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BSC-LT
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salamandra-7b-base-gptq

Text Generation
Transformers
Safetensors
llama
text-generation-inference
4-bit precision
gptq
🇪🇺 Region: EU
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  1. README.md +0 -108
  2. config.json +0 -54
  3. generation_config.json +0 -6
  4. model-00001-of-00002.safetensors +0 -3
  5. model-00002-of-00002.safetensors +0 -3
  6. model.safetensors.index.json +0 -970
  7. special_tokens_map.json +0 -23
  8. tokenizer.json +0 -3
  9. tokenizer.model +0 -3
  10. tokenizer_config.json +0 -1098
README.md DELETED
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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-7b
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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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-
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/633b489acbdbadd99c0b75ef/cIxiiJ5RdoHs85zjDCbEm.png)
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-
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- # Salamandra-7b-gptq Model Card
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-
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- This model is the gptq-quantized version of [Salamandra-7b](https://huggingface.co/BSC-LT/salamandra-7b) for speculative decoding.
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-
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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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-
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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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-
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- This model card corresponds to the gptq-quantized version of Salamandra-7b for speculative decoding.
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-
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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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-
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-
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- ## How to Use
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-
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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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- ```
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- from vllm import LLM, SamplingParams
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-
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- model_name = "BSC-LT/salamandra-7b-base-gptq"
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- llm = LLM(model=model_name)
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-
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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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- ```
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-
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- ### Author
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- International Business Machines (IBM).
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-
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- ### Copyright
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- International Business Machines (IBM).
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-
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- ### Contact
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- For further information, please send an email to <[email protected]>.
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-
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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-7b model for speculative decoding released here.
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-
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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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-
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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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-
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- ### License
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- [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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