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RichardErkhov/
HPLT_-_sft-fpft-fi-bloom-1b1-gguf

Name: HPLT_-_sft-fpft-fi-bloom-1b1-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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sft-fpft-fi-bloom-1b1 - GGUF

Model creator: https://huggingface.co/HPLT/
Original model: https://huggingface.co/HPLT/sft-fpft-fi-bloom-1b1/

Name	Quant method	Size
sft-fpft-fi-bloom-1b1.Q2_K.gguf	Q2_K	0.66GB
sft-fpft-fi-bloom-1b1.IQ3_XS.gguf	IQ3_XS	0.73GB
sft-fpft-fi-bloom-1b1.IQ3_S.gguf	IQ3_S	0.73GB
sft-fpft-fi-bloom-1b1.Q3_K_S.gguf	Q3_K_S	0.73GB
sft-fpft-fi-bloom-1b1.IQ3_M.gguf	IQ3_M	0.77GB
sft-fpft-fi-bloom-1b1.Q3_K.gguf	Q3_K	0.79GB
sft-fpft-fi-bloom-1b1.Q3_K_M.gguf	Q3_K_M	0.79GB
sft-fpft-fi-bloom-1b1.Q3_K_L.gguf	Q3_K_L	0.82GB
sft-fpft-fi-bloom-1b1.IQ4_XS.gguf	IQ4_XS	0.84GB
sft-fpft-fi-bloom-1b1.Q4_0.gguf	Q4_0	0.87GB
sft-fpft-fi-bloom-1b1.IQ4_NL.gguf	IQ4_NL	0.87GB
sft-fpft-fi-bloom-1b1.Q4_K_S.gguf	Q4_K_S	0.87GB
sft-fpft-fi-bloom-1b1.Q4_K.gguf	Q4_K	0.91GB
sft-fpft-fi-bloom-1b1.Q4_K_M.gguf	Q4_K_M	0.91GB
sft-fpft-fi-bloom-1b1.Q4_1.gguf	Q4_1	0.93GB
sft-fpft-fi-bloom-1b1.Q5_0.gguf	Q5_0	0.99GB
sft-fpft-fi-bloom-1b1.Q5_K_S.gguf	Q5_K_S	0.99GB
sft-fpft-fi-bloom-1b1.Q5_K.gguf	Q5_K	1.02GB
sft-fpft-fi-bloom-1b1.Q5_K_M.gguf	Q5_K_M	1.02GB
sft-fpft-fi-bloom-1b1.Q5_1.gguf	Q5_1	1.05GB
sft-fpft-fi-bloom-1b1.Q6_K.gguf	Q6_K	1.12GB
sft-fpft-fi-bloom-1b1.Q8_0.gguf	Q8_0	1.45GB

Original model description:

language:

fi 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
Paper

Instruction tuning details

Base model: bloom-1b1
Instruction tuning language: Finnish
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. You can download our data HERE.

Usage

The model checkpoint should be loaded using transformers library.

Please refer to our Github repository HERE 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",
}

RichardErkhov/HPLT_-_sft-fpft-fi-bloom-1b1-gguf

Model Description

Instruction tuning details

Usage

Citation

RichardErkhov/
HPLT_-_sft-fpft-fi-bloom-1b1-gguf