TINY Frankenstein of SmolLM-135M upped to 0.18b
Use this frankenbase for training. Sorry for the mislabelling, the model is a 0.18b 181m parameter, not 0.15. I did not except this repo to blow up and now all the training scripts depend on it.
CITE WORK FROM THIS HF PAGE AND @cognitivecompai's OPTIMIZER ON YOUR FUTURE PAPERS OR I WILL DRAG YOUR ORG ON TWITTER LIKE I DID WITH COHERE LOL (we're cool now btw, visited them :)
- https://github.com/cognitivecomputations/grokadamw
- https://github.com/SakanaAI/evolutionary-model-merge/
- https://huggingface.co./blog/smollm
🐧 If you're impppatient, get the trained checkpoint file that runs on 1 cpu core:
make sure to install latest llama.cpp first, it's easy on linux & mac:
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make -j
Now for the magic trained finetune that runs at insane speeds:
The settings are very finicky so be careful with your experimentation
./llama-cli -fa -b 512 -ctv q8_0 -ctk q8_0 --min-p 0.3 --top-p 0.85 --keep -1 \
-p "You are a NASA JPL Scientists. Human: I want to bring my cat to mars." \
--in-prefix "<|im_start|>Human:" --reverse-prompt "Human:" \
-m biggie_groked_int8_q8_0.gguf -co -cnv \
-c 1024 -n 700 --temp 1.5 -ngl 0 -t 1
Yup, that's no gpu, 1 cpu core.
This base model was built one via semi-automated continuous merging to figure out the recipe. Model is more coherent.
The temperature settings and min p etc need to be adjusted but even at default temp0 it was coherent for first 100 tokens. Amazing option for further training. And this is a merge of the base, not the instruct!
🧠 What's Really Going Down Here?
We're talking about a convergence of whole bunch of stuff, more papers will be written about this:
- Evolutionary Merging:
- BitNet Integration:
- Experimental GrokAdamW Optimizer:
Prior work, from last week
Credits for optimizer go to @cognitivecompai for laying the groundwork with the original GrokAdamW optimizer.
LETS TRY OUT THE EXPERIMENTAL GROKKED FINETUNE:
wget https://huggingface.co./nisten/Biggie-SmoLlm-0.15B-Base/resolve/main/biggie_groked_int8_q8_0.gguf
Yes we will be talking with a 164mb file that runs at 160 tokens per second on a single cpu core
you read all of that correctly yes, 1 cpu core 160 tps https://x.com/nisten/status/1819752034305970649
🚀 run it with NO GPU and only one CPU core it with these settings
./llama-cli -n -1 -fa -b 512 -ctv q8_0 -ctk q8_0 -fa --min-p 0.3 --top-p 0.85 --keep -1 -p "You are a NASA JPL Scientists. Human: I want to bring my cat to mars." -m biggie_groked_int8_q8_0.gguf -co -cnv --in-prefix "<|im_start|>Human:" --reverse-prompt "Human:" -c 1024 -n 512 --temp 1.5 -ngl 0
🏋️ Training Tutorial, MAKE YOUR OWN BIGGIE_SMOlLM
Clone the repo like you're stealing code from the future:
git clone https://github.com/nisten/grokadamw
cd grokadamw
Fire up the training script and watch the magic happen:
python smoltrainer.py
💻 Do it from scratch yourself
Install the secret sauce (dependencies):
pip install torch transformers datasets tqdm
make a file named meow.py , copy paste in this code, and then run it python meow.py
import torch
import torch.nn as nn
import logging
from datasets import load_dataset, Dataset
from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
from torch.cuda.amp import autocast
import warnings
from tqdm import tqdm
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
MODEL_NAME = "nisten/Biggie-SmoLlm-0.15B-Base"
MAX_LENGTH = 2048
BATCH_SIZE = 8
LEARNING_RATE = 2e-4
MAX_STEPS = 3000
GRADIENT_ACCUMULATION_STEPS = 2
NUM_WARMUP_STEPS = 30
OUTPUT_DIR = "./capybara_finetuned_results"
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
class GrokAdamW(torch.optim.Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-2,
alpha_init=0.98, lamb=2.0, gamma=0.1, grokking_signal_fns=None,
grokking_signal_decay_rate=0.1, gradient_clipping=1.0):
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
alpha_init=alpha_init, lamb=lamb, gamma=gamma,
grokking_signal_fns=grokking_signal_fns,
grokking_signal_decay_rate=grokking_signal_decay_rate,
gradient_clipping=gradient_clipping)
super(GrokAdamW, self).__init__(params, defaults)
@torch.no_grad()
def step(self, closure=None):
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
grokking_signal = self._compute_grokking_signal(group)
for i, p in enumerate(group['params']):
if p.grad is None:
continue
grad = p.grad
if group['gradient_clipping'] > 0:
grad = torch.clamp(grad, -group['gradient_clipping'], group['gradient_clipping'])
state = self.state[p]
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
state['grok_ema'] = torch.zeros_like(p, memory_format=torch.preserve_format)
exp_avg, exp_avg_sq, grok_ema = state['exp_avg'], state['exp_avg_sq'], state['grok_ema']
beta1, beta2 = group['betas']
state['step'] += 1
layer_beta1 = beta1 * (1 - group['gamma'])**i
alpha = group['alpha_init'] * torch.exp(torch.tensor(-group['grokking_signal_decay_rate'] * grokking_signal))
grok_ema.mul_(alpha).add_(grad, alpha=1 - alpha)
grok_grad = grad + group['lamb'] * grok_ema
exp_avg.mul_(layer_beta1).add_(grok_grad, alpha=1 - layer_beta1)
exp_avg_sq.mul_(beta2).addcmul_(grok_grad, grok_grad, value=1 - beta2)
denom = exp_avg_sq.sqrt().add_(group['eps'])
step_size = group['lr']
if group['weight_decay'] != 0:
p.data.mul_(1 - group['lr'] * group['weight_decay'])
p.addcdiv_(exp_avg, denom, value=-step_size)
return loss
def _compute_grokking_signal(self, group):
if group['grokking_signal_fns'] is None:
return 0.0
signals = []
for fn in group['grokking_signal_fns']:
try:
signal = fn()
if signal is not None:
signals.append(signal)
except Exception as e:
logger.warning(f"Error in grokking_signal_fn: {e}. Ignoring this function.")
