import torch
from transformers import pipeline, AutoTokenizer, TextStreamer
import re
tokenizer = AutoTokenizer.from_pretrained("TheBossLevel123/TinyAITA")
pipe = pipeline("text-generation", model="TheBossLevel123/TinyAITA", torch_dtype=torch.bfloat16, device_map="auto")

streamer=TextStreamer(tokenizer)

prompt = 'AITA for XYZ?'
outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.9, streamer=streamer, eos_token_id=tokenizer.encode("<|im_end|>"))
if outputs and "generated_text" in outputs[0]:
    text = outputs[0]["generated_text"]
    print(f"Prompt: {prompt}")
    print("")
    print(text)

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
training_steps: 200
mixed_precision_training: Native AMP

Training results

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

theminji/TinyAITA

TinyAITA

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

theminji/
TinyAITA