README.md

---
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")