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models projecte aina FLOR 1 3B Instructed
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FLOR-1.3B-Instructed is a 1.3B-parameter transformer-based causal language model for Catalan, Spanish, and English, trained on a combined dataset from InstruCat, a Catalan language set of instruction generated automatically from prject-aina task orientated dataset, a subset of the Dolly dataset for English, and MENTOR_ES and MENTOR_CA, a Spanish and Catalan sets of instructions commisioned by the BSC Language Technologies Unit. It is th result of a language adaptation technique performed on BLOOM-7.1B, which involves modifying the model's vocabulary and embedding layer, and continuously pre-training the model with 140B tokens in our target languages. Blog post describing the base model with more parameters: flor-6-3b, a chinchilla compliant model
The FLOR-1.3B-Instructed model is ready-to-use for some downstream tasks. It can perform text-generation tasks because fine-tuned for specific scenarios, such as summarization, Question Answering, creative writing, etc.
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="projecte-aina/FLOR-1.3B-Instructed")
instruction = "Quants habitants té Mataró?"
context = "Mataró és una ciutat de Catalunya, capital de la comarca del Maresme. Situada al litoral mediterrani, a uns 30 km al nord-est de Barcelona, ha estat tradicionalment un centre administratiu de rellevància territorial i un pol de dinamisme econòmic. Compta amb prop de 130.000 habitants, essent actualment la vuitena població del Principat i la tretzena dels Països Catalans. "
# We need to format the prompt and context using ### and \n
def givePrediction(instruction, context, max_new_tokens=50, repetition_penalty=1.2, top_k=50, top_p=0.95, do_sample=True, temperature=0.5)
text = f"### Instruction\n{{instruction}}\n### Context\n{{context}}\n### Answer\n"
response = pipe(text.format(instruction=instruction, context=context),temperature=temperature,repetition_penalty=repetition_penalty, max_new_tokens=max_new_tokens,top_k=top_k, top_p=top_p, do_sample=do_sample)[0]["generated_text"]
answer = response.split("###")[-1][8:-1]
return answer
answer = givePrediction(instruction, context)
print(answer)
'130 000'At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
The training corpus is composed of 140B tokens gathered from web crawlings and public domain data.
The Language Technologies Unit from Barcelona Supercomputing Center.
For further information, please send an email to [email protected].
Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
This work was funded by [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of Projecte AINA.
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The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.
Be aware that the model may have biases and/or any other undesirable distortions.
When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the model (Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties.
| Inference type | Python sample (Notebook) | CLI with YAML |
|---|---|---|
| Real time | text-generation-online-endpoint.ipynb | text-generation-online-endpoint.sh |
| Batch | text-generation-batch-endpoint.ipynb | coming soon |
{
"input_data": {
"input_string": [
"Once upon a time,"
],
"parameters": {
"top_p": 0.8,
"temperature": 0.8,
"max_new_tokens": 90,
"do_sample": true
}
}
}[
{
"0": "Once upon a time, there were a few things that could be done in the fields.\n\n- The first was to plant crops.\n- The second was to raise cattle.\n- The third was to grow vegetables.\n- The fourth was to plant fruits.\n- The fifth was to make wine.\n- The sixth was to make cheese.\n- The seventh was to make beer.\n- The"
}
]Version: 1
SharedComputeCapacityEnabled hiddenlayerscanned huggingface_model_id : projecte-aina/FLOR-1.3B-Instructed inference_compute_allow_list : ['Standard_NC6s_v3', 'Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_NC24ads_A100_v4', 'Standard_NC48ads_A100_v4', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4'] inference_supported_envs : ['vllm'] license : apache-2.0 disable-batch : true task : text-generation
View in Studio: https://ml.azure.com/registries/azureml/models/projecte-aina-FLOR-1-3B-Instructed/version/1
License: apache-2.0
SharedComputeCapacityEnabled: True
SHA: 5d8b61d6bb55ac1f9e5e3376658356c1aadad196
datasets:
inference-min-sku-spec: 6|1|112|64
inference-recommended-sku: Standard_NC6s_v3, Standard_NC12s_v3, Standard_NC24s_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4
languages: en, es, ca