_ahead_talk_global = 4
n_passes_global = 2
n_ahead_global = 8
n_examples = 0

def model_init(params):
    original = False
    if params is None:
        params = {}
    else:
        params = params.params
    # save params to file
    n_ahead = params.get("n_ahead", n_ahead_global if not original else 1)
    n_ahead_talk = params.get("n_ahead_talk", n_ahead_talk_global if not original else 1)
    n_passes = params.get("n_passes", n_passes_global if not original else 1)
    gumbel_temperature = params.get("gumbel_temperature", 1)
    use_start_thought_token = params.get("use_start_thought_token", True)
    use_end_thought_token = params.get("use_end_thought_token", True)
    include_policy_loss = params.get("include_policy_loss", True)
    gumbel_detach = params.get("gumbel_detach", True)
    merged_talk_heads = params.get("merged_talk_heads", True)
    residual_think_head = params.get("residual_think_head", False)
    optimize_lm_head_only_at_start = params.get("optimize_lm_head_only_at_start", False)

    model_id = "LeroyDyer/SpydazWeb_AGI_MistralStar"
    tokenizer_id = model_id
    print("Loading model")

    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
        max_thoughts=n_ahead + n_ahead_talk + 1,
        merged_talk_heads=merged_talk_heads,
        merged_lm_and_talk_heads=False,
        merged_lm_and_think_heads=True,
        use_concat_talk_head=True,
        use_shallow_think=True,
        use_shallow_talk=False,
        use_complex_think_head=False,
        use_complex_talk_head=True,
        use_weighted_talk_head=True,
        trust_remote_code=True,
        device_map="auto",
    )
    print("Loaded model")

    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, truncation=True, padding_side="right")
    tokenizer.pad_token_id = tokenizer.eos_token_id



    model.gumbel_detach = gumbel_detach
    model.include_policy_loss = include_policy_loss
    model.use_end_thought_token = use_end_thought_token
    model.use_start_thought_token = use_start_thought_token
    model.n_ahead = n_ahead
    model.n_ahead_talk = n_ahead_talk
    model.n_passes = n_passes
    model.residual_think_head = residual_think_head
    model.optimize_lm_head_only_at_start = optimize_lm_head_only_at_start
    model.gumbel_temperature = gumbel_temperature
    model.original_mode = original
    model.config_params = params
    return model
    
model,tokenizer = model_init(None)


## TRAINING : 

peft_config = LoraConfig(
          r = 128, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj","lm_head", "embed_tokens"],
    lora_alpha = 32,
    lora_dropout = 0, # Supports any, but = 0 is optimized
    bias = "none", 
    use_dora=True,
)

from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline, AutoConfig
from datasets import load_dataset
from transformers import TrainingArguments
from trl import SFTTrainer
from peft import LoraConfig

## DATA
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""
EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
def formatting_prompts_func(examples):
    instructions = examples["instruction"]
    inputs       = examples["input"]
    outputs      = examples["output"]
    texts = []
    for instruction, input, output in zip(instructions, inputs, outputs):
        # Must add EOS_TOKEN, otherwise your generation will go on forever!
        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
        texts.append(text)
    return { "text" : texts, }
pass
dataset = load_dataset("gate369/Alpaca-Star", split = "train[:2000]")
dataset = dataset.shuffle(seed=3704)
dataset = dataset.map(formatting_prompts_func, batched = True,)
## TRAIN

max_seq_length = 1024
training_args = TrainingArguments(
    output_dir="./out",
    num_train_epochs=3,
    per_device_train_batch_size=1,
    gradient_checkpointing=False,
    gradient_accumulation_steps=8,
    optim="lion_32bit",
    logging_steps=1,
    save_strategy="steps",
    max_steps=1000,
    bf16=True,
    tf32=False,
    learning_rate=6e-05,
    max_grad_norm=0.3,
    warmup_ratio=0.06,
    lr_scheduler_type="cosine",
    push_to_hub=False,

)
trainer = SFTTrainer(
    args=training_args,
    train_dataset=dataset,
    model=model,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
    dataset_text_field="text",
    peft_config=peft_config,
)
trainer.train()

## SAVE
tokenizer.save_pretrained("SFTTrainerModel")
model.save_pretrained("SFTTrainerModel")


import os
import huggingface_hub
from huggingface_hub import notebook_login
from huggingface_hub import create_repo, HfApi
from huggingface_hub import hf_hub_download
from huggingface_hub import create_repo, HfApi
from huggingface_hub import snapshot_download

MODEL_NAME = "_Spydaz_Web_AI_MistralStar"
Folderinput = "SFTTrainerModel"
WRITE_TOKEN = ""
username = "LeroyDyer"
huggingface_hub.login(WRITE_TOKEN)
api = HfApi(token=WRITE_TOKEN)
# Create empty repo
api.create_repo(
    repo_id = f"{username}/{MODEL_NAME}",
    repo_type="model",
    exist_ok=True,
)

api.upload_folder(
    repo_id = f"{username}/{MODEL_NAME}",
    folder_path = Folderinput
)