---
library_name: peft
base_model: EleutherAI/gpt-neo-1.3B
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->


```
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

<!-- Provide a longer summary of what this model is. -->

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]

<!-- Provide the basic links for the model. -->

- **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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[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)


<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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)
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 
SFTTrainer (Kudos - Cheng Yu at Canberra DL)
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results


0.0007 loss (improved by HyperParam Opt.)


[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[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()
```