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basilepp19_-_bloom-1b7_it-gguf
RichardErkhov/basilepp19_-_bloom-1b7_it-gguf
basilepp19_-_bloom-1b7_it-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.gguf28.8 GB · 100%
At a glance
- Access
- Public
- Created
- Oct 26, 2024
- Updated
- Oct 26, 2024
- SHA
- d4b31876
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
bloom-1b7_it - GGUF
- Model creator: https://huggingface.co/basilepp19/
- Original model: https://huggingface.co/basilepp19/bloom-1b7_it/
| Name | Quant method | Size |
|---|---|---|
| bloom-1b7_it.Q2_K.gguf | Q2_K | 0.98GB |
| bloom-1b7_it.Q3_K_S.gguf | Q3_K_S | 1.1GB |
| bloom-1b7_it.Q3_K.gguf | Q3_K | 1.2GB |
| bloom-1b7_it.Q3_K_M.gguf | Q3_K_M | 1.2GB |
| bloom-1b7_it.Q3_K_L.gguf | Q3_K_L | 1.25GB |
| bloom-1b7_it.IQ4_XS.gguf | IQ4_XS | 1.27GB |
| bloom-1b7_it.Q4_0.gguf | Q4_0 | 1.31GB |
| bloom-1b7_it.IQ4_NL.gguf | IQ4_NL | 1.31GB |
| bloom-1b7_it.Q4_K_S.gguf | Q4_K_S | 1.31GB |
| bloom-1b7_it.Q4_K.gguf | Q4_K | 1.39GB |
| bloom-1b7_it.Q4_K_M.gguf | Q4_K_M | 1.39GB |
| bloom-1b7_it.Q4_1.gguf | Q4_1 | 1.41GB |
| bloom-1b7_it.Q5_0.gguf | Q5_0 | 1.51GB |
| bloom-1b7_it.Q5_K_S.gguf | Q5_K_S | 1.51GB |
| bloom-1b7_it.Q5_K.gguf | Q5_K | 1.57GB |
| bloom-1b7_it.Q5_K_M.gguf | Q5_K_M | 1.57GB |
| bloom-1b7_it.Q5_1.gguf | Q5_1 | 1.61GB |
| bloom-1b7_it.Q6_K.gguf | Q6_K | 1.72GB |
| bloom-1b7_it.Q8_0.gguf | Q8_0 | 2.23GB |
Original model description:
license: bigscience-bloom-rail-1.0 language:
- it
Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->This model is obtained by adapting bloom-1b7 to the Italian language. Among the languages supported by the BLOOM model, there is no Italian, making its use in that context challenging. We adapt the original BLOOM model using the MAD-X language adaptation strategy.
Model Details
Model Description
We adapt the bloom-1b7 to the Italian language using the MAD-X language adaptation strategy. To produce a valuable model, we follow the same procedure proposed in: https://arxiv.org/abs/2212.09535
We use default script parameters and select a sample of 100,000 examples in the Italian language. We decided to sample data from the Filtered Oscar Dataset for the Italian Language released by Sarti.
It is important to underline that when you use the adapted LLM is necessary to use the tokenizer of the adapted model.
- Developed by: Pierpaolo Basile, Pierluigi Cassotti, Marco Polignano, Lucia Siciliani, Giovanni Semeraro. Department of Computer Science, University of Bari Aldo Moro, Italy
- Model type: BLOOM
- Language(s) (NLP): Italian
- License: BigScience BLOOM RAIL 1.0
Citation
Pierpaolo Basile, Pierluigi Cassotti, Marco Polignano, Lucia Siciliani, Giovanni Semeraro. On the impact of Language Adaptation for Large Language Models: A case study for the Italian language using only open resources. Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023).
- Created
- Oct 26, 2024
- Updated
- Oct 26, 2024