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