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Rename Create_Instance.py to _Create_Instance.py
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## CREATE MODEL FROM SCRATCH
## TOBE REMOVED
# pip install reportlab
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/_Spydaz_Web_AI_V2_Aligned"
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
special_tokens_to_add = []
if model.use_start_thought_token:
special_tokens_to_add.append("<|startthought|>")
if model.use_end_thought_token:
special_tokens_to_add.append("<|endthought|>")
if special_tokens_to_add:
tokenizer.add_special_tokens({"additional_special_tokens": special_tokens_to_add})
model.resize_token_embeddings(len(tokenizer))
model.tokenizer = tokenizer
for name, module in model.named_modules():
if "embed" in name:
print(module, flush=True)
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,tokenizer
model,tokenizer = model_init(None)
model
tokenizer.save_pretrained("IModel")
model.save_pretrained("IModel")
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
WRITE_TOKEN=""
username = "LeroyDyer"
huggingface_hub.login(WRITE_TOKEN)
api = HfApi(token=WRITE_TOKEN)
MODEL_NAME = "_Spydaz_Web_AI_MistralStar"
Folderinput = "IModel"
# 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
)
import huggingface_hub
from trl import SFTTrainer
from transformers import TrainingArguments
from datasets import load_dataset
from unsloth import FastLanguageModel
import torch
WRITE_TOKEN = ""
username = "LeroyDyer"
huggingface_hub.login(WRITE_TOKEN)
MODEL_ID = "LeroyDyer/_Spydaz_Web_AI_MistralStar"
max_seq_length = 1512 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = MODEL_ID, # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
#token = "", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
model = FastLanguageModel.get_peft_model(
model,
r = 32, # 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"],
lora_alpha = 64,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 644993,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
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
from datasets import load_dataset
dataset = load_dataset("gate369/Alpaca-Star", split = "train[:1000]")
dataset = dataset.shuffle(seed=9969)
dataset = dataset.map(formatting_prompts_func, batched = True,)
from trl import SFTTrainer
from transformers import TrainingArguments
from unsloth import is_bfloat16_supported
from unsloth import UnslothTrainer, UnslothTrainingArguments
trainer = UnslothTrainer(
model = model,
tokenizer = tokenizer,
train_dataset = dataset,
dataset_text_field = "text",
max_seq_length = max_seq_length,
dataset_num_proc = 8,
args = UnslothTrainingArguments(
per_device_train_batch_size = 10,
gradient_accumulation_steps = 8,
warmup_ratio = 0.1,
num_train_epochs = 2,
learning_rate = 2e-4,
embedding_learning_rate = 2e-5,
output_dir = "outputs",
save_strategy = "steps",
save_steps = 50,
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.00,
lr_scheduler_type = "cosine",
seed = 3607,
),
)
trainer_stats = trainer.train()
# Merge to 16bit
if False: model.save_pretrained_merged("LCARS_AI_015", tokenizer, save_method = "merged_16bit",)
if True: model.push_to_hub_merged("_Spydaz_Web_AI_STAR_Aligned", tokenizer, save_method = "merged_16bit", token = "")
# Merge to 4bit
if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit_forced",)
if True: model.push_to_hub_merged("_Spydaz_Web_AI_STAR_Aligned_4_BIT", tokenizer, save_method = "merged_4bit_forced", token = "")
# Just LoRA adapters
if False: model.save_pretrained_merged("model", tokenizer, save_method = "lora",)
if False: model.push_to_hub_merged("Test_Lora", tokenizer, save_method = "lora", token = "")