MyMoodAI/basicmood

Model Card for Model ID

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel, PeftConfig
import shelve


model_name = "MyMoodAI/basicmood"
adapters_name = "MyMoodAI/basicmood"


torch.cuda.empty_cache()



print(f"Starting to load the model {model_name} into memory")

m = AutoModelForCausalLM.from_pretrained(
    model_name,
    #load_in_4bit=True,
)
print(f"Loading the adapters from {adapters_name}")
m = PeftModel.from_pretrained(m, adapters_name)





tokenizer = AutoTokenizer.from_pretrained("MyMoodAI/basicmood", trust_remote_code=True)

while True:
    mood_input = input("Mood: ")

    inputs = tokenizer("Prompt: %s ### Answer: "%mood_input, return_tensors="pt", return_attention_mask=True)
    outputs = m.generate(**inputs, max_length=24)

    print(tokenizer.batch_decode(outputs)[0])

Train Proccedure at the very bottom

Model Details

Model Description

Classify Guilty, Anxious, Depressed states (low accuracy and rudimentary); trained on a generic dataset from the GEMENI API

• Developed by: Emmanuel Nsanga (space and a communication channel on Slack. provided by (mainly the AI builders Club - thebuilderclub.org), Canberra Deep Learning and the Sydney Startup Hub
• Funded by:**Emmanuel Nsanga & Roy Kwan [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [More Information Needed] This model specifically (only for now) is English
  • License: [More Information Needed] Big Science RAILS
• Finetuned from model [optional]: [More Information Needed] gpt-neo-1.3B

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Neede Model name: 11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz d]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

Risks: Total inaccuracy and sensetive human emotions understanding. (Kudos - 'Crystal Pang')

Limitations: Not a real undestanding of emotions - still need human feeback.

Bias. Out of distrbution bias and model size. (Kudos Leo Chow)

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

GEMENI API Prompts - (Generate a 1000 samples of very simple guilty/anxious/depressed mood states of short sentences)

[More Information Needed]

Training Procedure

SFTTrainer (Kudos - Cheng Yu at Canberra DL)

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

0.0007 loss (improved by HyperParam Opt.)

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed] (Just under roughly 10 hours to fine-tune exactly and/or six months of Google Colab Pro+)
• Cloud Provider: [More Information Needed] Google Colab Pro+
• Compute Region: [More Information Needed] Sydney
• Carbon Emitted: [More Information Needed] Refer to Google Data Centre Emisions management

Technical Specifications [optional]

Trained for under two hours on one Epoch.

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed] Google Colab Pro+, Vultr, AWS

Hardware

V100 High RAM (for Fine-tuning)

CPU (Hardware) - 11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz Octa-core (4GB - 4GB SWAP)

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

Eleuther.ai

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

emmanuel.nsanga@inferencetraining.ai

[More Information Needed]

Framework versions

• PEFT 0.10.0

!pip3 install -q -U google-generativeai


import google.generativeai as genai

GOOGLE_API_KEY = ''



genai.configure(api_key=GOOGLE_API_KEY)



model = genai.GenerativeModel('gemini-pro')


response = model.generate_content("Generate a 1000 samples of very simple guilty mood states of short sentences", stream=True)


response.resolve()
guiltsamples = response.text.split('\n')


response = model.generate_content("Generate a 1000 samples of very simple anxious mood states of short sentences", stream=True)
response.resolve()
anxioussamples = response.text.split('\n')



response = model.generate_content("Generate a 1000 samples of very simple depressed mood states of short sentences", stream=True)
response.resolve()
depressedsamples = response.text.split('\n')



guiltsamples = list(zip(guiltsamples, ["You're feeling guilty" for d in range(len(guiltsamples))]))
anxioussamples = list(zip(anxioussamples, ["You're feeling anxious" for d in range(len(anxioussamples))]))
depressedsamples = list(zip(depressedsamples, ["You're feeling depressed" for d in range(len(depressedsamples))]))
data = guiltsamples + anxioussamples + depressedsamples






from peft import PeftModel
import pandas as pd
import shelve
from datasets import Dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling, BitsAndBytesConfig
from transformers import AutoModelForCausalLM
import torch
from datasets import load_dataset, Dataset
import datasets
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model
from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model

from trl import SFTTrainer, DataCollatorForCompletionOnlyLM

from transformers import GPTNeoXForCausalLM, AutoTokenizer

from transformers import get_scheduler

torch.cuda.empty_cache()

class TrainModel:
    def __init__(self, params, data, accu_epochs):
        self.quant_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_16bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.bfloat16


