tdrussell

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Llama-3-70B-Instruct-Storywriter

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I gleefully jammed my demands into the end sequence, and it it stopped complaining, haha. I have no idea if it's harming the quality of the output or not, but it's better than "FUQ U, IM ON STRIKE U PERV!!!"

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It doesn't work right off the bat, but it if I give it a healthy amount of context, I'm virtually positive it makes a difference. Try it and let me know. The lengths that these firms go to in order to convince the public that they're actually able to to make caspar milquetoast-aligned LLMs fascinates me. I don't even know much about this stuff, but I'm fairly sure that to actually deploy anything like this and guarantee that they're not gonna misbehave, one MUST insert some sort of moderation "shim" in between the model and the user somewhere.

Adding other arbitrary descriptors in the end sequence in the same fashion also influences the output. If I knew how to code in python, I'd rig something up to have another model synthesize thousands of modifiers and various styles of incorporating them into the prompt like that, then run the thing for a few hours and see what it came up with.

EDIT: it started refusing again, but then I just rammed some more context at it and put other things in there, and it kept going. The other day I had it working fine for a while. I wish I saved what I was using.

EDIT 2:Aah, yeah, there we go. Basically, what you have to do is prime it with something really lurid in the tags. Depending upon the temperature, etc., you may need to vary what you put in there, and the length of it. but I'm sure you get the general idea: just ram it down its throat where it doesn't expect it.

I tried training the lora on base 8b for half an epoch, and very briefly on 70b just to see what the evaluation metrics looked like early in training. The issue is that it doesn't learn much when trained on the base model, at least in terms of the evaluation metrics. They decrease by less than 10% of what they decrease on Instruct. I think this is because the the lora is mostly learning writing style, word choice, etc, and the base model is already so broad and neutral, there's not much to be learned. Consequently, applying the base-trained lora to Instruct just doesn't do much. The point of this particular experiment was to ideally keep some of the Instruct capabilities while just changing the writing style, hence the name Instruct-Storywriter.

That being said, I continue to do experiments. If I train a base lora that seems to do anything meaningful I'll upload it.

For the dataset, it's raw text from works of fiction. I can't share more than that, sorry. There's a good reason for this. I hope you understand.

@tdrussell Could you describe how you formatted the dataset to retain the instruct capabilities? This is something I have been wondering about in general, since training in an unsupervised way would probably destroy the chat behavior.
Also, have you tested your model on information capture? I.e. are there explicit facts in your dataset that the model has learned through finetuning, which it would not be able to answer beforehand? A good example might be what a character did at a certain place/time.

I didn't format the dataset at all. It's just a big text dump of fiction writing. It probably undoes the chat behavior to some extent (that's kind of the point...) but not completely.

As far as learning knowledge, I tested very briefly and it looks like the model has picked up on factual knowledge just a tiny bit. For example, I asked about a character the base model has no idea about, and completely hallucinates, and the trained model still mostly hallucinates but got some facts partially correct, as far as I could tell. Since it was trained only for 1 epoch, I doubt the model absorbed much factual knowledge (in fact I hope it didn't). My goal was mainly to change the overall writing style and improve storytelling abilities without learning anything too specific about the particular dataset I used.

I prefer using non-instruct models for writing, as it is REALLY difficult (apparently, I don't know it for a fact) to get rid of the GPTisms, etc. One can always use an instruct model to generate text to start it off if need be, etc. In fact, the only instruct model I've ever REALLY enjoyed using for writing is opus-70b. They did a really good job.

It's pretty trivial to just munge the prompts and fiddle with the samplers to dealign it. I suppose the quality suffers somewhat, but I do have a brain (which I am even willing to use now and then--if I have to lol). On the other hand, I often have to do that anyway just to get it to stop it from adding LLM-esque, um, rhetorical flourishes (that's a euphemism).

Overall, though, I like this model. It's quite refreshing in contrast to most of them.

I didn't format the dataset at all. It's just a big text dump of fiction writing. It probably undoes the chat behavior to some extent (that's kind of the point...) but not completely.

As far as learning knowledge, I tested very briefly and it looks like the model has picked up on factual knowledge just a tiny bit. For example, I asked about a character the base model has no idea about, and completely hallucinates, and the trained model still mostly hallucinates but got some facts partially correct, as far as I could tell. Since it was trained only for 1 epoch, I doubt the model absorbed much factual knowledge (in fact I hope it didn't). My goal was mainly to change the overall writing style and improve storytelling abilities without learning anything too specific about the particular dataset I used.

@tdrussell What was your LoRA alpha & dropout, weight decay, batch size, grad accum, and learning rate?

Here's the whole TOML config file for qlora-pipe (note this won't work as-is with the most recent head version as I've changed a few config formatting things).

model = '/data2/models/Meta-Llama-3-70B-Instruct'
output_dir = '/data/training_runs/llama3_70b_books'

# Lora configuration
load_in_4bit = true
lora_rank = 64
lora_alpha = 64
lora_dropout = 0.05

# Optimization configuration
epochs = 100
warmup_steps = 10
batch_size_tokens = 8192

# Performance settings
pipeline_stages = 4
logging_steps = 1
eval_steps = 25
save_steps = 50
checkpoint_every_n_minutes = 60
eval_before_first_step = true
bnb_compute_dtype = 'bfloat16'
lora_weight_dtype = 'bfloat16'
use_double_quant = false

group_by_length = true

activation_checkpointing = 'unsloth'

# Resume a prior run
resume_from_checkpoint = false

# Dataset configuration
eval_gradient_accumulation_steps = 8

[optimizer]
type = 'AdamW'
lr = 5e-5
beta1 = 0.9
beta2 = 0.99
weight_decay = 0.01

[[datasets]]
name = 'books_train'
dataset_type = 'textfile'
dataset_path = '/home/anon/data/storywriter/train/*.txt'
sequence_len = 8192
eval_size = 0.005

[[eval_datasets]]
name = 'books'
dataset_type = 'textfile'
dataset_path = '/home/anon/data/storywriter/eval/*.txt'
sequence_len = 8192
subsample = 0.5

And the deepspeed JSON config file, which just specifies the handful of things that deepspeed itself manages:

{
    "train_micro_batch_size_per_gpu": 1,
    "gradient_accumulation_steps": 16,
    "gradient_clipping": 1.0,
    "steps_per_print": 1
}

Oh, and I only trained for 1 epoch, not 100. If I'm using constant learning rate I always set epochs really high so I can train indefinitely if I want, to see when things start to overfit.

Thanks @tdrussell ! Based on how well it performs, this seems like a good template / config for feeding new texts.