Llama-3-8B-AEZAKMI-run1

My first run, 8192 ctx qlora, trained on AEZAKMI-3_6 dataset. Base seems to not be too slopped but finetune is not great - lots of slopped GPTisms, "It's important to remember" etc. It does seem uncensored though, so if you're not fine with Llama-3-8B-Instruct, this might be an option until more better finetunes come out. ChatML prompt format. Training script below. Took around 8 hours on 3090 Ti via unsloth. Benchmark prompt results can be found in my misc repo

from unsloth import FastLanguageModel
from datasets import Dataset, load_dataset
from dataclasses import dataclass, field
from typing import Dict, Optional
import torch
max_seq_length = 8192 # 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-path-llama-3-8b", # Choose ANY! eg mistralai/Mistral-7B-Instruct-v0.2
    max_seq_length = max_seq_length,
    attn_implementation="flash_attention_2",
    dtype = dtype,
    load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)



#@title Alignment Handbook utils
import os
import re
from typing import List, Literal, Optional

from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk
from datasets.builder import DatasetGenerationError


#DEFAULT_CHAT_TEMPLATE = "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"

from datasets import load_dataset

EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN

dataset = load_dataset("adamo1139/AEZAKMI_v3-6", split = "train")
def formatting_prompts_func(examples):
    convos = examples["conversations"]
    texts = []
    mapper = {"system" : "<|im_start|>system\n", "human" : "<|im_start|>user\n", "gpt" : "<|im_start|>assistant\n"}
    end_mapper = {"system" : "<|im_end|>\n", "human" : "<|im_end|>\n", "gpt" : "<|im_end|>\n"}
    for convo in convos:
        text = "".join(f"{mapper[(turn := x['from'])]} {x['value']}{end_mapper[turn]}" for x in convo)
        texts.append(f"{text}{EOS_TOKEN}") # Since there are multi-turn
        # conversations, I append the EOS_TOKEN at the end of the whole
        # conversation. These conversations always ends with a gpt message.
    return { "text" : texts, }
pass
dataset = dataset.map(formatting_prompts_func, batched = True,)

import pprint
pprint.pprint("""NOT a formatted dataset""")
pprint
pprint.pprint(dataset[250])
pprint.pprint(dataset[260])
pprint.pprint(dataset[270])
pprint.pprint(dataset[280])
pprint.pprint(dataset[290])

# Print sample
pprint.pprint("""formatted dataset""")
pprint.pprint(dataset[250])
pprint.pprint(dataset[260])
pprint.pprint(dataset[270])
pprint.pprint(dataset[280])
pprint.pprint(dataset[290])


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 = 32,
    lora_dropout = 0, # Currently only supports dropout = 0
    bias = "none",    # Currently only supports bias = "none"
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = False,  # We support rank stabilized LoRA
    loftq_config = None, # And LoftQ
)

model.print_trainable_parameters()

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments
from transformers.utils import logging
from trl import SFTTrainer

sft_trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = dataset,
    dataset_text_field = "text",
    max_seq_length = 8192,
    packing=True,
    args = TrainingArguments(
        evaluation_strategy = "no",
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        num_train_epochs = 1.5,
        warmup_steps = 10,
        learning_rate = 0.000095,
        fp16 = not torch.cuda.is_bf16_supported(),
        bf16 = torch.cuda.is_bf16_supported(),
        logging_steps = 1,
        output_dir = "1904-llama-3-8b-aezakmi-intermediate",
        optim = "adamw_8bit",
        weight_decay = 0.0,
        lr_scheduler_type = "cosine",
        seed = 42,
        save_strategy = "steps",
        save_steps = 150,
        save_total_limit = 5,
    ),
)


sft_trainer.train()
model.save_pretrained("1904-llama-3-8b-aezakmi-final") # Local saving