CodeLlama-7B-Instruct-bf16-sharded-ft-v0_01

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• load_in_8bit: False
• load_in_4bit: True
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: False
• bnb_4bit_compute_dtype: float16

Framework versions

• PEFT 0.6.0.dev0

"""

Original file is located at https://colab.research.google.com/drive/1yH0ov1ZDpun6yGi19zE07jkF_EUMI1Bf

Code Credit: Hugging Face

**Dataset Credit: https://twitter.com/Dorialexander/status/1681671177696161794 **

Finetune Llama-2-7b on a Google colab

Welcome to this Google Colab notebook that shows how to fine-tune the recent code Llama-2-7b model on a single Google colab and turn it into a chatbot

We will leverage PEFT library from Hugging Face ecosystem, as well as QLoRA for more memory efficient finetuning

Setup

Run the cells below to setup and install the required libraries. For our experiment we will need accelerate, peft, transformers, datasets and TRL to leverage the recent SFTTrainer. We will use bitsandbytes to quantize the base model into 4bit. We will also install einops as it is a requirement to load Falcon models. """

!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git !pip install -q datasets bitsandbytes einops wandb

"""## Dataset

login huggingface """

import wandb

!wandb login

Initialize WandB

wandb_key=["<API_KEY>"] wandb.init(project="<project_name>", name="<name>" )

login with API

from huggingface_hub import login login()

from datasets import load_dataset

#dataset_name = "timdettmers/openassistant-guanaco" ###Human ,.,,,,,, ###Assistant dataset_name = "nickrosh/Evol-Instruct-Code-80k-v1" #dataset_name = 'AlexanderDoria/novel17_test' #french novels dataset = load_dataset(dataset_name, split="train")

"""## Loading the model"""

import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer

#model_name = "TinyPixel/Llama-2-7B-bf16-sharded" #model_name = "abhinand/Llama-2-7B-bf16-sharded-512MB" model_name= "TinyPixel/CodeLlama-7B-Instruct-bf16-sharded" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, )

model = AutoModelForCausalLM.from_pretrained( model_name, quantization_config=bnb_config, trust_remote_code=True ) model.config.use_cache = False

"""Let's also load the tokenizer below"""

inputs = tokenizer(text, return_tensors="pt", padding="max_length", max_length=max_seq_length, truncation=True).to(device)

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token

from peft import LoraConfig, get_peft_model

lora_alpha = 16 lora_dropout = 0.1 lora_r = 64

peft_config = LoraConfig( lora_alpha=lora_alpha, lora_dropout=lora_dropout, r=lora_r, bias="none", task_type="CAUSAL_LM" )

"""## Loading the trainer

Here we will use the SFTTrainer from TRL library that gives a wrapper around transformers Trainer to easily fine-tune models on instruction based datasets using PEFT adapters. Let's first load the training arguments below. """

from transformers import TrainingArguments

output_dir = "./results" per_device_train_batch_size = 4 gradient_accumulation_steps = 4 optim = "paged_adamw_32bit" save_steps = 100 logging_steps = 10 learning_rate = 2e-4 max_grad_norm = 0.3 max_steps = 100 warmup_ratio = 0.03 lr_scheduler_type = "constant"

training_arguments = TrainingArguments( output_dir=output_dir, per_device_train_batch_size=per_device_train_batch_size, gradient_accumulation_steps=gradient_accumulation_steps, optim=optim, save_steps=save_steps, logging_steps=logging_steps, learning_rate=learning_rate, fp16=True, max_grad_norm=max_grad_norm, max_steps=max_steps, warmup_ratio=warmup_ratio, group_by_length=True, lr_scheduler_type=lr_scheduler_type, )

"""Then finally pass everthing to the trainer"""

from trl import SFTTrainer

max_seq_length = 512

trainer = SFTTrainer( model=model, train_dataset=dataset, peft_config=peft_config, dataset_text_field="output", max_seq_length=max_seq_length, tokenizer=tokenizer, args=training_arguments, )

"""We will also pre-process the model by upcasting the layer norms in float 32 for more stable training"""

for name, module in trainer.model.named_modules(): if "norm" in name: module = module.to(torch.float32)

"""## Train the model You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the __call__ method is faster than using a method to encode the text followed by a call to the pad method to get a padded encoding. Now let's train the model! Simply call trainer.train() """

trainer.train()

"""During training, the model should converge nicely as follows: The SFTTrainer also takes care of properly saving only the adapters during training instead of saving the entire model. """

model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model # Take care of distributed/parallel training model_to_save.save_pretrained("outputs")

lora_config = LoraConfig.from_pretrained('outputs') model = get_peft_model(model, lora_config)

dataset['output']

text = "make a advanced python script to finetune a llama2-7b-bf16-sharded model with accelerator and qlora" device = "cuda:0" inputs = tokenizer(text, return_tensors="pt", padding="max_length", max_length=max_seq_length, truncation=True).to(device) #inputs = tokenizer(text, return_tensors="pt").to(device) outputs = model.generate(**inputs, max_new_tokens=150) print(tokenizer.decode(outputs[0], skip_special_tokens=False))

model.push_to_hub("K00B404/CodeLlama-7B-Instruct-bf16-sharded-ft-v0_01", use_auth_token="<HUGGINGFACE_WRITE-api")