base_model, library_name, model_name, tags, licence

base_model	library_name	model_name	tags	licence
DoppelReflEx/L3-8B-R1-WolfCore	transformers	Looking_Glass-llama	generated_from_trainer sft unsloth trl	license

Model Card for Looking_Glass-llama

This was an interm model meant for testing and planned on back merging and continued training. I had it on public for a very short time and Quants were made. So keeping it public I guess. Trained for thinking but this model is a bit repetative. There will be another version of it coming when I get my eq back up and running. Recomendation for now. Use a high temp and repetition penalty. Uses llama 3 template. Check out my Looking Glass system prompts dataset for example system prompts to use.

This model is a fine-tuned version of DoppelReflEx/L3-8B-R1-WolfCore. 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="None", 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: 0.23.0
• Transformers: 4.56.2
• Pytorch: 2.8.0
• Datasets: 3.6.0
• Tokenizers: 0.22.1

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 Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}