66 lines
1.9 KiB
Markdown
66 lines
1.9 KiB
Markdown
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---
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base_model: DoppelReflEx/L3-8B-R1-WolfCore
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library_name: transformers
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model_name: Looking_Glass-llama
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tags:
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- generated_from_trainer
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- sft
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- unsloth
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- trl
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licence: license
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---
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# Model Card for Looking_Glass-llama
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This was an interm model meant for testing and planned on back merging and continued training.
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I had it on public for a very short time and Quants were made. So keeping it public I guess.
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Trained for thinking but this model is a bit repetative.
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There will be another version of it coming when I get my eq back up and running.
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Recomendation for now. Use a high temp and repetition penalty.
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Uses llama 3 template. Check out my Looking Glass system prompts dataset for example system prompts to use.
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This model is a fine-tuned version of [DoppelReflEx/L3-8B-R1-WolfCore](https://huggingface.co/DoppelReflEx/L3-8B-R1-WolfCore).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="None", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.23.0
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- Transformers: 4.56.2
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- Pytorch: 2.8.0
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- Datasets: 3.6.0
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- Tokenizers: 0.22.1
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## Citations
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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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},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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