gguf arxiv:2309.08958 endpoints_compatible region:us
---
{}
---
Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


sft-fpft-es-bloom-560m - GGUF
- Model creator: https://huggingface.co/HPLT/
- Original model: https://huggingface.co/HPLT/sft-fpft-es-bloom-560m/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [sft-fpft-es-bloom-560m.Q2_K.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q2_K.gguf) | Q2_K | 0.39GB |
| [sft-fpft-es-bloom-560m.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.IQ3_XS.gguf) | IQ3_XS | 0.43GB |
| [sft-fpft-es-bloom-560m.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.IQ3_S.gguf) | IQ3_S | 0.43GB |
| [sft-fpft-es-bloom-560m.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q3_K_S.gguf) | Q3_K_S | 0.43GB |
| [sft-fpft-es-bloom-560m.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.IQ3_M.gguf) | IQ3_M | 0.45GB |
| [sft-fpft-es-bloom-560m.Q3_K.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q3_K.gguf) | Q3_K | 0.46GB |
| [sft-fpft-es-bloom-560m.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q3_K_M.gguf) | Q3_K_M | 0.46GB |
| [sft-fpft-es-bloom-560m.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q3_K_L.gguf) | Q3_K_L | 0.47GB |
| [sft-fpft-es-bloom-560m.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.IQ4_XS.gguf) | IQ4_XS | 0.49GB |
| [sft-fpft-es-bloom-560m.Q4_0.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q4_0.gguf) | Q4_0 | 0.5GB |
| [sft-fpft-es-bloom-560m.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.IQ4_NL.gguf) | IQ4_NL | 0.5GB |
| [sft-fpft-es-bloom-560m.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q4_K_S.gguf) | Q4_K_S | 0.5GB |
| [sft-fpft-es-bloom-560m.Q4_K.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q4_K.gguf) | Q4_K | 0.52GB |
| [sft-fpft-es-bloom-560m.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q4_K_M.gguf) | Q4_K_M | 0.52GB |
| [sft-fpft-es-bloom-560m.Q4_1.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q4_1.gguf) | Q4_1 | 0.53GB |
| [sft-fpft-es-bloom-560m.Q5_0.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q5_0.gguf) | Q5_0 | 0.57GB |
| [sft-fpft-es-bloom-560m.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q5_K_S.gguf) | Q5_K_S | 0.57GB |
| [sft-fpft-es-bloom-560m.Q5_K.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q5_K.gguf) | Q5_K | 0.58GB |
| [sft-fpft-es-bloom-560m.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q5_K_M.gguf) | Q5_K_M | 0.58GB |
| [sft-fpft-es-bloom-560m.Q5_1.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q5_1.gguf) | Q5_1 | 0.6GB |
| [sft-fpft-es-bloom-560m.Q6_K.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q6_K.gguf) | Q6_K | 0.64GB |
| [sft-fpft-es-bloom-560m.Q8_0.gguf](https://huggingface.co/RichardErkhov/HPLT_-_sft-fpft-es-bloom-560m-gguf/blob/main/sft-fpft-es-bloom-560m.Q8_0.gguf) | Q8_0 | 0.82GB |




Original model description:

---
language:
- es
tags:
- generation
- question answering
- instruction tuning
license: cc-by-nc-4.0
---

###  Model Description

This HF repository contains base LLMs instruction tuned (SFT) with full-parameter fine-tuning and then used to study whether monolingual or multilingual instruction tuning is more favourable.
* [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main)
* [Paper](https://arxiv.org/abs/2309.08958)

#### Instruction tuning details
* Base model: [bloom-560m](https://huggingface.co/bloom-560m)
* Instruction tuning language: Spanish
* Training method: full-parameter fine-tuning.
* Best checkpoint: best cross-entropy on a validation set, trained for 3 epochs.
* Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data).

#### Usage
The model checkpoint should be loaded using `transformers` library.

Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/fpft) for inference and training instructions.

#### Citation
```
@inproceedings{chen-etal-2024-monolingual,
  title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}",
  author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield",
  year="2024",
  booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
}
```



