MoodChartAI/basicmood

Model Card for Model ID

Model Details

Model Description

Just a way to sample moods of an end-uesr using generic data from the Google GEMENI API

• Developed by: [More Information Needed] inferencetrainingAI, Vultr.com & GitLab, Google Colab, AWS
• Funded by [optional]: [More Information Needed] Crystal P & Emmanuel Nsanga, Roy Kwan
• Shared by [optional]: [More Information Needed]
• Model type: Peft Model
• Language(s) (NLP): [More Information Needed]
• License: MIT
• Finetuned from model [optional]: EleutherAI 1.3B

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Training file included

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

import gc

gc.collect()

model_name = "MoodChartAI/basicmood"
adapters_name = "MoodChartAI/basicmood"


torch.cuda.empty_cache()


os.system("sudo swapoff -a; swapon -a")

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

m = AutoModelForCausalLM.from_pretrained(
    model_name,
    #load_in_4bit=True,
).to(device='cpu:7')

print(f"Loading the adapters from {adapters_name}")
m = PeftModel.from_pretrained(m, adapters_name)


tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B", trust_remote_code=True)



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

    inputs = tokenizer("Prompt: %s Completions: You're feeling"%mood_input, return_tensors="pt", return_attention_mask=True)
    inputs.to(device='cpu:8')
    outputs = m.generate(**inputs, max_length=12)

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

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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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.

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Training Details

Training Data

Generic data from GEMENI API

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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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]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

[More Information Needed] 16GB RAM 8GB sawp memeroy

Software

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Citation [optional]

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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Framework versions

• PEFT 0.8.2