!git clone https://github.com/facebookresearch/llama.git

!bash download.sh

!pip install git https://github.com/huggingface/transformerscd transformerspython convert_llama_weights_to_hf.py \ --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir models_hf/7B

!git clone https://github.com/facebookresearch/llama-recipes.git

!pip install jupyterlabjupyter lab # in the repo you want to work in

model_id="./models_hf/7B"

import torchfrom transformers
import LlamaForCausalLM, LlamaTokenizerfrom peft import PeftModel, PeftConfig

model_id="./models_hf/7B"tokenizer = LlamaTokenizer.from_pretrained(model_id)model =LlamaForCausalLM.from_pretrained(model_id, load_in_8bit=True, device_map='auto', torch_dtype=torch.float16)

model = PeftModel.from_pretrained(model, "/root/llama-recipes/samsungsumarizercheckpoint")

eval_prompt = """Summarize this dialog:A: Hi Tom, are you busy tomorrow’s afternoon?B: I’m pretty sure I am. What’s up?A: Can you go with me to the animal shelter?.B: What do you want to do?A: I want to get a puppy for my son.B: That will make him so happy.A: Yeah, we’ve discussed it many times. I think he’s ready now.B: That’s good. Raising a dog is a tough issue. Like having a baby ;-)A: I'll get him one of those little dogs.B: One that won't grow up too big;-)A: And eat too much;-))B: Do you know which one he would like?A: Oh, yes, I took him there last Monday. He showed me one that he really liked.B: I bet you had to drag him away.A: He wanted to take it home right away ;-).B: I wonder what he'll name it.A: He said he’d name it after his dead hamster – Lemmy - he's a great Motorhead fan :-)))---Summary:"""
model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda")
model.eval()with torch.no_grad(): print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))

from llmengine import FineTuneresponse = FineTune.create( model="llama-2-7b", training_file="s3://my-bucket/path/to/training-file.csv",)
print(response.json())

!pip install datasets==2.13.1 smart_open[s3]==5.2.1 pandas==1.4.4

from datasets import load_datasetfrom smart_open import smart_openimport pandas as pd
dataset = load_dataset('derek-thomas/ScienceQA')dataset['train'].features

Context: A baby wants to know what is inside of a cabinet. Her hand applies a force to the door, and the door opens.Question: Which type of force from the baby's hand opens the cabinet door?Options: (A) pull (B) pushAnswer: A.

choice_prefixes = [chr(ord('A') + i) for i in range(26)] # A-Zdef format_options(options, choice_prefixes): return ' '.join([f'({c}) {o}' for c, o in zip(choice_prefixes, options)])

def format_prompt(r, choice_prefixes): 
    options = format_options(r['choices'], choice_prefixes) 
    return f'''Context: {r["hint"]}\nQuestion: {r["question"]}\nOptions:{options}\nAnswer:'''
def format_response(r, choice_prefixes):
    return choice_prefixes[r['answer']]

def convert_dataset(ds): 
    prompts = [format_prompt(i, choice_prefixes) for i in ds if i['hint'] != '']
        labels = [format_response(i, choice_prefixes) for i in ds if i['hint'] != ''] 
        df = pd.DataFrame.from_dict({'prompt': prompts, 'response': labels}) 
        return df

train_url = 's3://...'val_url = 's3://...'df_train = convert_dataset(dataset['train'])with smart_open(train_url, 'wb') as f: df_train.to_csv(f)df_val = convert_dataset(dataset['validation'])with smart_open(val_url, 'wb') as f:df_val.to_csv(f)

!pip install scale-llm-engine

import os
os.environ['SCALE_API_KEY'] = 'xxx'

from llmengine import FineTuneresponse = FineTune.create( model="llama-2-7b", training_file=train_url, validation_file=val_url, hyperparameters={ 'lr':2e-4, }, suffix='science-qa-llama')run_id = response.fine_tune_id

while True: job_status = FineTune.get(run_id).status # Returns one of `PENDING`, `STARTED`, `SUCCESS`, `RUNNING`, # `FAILURE`, `CANCELLED`, `UNDEFINED` or `TIMEOUT` print(job_status) if job_status == 'SUCCESS': break time.sleep(60)#Logs for completed or running jobs can be fetched withlogs = FineTune.get_events(run_id)

ft_model = FineTune.get(run_id).fine_tuned_model

import pandas as pd
#Helper a function to get outputs for fine-tuned model with retriesdef 
 get_output(prompt: str, num_retry: int = 5): for _ in range(num_retry): try: response = Completion.create( model=ft_model, prompt=prompt, max_new_tokens=1, temperature=0.01 ) return response.output.text.strip() except Exception as e: print(e) return ""
#Read the test datatest = pd.read_csv(val_url)
test["prediction"] = test["prompt"].apply(get_output)
print(f"Accuracy: {(test['response'] == test['prediction']).mean() * 100:.2f}%")