Model Description

mitya is a model based on Dostoevsky’s renowned works, including but not limited to: The Brothers Karamazov, Crime & Punishment, Demons, etc. It is trained upon the open-sourced model Mistral-7b-v0.3 and comes in 6 distinct sizes and quantizations.

• Developed by: tri282
• Funded by: my sincere adoration for his ideals
• Model type: question-answering
• Language(s) (NLP): English (primary), though it can interpret other languages and answer in English
• Finetuned from model: mistral-7b-v0.3
• Repository: https://github.com/tri282/mitya-0.0
• Inference/Download: https://huggingface.co/tri282/dostoevskyGPT_merged

Intended Cause

before everyone, for everyone and everything

Bias, Risks, and Limitations

this model was initially trained on 7,000 question-answer pairs with LoRA, and later on adapted to its base model. given the limited training examples it was fine-tuned on, expect minor, if not any (for i spitefully claim), errors with regards to its syntax and so on

Other Usage

• Inference:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

path = “tri282/dostoevskyGPT_merged”

tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path)

input_text = “your text here”
inputs = tokenizer(input_text, return_tensors = “pt”)

with torch.no_grad():
____outputs = model.generate(**inputs, max_new_tokens = 250)

output_text = tokenizer.decode(outputs[0], skip_special_tokens = True)
print(output_text)

• Download:

from huggingface_hub import snapshot_download

path = “tri282/dostoevskyGPT_merged”
snapshot_download(repo_id = path, local_dir = “./your_directory_here”)

Training Data

currently propriety

Training Hyperparameters

• Training regime: fp16 mixed precision
• Epochs: 3
• Learning Rate: 2e-4
• Batch Size: 16
• Rank, LoRA Alpha, LoRA Dropout: 64, 96, 0.1

Speeds, Sizes, Times [optional]

this model was trained for 6 hours on Tesla L4 GPU. it is roughly 27GB with float32 precision

Evaluation

Summary

i hold firm awareness of the current limitations with regards to my model, that being said, i had a great time testing it out. i ask nothing but your great expectations on future optimizations and versions

Citations

special thanks to Dostoevsky himself, cordially