RichardErkhov/RyanYr_-_reflect_llama8B_llama-mstlrg-om2-80k_sft-t2_lr5e-6-gguf
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Quantization made by Richard Erkhov.
reflectllama8Bllama-mstlrg-om2-80ksft-t2lr5e-6 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflectllama8Bllama-mstlrg-om2-80ksft-t2lr5e-6/
Original model description: --- basemodel: meta-llama/Llama-3.1-8B-Instruct libraryname: transformers modelname: reflectllama8Bllama-mstlrg-om2-80ksft-t2_lr5e-6 tags:
- generatedfromtrainer
- trl
- sft licence: license ---
Model Card for reflectllama8Bllama-mstlrg-om2-80ksft-t2lr5e-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/reflect_llama8B_llama-mstlrg-om2-80k_sft-t2_lr5e-6", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=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}}
}