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alvarobartt/UltraCM-13B-GGUF
UltraCM-13B-GGUF is a text generation model from alvarobartt. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
UltraCM-13B is a fine-tuned LLM for completion-critique in order to evaluate LLM outputs on helpfulness, truthfulness, honesty, and to what extent the answer follows the given instructions.
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.gguf49.8 GB · 100%
From the Hugging Face model README
UltraCM-13B is a fine-tuned LLM for completion-critique in order to evaluate LLM outputs on helpfulness, truthfulness, honesty, and to what extent the answer follows the given instructions.
UltraCM-13B is a 13b param LLM that was released by OpenBMB, as part of their paper UltraFeedback: Boosting Language Models with High-quality Feedback.
This model contains the quantized variants using the GGUF format, introduced by the llama.cpp team, and also heavily inspired by TheBloke work on quantizing most of the LLMs out there.
| Name | Quant method | Bits | Size | Max RAM required | Use case |
|---|---|---|---|---|---|
| UltraCM-13b.q4_0.gguf | Q4_0 | 4 | 7.37 GB | 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| UltraCM-13b.q4_k_s.gguf | Q4_K_S | 4 | 7.41 GB | 9.91 GB | small, greater quality loss |
| UltraCM-13b.q4_k_m.gguf | Q4_K_M | 4 | 7.87 GB | 10.37 GB | medium, balanced quality - recommended |
| UltraCM-13b.q5_0.gguf | Q5_0 | 5 | 8.97 GB | 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| UltraCM-13b.q5_k_s.gguf | Q5_K_S | 5 | 8.97 GB | 11.47 GB | large, low quality loss - recommended |
| UltraCM-13b.q5_k_m.gguf | Q5_K_M | 5 | 9.23 GB | 11.73 GB | large, very low quality loss - recommended |
Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
For more information on quantization, I'd highly suggest anyone reading to go check TheBloke out, as well as joining their Discord server.
[More Information Needed]
Since this is only a GGUF-quantization of the original weights, please refer and cite the original authors instead.
@misc{cui2023ultrafeedback,
title={UltraFeedback: Boosting Language Models with High-quality Feedback},
author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun},
year={2023},
eprint={2310.01377},
archivePrefix={arXiv},
primaryClass={cs.CL}
}