BSC-LT
/

salamandra-2b-instruct-gptq

@@ -3,197 +3,124 @@ library_name: transformers
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 tags: []
 ---
+---
+license: apache-2.0
+library_name: transformers
+base_model: BSC-LT/salamandra-2b-instruct
+pipeline_tag: text-generation
+language:
+- bg
+- ca
+- code
+- cs
+- cy
+- da
+- de
+- el
+- en
+- es
+- et
+- eu
+- fi
+- fr
+- ga
+- gl
+- hr
+- hu
+- it
+- lt
+- lv
+- mt
+- nl
+- nn
+- \no
+- oc
+- pl
+- pt
+- ro
+- ru
+- sh
+- sk
+- sl
+- sr
+- sv
+- uk
+---
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/633b489acbdbadd99c0b75ef/MhsW4ODhK6ofYq8DnpyKc.png)
+# Salamandra-2b-instruct-gptq Model Card
+This model is the gptq-quantized version of [Salamandra-2b-instruct](https://huggingface.co/BSC-LT/salamandra-2b-instruct) for speculative decoding.
+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.
+Inferencing with this model can be done using [VLLM](https://docs.vllm.ai/en/stable/models/engine_args.html).
+Salamandra is a highly multilingual model pre-trained from scratch that comes in three different
+sizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants,
+promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/)
+and the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU
+within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
+This model card corresponds to the gptq-quantized version of Salamandra-2b-instruct for speculative decoding.
+The entire Salamandra family is released under a permissive [Apache 2.0 license]((https://www.apache.org/licenses/LICENSE-2.0)).
+## How to Use
+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
+any current version of the libraries. This is an example of a conversational chatbot using the model:
+```
+from vllm import LLM, SamplingParams
+model_name = "BSC-LT/salamandra-2b-instruct-gptq"
+llm = LLM(model=model_name)
+messages = []
+while True:
+    user_input = input("user >> ")
+    if user_input.lower() == "exit":
+        print("Chat ended.")
+        break
+    messages.append({'role': 'user', 'content': user_input})
+    outputs = llm.chat(messages,
+                       sampling_params=SamplingParams(
+                           temperature=0.5,
+                           stop_token_ids=[5],
+                           max_tokens=200)
+                       )[0].outputs
+    model_output = outputs[0].text
+    print(f'assistant >> {model_output}')
+    messages.append({'role': 'assistant', 'content': model_output})
+```
+### Author
+International Business Machines (IBM).
+### Copyright
+International Business Machines (IBM).
+### Contact
+For further information, please send an email to <langtech@bsc.es>.
+### Acknowledgements
+We appreciate the collaboration with IBM in this work.
+Specifically, the IBM team created gptq-quantized version of the Salamandra-2b-instruct model for speculative decoding released here.
+### Disclaimer
+Be aware that the model may contain biases or other unintended distortions.
+When third parties deploy systems or provide services based on this model, or use the model themselves,
+they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable
+regulations, including those governing the use of Artificial Intelligence.
+Barcelona Supercomputing Center and International Business Machines shall
+not be held liable for any outcomes resulting from third-party use.
+### License
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)