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--- |
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license: apache-2.0 |
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library_name: transformers |
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base_model: BSC-LT/salamandra-2b |
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pipeline_tag: text-generation |
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language: |
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- bg |
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- ca |
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- code |
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- cs |
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- cy |
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- da |
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- de |
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- el |
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- en |
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- es |
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- et |
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- eu |
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- fi |
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- fr |
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- ga |
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- gl |
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- hr |
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- hu |
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- it |
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- lt |
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- lv |
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- mt |
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- nl |
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- nn |
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- \no |
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- oc |
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- pl |
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- pt |
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- ro |
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- ru |
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- sh |
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- sk |
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- sl |
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- sr |
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- sv |
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- uk |
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--- |
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# Salamandra-2b-gptq Model Card |
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This model is the gptq-quantized version of [Salamandra-2b](https://huggingface.co/BSC-LT/salamandra-2b) for speculative decoding. |
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The model weights are quantized from FP16 to W4A16 (4-bit weights and FP16 activations) using the [GPTQ](https://arxiv.org/abs/2210.17323) algorithm. |
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Inferencing with this model can be done using [VLLM](https://docs.vllm.ai/en/stable/models/engine_args.html). |
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Salamandra is a highly multilingual model pre-trained from scratch that comes in three different |
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sizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants, |
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promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/) |
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and the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU |
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within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337. |
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This model card corresponds to the gptq-quantized version of Salamandra-2b for speculative decoding. |
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The entire Salamandra family is released under a permissive [Apache 2.0 license]((https://www.apache.org/licenses/LICENSE-2.0)). |
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## How to Use |
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The following example code works under ``Python 3.9.16``, ``vllm==0.6.3.post1``, ``torch==2.4.0`` and ``torchvision==0.19.0``, though it should run on |
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any current version of the libraries. This is an example of how to create a text completion using the model: |
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``` |
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from vllm import LLM, SamplingParams |
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model_name = "BSC-LT/salamandra-2b-base-gptq" |
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llm = LLM(model=model_name) |
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outputs = llm.generate("El mercat del barri ", |
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sampling_params=SamplingParams( |
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temperature=0.5, |
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max_tokens=200) |
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) |
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print(outputs[0].outputs[0].text) |
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``` |
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### Author |
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International Business Machines (IBM). |
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### Copyright |
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International Business Machines (IBM). |
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### Contact |
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For further information, please send an email to <[email protected]>. |
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### Acknowledgements |
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We appreciate the collaboration with IBM in this work. |
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Specifically, the IBM team created gptq-quantized version of the Salamandra-2b model for speculative decoding released here. |
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### Disclaimer |
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Be aware that the model may contain biases or other unintended distortions. |
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When third parties deploy systems or provide services based on this model, or use the model themselves, |
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they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable |
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regulations, including those governing the use of Artificial Intelligence. |
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Barcelona Supercomputing Center and International Business Machines shall |
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not be held liable for any outcomes resulting from third-party use. |
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### License |
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[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