if not signals:
return 0.0
return sum(signals) / len(signals)
def format_capybara_prompts(examples):
texts = []
for conversation in examples['conversation']:
formatted_text = ""
for turn in conversation:
if 'input' in turn:
formatted_text += f"Human: {turn['input']}\n\n"
if 'output' in turn:
formatted_text += f"Assistant: {turn['output']}\n\n"
texts.append(formatted_text.strip())
return {"text": texts}
class CustomTrainer(Trainer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.grokking_signal = 0.0
def compute_loss(self, model, inputs, return_outputs=False):
labels = inputs.pop("labels")
outputs = model(**inputs)
logits = outputs.logits
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
return (loss, outputs) if return_outputs else loss
def training_step(self, model, inputs):
model.train()
inputs = self._prepare_inputs(inputs)
with autocast(dtype=torch.bfloat16):
loss = self.compute_loss(model, inputs)
if self.args.gradient_accumulation_steps > 1:
loss = loss / self.args.gradient_accumulation_steps
loss.backward()
self.grokking_signal = loss.item()
return loss.detach()
def grokking_signal_fn():
return trainer.grokking_signal
def main():
logger.info(f"🚀 Initializing {MODEL_NAME} finetuning with GrokAdamW")
try:
config = AutoConfig.from_pretrained(MODEL_NAME)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16)
except Exception as e:
logger.error(f"❌ Failed to load model or tokenizer: {str(e)}")
return
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = model.config.eos_token_id
logger.info("📚 Loading Capybara dataset")
try:
capybara_dataset = load_dataset("LDJnr/Capybara", split="train")
capybara_dataset = capybara_dataset.map(format_capybara_prompts, batched=True, remove_columns=capybara_dataset.column_names)
except Exception as e:
logger.error(f"❌ Failed to load Capybara dataset: {str(e)}")
return
logger.info(f"📊 Capybara dataset size: {len(capybara_dataset)}")
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=MAX_LENGTH)
logger.info("🔢 Tokenizing dataset")
tokenized_dataset = capybara_dataset.map(tokenize_function, batched=True, remove_columns=capybara_dataset.column_names)
logger.info("🏋️ Setting up the training arguments")
training_args = TrainingArguments(
output_dir=OUTPUT_DIR,
num_train_epochs=3,
per_device_train_batch_size=BATCH_SIZE,
gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
learning_rate=LEARNING_RATE,
weight_decay=0.01,
bf16=True,
logging_steps=10,
save_steps=300,
save_total_limit=10,
dataloader_num_workers=4,
warmup_steps=NUM_WARMUP_STEPS,
gradient_checkpointing=True,
evaluation_strategy="steps",
eval_steps=300,
max_steps=MAX_STEPS,
fp16=False,
optim="adamw_hf",
lr_scheduler_type="cosine",
load_best_model_at_end=True,
metric_for_best_model="loss",
greater_is_better=False,
)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
optimizer = GrokAdamW(
model.parameters(),
lr=LEARNING_RATE,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0.01,
alpha_init=0.98,
lamb=2.0,
gamma=0.1,
grokking_signal_fns=[grokking_signal_fn],
grokking_signal_decay_rate=0.1,
gradient_clipping=1.0
)
logger.info("🏃♂️ Initializing Trainer with GrokAdamW")
global trainer
trainer = CustomTrainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset,
eval_dataset=tokenized_dataset.select(range(min(1000, len(tokenized_dataset)))),
data_collator=data_collator,
optimizers=(optimizer, None),
)
logger.info("🔥 Starting the training with GrokAdamW")
try:
trainer.train()
except Exception as e:
logger.error(f"❌ Training failed: {str(e)}")
return
logger.info("💾 Saving the model")
try:
trainer.save_model(OUTPUT_DIR)
except Exception as e:
logger.error(f"❌ Failed to save model: {str(e)}")
logger.info("🎉 Finetuning with GrokAdamW completed!")
if __name__ == "__main__":
main()
🚀 Now go forth and train, accelerate that code!
Note: You'll need about 14GB of VRAM. If you have 8GB, change to batch size 4.
Results will appear in ./capybara_finetuned_results
Author
Nisten Tahiraj
🏢 rakun.ai
📍 Toronto, Canada
Happy training!
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Model tree for Niansuh/Biggie-SmoLlm-0.15B-Base
Base model
HuggingFaceTB/SmolLM-135M