Quantization made by Richard Erkhov.

Github

Discord

Request more models

reflectllama8Bllama-mstlrg-om2-20ksft-t2lr1e-6 - GGUF

• Model creator: https://huggingface.co/RyanYr/
• Original model: https://huggingface.co/RyanYr/reflectllama8Bllama-mstlrg-om2-20ksft-t2lr1e-6/

Original model description:

• generatedfromtrainer
• trl
• sft

Model Card for reflectllama8Bllama-mstlrg-om2-20ksft-t2lr1e-6

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct.
It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="RyanYr/reflectllama8Bllama-mstlrg-om2-20ksft-t2lr1e-6", device="cuda")
output = generator([{"role": "user", "content": question}], maxnewtokens=128, returnfulltext=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

• TRL: 0.12.0.dev0
• Transformers: 4.45.2
• Pytorch: 2.5.1
• Datasets: 3.1.0
• Tokenizers: 0.20.3

Citations

Cite TRL as:

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}