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GritLM-7B-KTO - GGUF
- Model creator: https://huggingface.co/GritLM/
- Original model: https://huggingface.co/GritLM/GritLM-7B-KTO/
Name | Quant method | Size |
---|---|---|
GritLM-7B-KTO.Q2_K.gguf | Q2_K | 2.53GB |
GritLM-7B-KTO.IQ3_XS.gguf | IQ3_XS | 2.81GB |
GritLM-7B-KTO.IQ3_S.gguf | IQ3_S | 2.96GB |
GritLM-7B-KTO.Q3_K_S.gguf | Q3_K_S | 2.95GB |
GritLM-7B-KTO.IQ3_M.gguf | IQ3_M | 3.06GB |
GritLM-7B-KTO.Q3_K.gguf | Q3_K | 3.28GB |
GritLM-7B-KTO.Q3_K_M.gguf | Q3_K_M | 3.28GB |
GritLM-7B-KTO.Q3_K_L.gguf | Q3_K_L | 3.56GB |
GritLM-7B-KTO.IQ4_XS.gguf | IQ4_XS | 3.67GB |
GritLM-7B-KTO.Q4_0.gguf | Q4_0 | 3.83GB |
GritLM-7B-KTO.IQ4_NL.gguf | IQ4_NL | 3.87GB |
GritLM-7B-KTO.Q4_K_S.gguf | Q4_K_S | 3.86GB |
GritLM-7B-KTO.Q4_K.gguf | Q4_K | 4.07GB |
GritLM-7B-KTO.Q4_K_M.gguf | Q4_K_M | 4.07GB |
GritLM-7B-KTO.Q4_1.gguf | Q4_1 | 4.24GB |
GritLM-7B-KTO.Q5_0.gguf | Q5_0 | 4.65GB |
GritLM-7B-KTO.Q5_K_S.gguf | Q5_K_S | 4.65GB |
GritLM-7B-KTO.Q5_K.gguf | Q5_K | 4.78GB |
GritLM-7B-KTO.Q5_K_M.gguf | Q5_K_M | 4.78GB |
GritLM-7B-KTO.Q5_1.gguf | Q5_1 | 5.07GB |
GritLM-7B-KTO.Q6_K.gguf | Q6_K | 5.53GB |
GritLM-7B-KTO.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
pipeline_tag: text-generation inference: true license: apache-2.0 datasets:
- GritLM/tulu2
Model Summary
A KTO version of https://huggingface.co/GritLM/GritLM-7B
GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.
- Repository: ContextualAI/gritlm
- Paper: https://arxiv.org/abs/2402.09906
- Logs: https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview
- Script: https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh
Model | Description |
---|---|
GritLM 7B | Mistral 7B finetuned using GRIT |
GritLM 8x7B | Mixtral 8x7B finetuned using GRIT |
Use
The model usage is documented here.
Citation
@misc{muennighoff2024generative,
title={Generative Representational Instruction Tuning},
author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela},
year={2024},
eprint={2402.09906},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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