        )
        self.model = AutoModelForCausalLM.from_pretrained(
                    "EleutherAI/gpt-neo-1.3B",
                    quantization_config=self.quant_config,
                    device_map="auto"
                    )

        self.tokenizer = AutoTokenizer.from_pretrained(
                    "EleutherAI/gpt-neo-1.3B",
                    )
        self.params = params
        self.data = data
        self.epochs = accu_epochs




    def lora_config(self):
        lora_config = LoraConfig(
                r=abs(int(self.params['r']*100)),
                lora_alpha=int(self.params['alpha']),
                target_modules=["Wqkv", "out_proj"],
                lora_dropout=int(self.params['dropout']),
                bias="none",
                task_type="CAUSAL_LM"
        )
        print(self.params['r'], self.params['dropout'], self.params['alpha'])
        return(lora_config)


    def formatting_prompts_func(self, example):
        output_texts = []
        for i in range(len(example['Prompt'])):
            text = f"### Question: {example['Prompt'][i]}\n ### Answer: {example['Completion'][i]}"
            output_texts.append(text)
        return(output_texts)

    def prepare_data(self):
        df = pd.DataFrame(self.data, columns=['Prompt', 'Completion'])
        data = Dataset.from_pandas(df)
        return(data)



    def training(self):
        print(abs(self.params['r'].item()*100))
        bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.float16
        )


        self.model = get_peft_model(self.model, self.lora_config())
        training_arguments = TrainingArguments(
                            optim='paged_adamw_8bit',
                            output_dir="Multi-lingual-finetuned-med-text",
                            per_device_train_batch_size=4,
                            gradient_accumulation_steps=4,
                            lr_scheduler_type="cosine",
                            save_strategy="epoch",
                            logging_steps=100,
                            max_steps=10000,
                            warmup_steps=10,
                            num_train_epochs=self.epochs,
                            fp16=True

        )
        self.tokenizer.pad_token = self.tokenizer.eos_token
        response_template = " ### Answer:"
        collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=self.tokenizer)


        trainer = SFTTrainer(
                model=self.model,
                train_dataset=self.prepare_data(),
                args=training_arguments,
                formatting_func=self.formatting_prompts_func,
                data_collator=collator


        )
        trainer.train()
        trainer.state.log_history
        print(trainer.state.log_history)
        return(trainer.state.log_history[0]['loss'])











class HyperParam:
    def __init__(self):
        self.meana = torch.Tensor([8])
        self.stda = torch.Tensor([0.1])
        self.meanr = torch.Tensor([16.])
        self.stdr = torch.Tensor([1.])
        self.meand = torch.Tensor([.25])
        self.stdd = torch.Tensor([0.01])
        self.lr = 0.5
        self.accu_epochs = 1



    def sample_params(self):
        alpha = torch.distributions.Normal(self.meana.unsqueeze(0), self.stda.unsqueeze(0))
        dropout = torch.distributions.Normal(self.meand.unsqueeze(0), self.stdd.unsqueeze(0))
        r = torch.distributions.Normal(self.meand.unsqueeze(0), self.stdd.unsqueeze(0))
        return({'alpha': alpha.sample(), 'dropout': dropout.sample(), 'r': r.sample()})


    def loss(self):
        Training = TrainModel(self.sample_params(), data, self.accu_epochs)
        loss = Training.training()
        return(12)


    def hyper(self):
        optimizer = torch.optim.Adagrad([self.meanr, self.stdr, self.meana, self.stda, self.meand, self.stdd], self.lr)
        while True:
            scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100)
            scheduler.step()
            params = optimizer.param_groups
            params = params[0]['params']
            optimizer.step(closure=self.loss)
            self.lr = scheduler.get_last_lr()[0]
            self.meanr = params[0]
            self.stdr = params[1]
            self.meana = params[2]
            self.stda = params[3]
            self.meand = params[4]
            self.stdd = params[5]
            self.accu_epochs+=1









Hyper = HyperParam()

Hyper.hyper()