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RichardErkhov/suehyunpark_-_potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token-gguf

sourceHugging Faceupdated 1y agoView on Hugging Face
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Quantization made by Richard Erkhov.

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potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token - GGUF

  • —Model creator: https://huggingface.co/suehyunpark/
  • —Original model: https://huggingface.co/suehyunpark/potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token/
NameQuant methodSize
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q2_K.ggufQ2_K2.96GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.IQ3_XS.ggufIQ3_XS3.28GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.IQ3_S.ggufIQ3_S3.43GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q3_K_S.ggufQ3KS3.41GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.IQ3_M.ggufIQ3_M3.52GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q3_K.ggufQ3_K3.74GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q3_K_M.ggufQ3KM3.74GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q3_K_L.ggufQ3KL4.03GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.IQ4_XS.ggufIQ4_XS4.18GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q4_0.ggufQ4_04.34GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.IQ4_NL.ggufIQ4_NL4.38GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q4_K_S.ggufQ4KS4.37GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q4_K.ggufQ4_K4.58GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q4_K_M.ggufQ4KM4.58GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q4_1.ggufQ4_14.78GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q5_0.ggufQ5_05.21GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q5_K_S.ggufQ5KS5.21GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q5_K.ggufQ5_K5.34GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q5_K_M.ggufQ5KM5.34GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q5_1.ggufQ5_15.65GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q6_K.ggufQ6_K6.14GB
potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token.Q8_0.ggufQ8_07.95GB

Original model description: --- basemodel: barc0/Llama-3.1-ARC-Potpourri-Induction-8B libraryname: transformers model_name: potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token tags:

  • —generatedfromtrainer
  • —trl
  • —sft licence: license ---

Model Card for potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token

This model is a fine-tuned version of barc0/Llama-3.1-ARC-Potpourri-Induction-8B. It has been trained using TRL.

Quick start

python
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="suehyunpark/potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task-tool-token", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>

This model was trained with SFT.

Framework versions

  • —TRL: 0.12.1
  • —Transformers: 4.46.2
  • —Pytorch: 2.5.1
  • —Datasets: 3.1.0
  • —Tokenizers: 0.20.3

Citations

Cite TRL as:

bibtex
@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}}
}