Repro unsloth full finetuning

gistfile1.txt

# Based on https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb
# but with `full_finetuning=True` and without `get_peft_model`
# Output is at the bottom of the gist

import os
from unsloth import FastLanguageModel
from transformers import TextStreamer
import torch

max_seq_length = 2048
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = torch.bfloat16,
    load_in_4bit = False,
    full_finetuning = True,
)

# ============
#  Data prep |
# ============

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("yahma/alpaca-cleaned", split = "train")
dataset = dataset.map(formatting_prompts_func, batched = True,)


# ========
#  Train |
# ========

from trl import SFTConfig, SFTTrainer
trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = dataset,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    packing = False,
    args = SFTConfig(
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 5,
        max_steps = 60,
        learning_rate = 2e-4,
        logging_steps = 1,
        optim = "adamw_8bit",
        weight_decay = 0.01,
        lr_scheduler_type = "linear",
        seed = 3407,
        output_dir = "outputs",
        report_to = "none", # Use this for WandB etc
    ),
)
trainer_stats = trainer.train()


# ===========
#  Generate |
# ===========

inputs = tokenizer(
[
    alpaca_prompt.format(
        "Continue the fibonnaci sequence.", # instruction
        "1, 1, 2, 3, 5, 8", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)


# ==========
# | Output |
# ==========

# ### Instruction:
# Continue the fibonnaci sequence.
#
# ### Input:
# 1, 1, 2, 3, 5, 8
#
# ### Response:
# The fibonnaci sequence is a sequence of numbers that is used in mathematics, computer programs, computer programs, and computer games to solve problems, solve systems of linear equations, and even to generate a sequence of numbers. The sequence is calculated as follows:
#
# 1. Create an empty sequence `a`.
# 2. For every i in range(n):
#     for j in range(n):
#  1. For i in range(n):
#  2. For j in range(n):
#  3. For j in range(n):
#  4. For j in range(n):
#  5. For j in range(n):
#  6. For j in range