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_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 = "")