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---
library_name: peft
license: afl-3.0
datasets:
- nickrosh/Evol-Instruct-Code-80k-v1
---
## 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`](https://huggingface.co/docs/trl/main/en/sft_trainer). We will use `bitsandbytes` to [quantize the base model into 4bit](https://huggingface.co/blog/4bit-transformers-bitsandbytes). 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](https://huggingface.co/docs/trl/main/en/sft_trainer) 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")
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