Delete LOAD_THIS_MODEL.py

LOAD_THIS_MODEL.py

@@ -1,126 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline, AutoConfig, BitsAndBytesConfig
-
-
-
-
-import torch
-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
-    model.run_start = int(time.time())
-    model.train()
-    return model,tokenizer
-
-model,tokenizer = model_init(None)
-tokenizer.save_pretrained("IModel")
-model.save_pretrained("IModel")
-
-
-
-
-
-from datasets import load_dataset
-
-### DATA SET FOR TRAINING
-
-
-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[:2000]")
-dataset = dataset.shuffle(seed=3704)
-dataset = dataset.map(formatting_prompts_func, batched = True,)