Oblivion's End

A merged LoRA for gemma-2-9b-it, trained using DPO datasets for creative writing using my DPO training notebook.

Model Details

How to Use

from unsloth import FastLanguageModel
import torch
max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = False # Use 4bit quantization to reduce memory usage. Can be False.

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "mehmetkeremturkcan/oblivionsend",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit
)

from transformers import TextStreamer
FastLanguageModel.for_inference(model)
text_streamer = TextStreamer(tokenizer)

inputs = tokenizer(
[
"""<start_of_turn>user
Write a story with the following description: Setting - a dark abandoned watchtower and its environs. A wizard carefully explores a tomb where a priest of a dark, dead God has raised a band of brigands that have been terrorizing a town."""+ """<end_of_turn>
<start_of_turn>model
"""
], return_tensors = "pt").to("cuda")

_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 4096, num_beams=1, temperature=1.0, do_sample=True)

Model Description

• Finetuned from model: google/gemma-2-9b-it

Model Sources [optional]

• Repository: GitHub.

Uses

Made for creative writing.

Training Details

Training Data

Check out the model card details at HuggingFace.

Training Procedure

Model training performance (margins) are available in the wandb instance.

Training Hyperparameters

• Training regime: bf16 on a 1x 80GB A100 node.

Environmental Impact

Total emissions are estimated to be 0.83 kgCO$_2$eq.