Add some metadata to the model card

#1
by tomaarsen HF Staff - opened
Files changed (1) hide show
  1. README.md +4 -1
README.md CHANGED
@@ -8,6 +8,9 @@ language:
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  - es
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  base_model:
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  - PleIAs/Pleias-350m-Preview
 
 
 
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  ---
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@@ -91,4 +94,4 @@ With only 350 million parameters, Pleias-RAG-350M is classified among the *phone
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  We also release an unquantized [GGUF version](https://huggingface.co/PleIAs/Pleias-RAG-350M-gguf) for deployment on CPU. Our internal performance benchmarks suggest that waiting times are currently acceptable for most either even under constrained RAM: about 20 seconds for a complex generation including reasoning traces on 8g RAM and below. Since the model is unquantized, quality of text generation should be identical to the original model.
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- Once integrated into a RAG system, Pleias-RAG-350M can also be use in a broader range of non-conversational use cases including user support or educational assistance. Through this release, we aims to make tiny model workable in production by relying systematically on an externalized memory.
 
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  - es
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  base_model:
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  - PleIAs/Pleias-350m-Preview
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+ pipeline_tag: text-generation
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+ tags:
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+ - transformers
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  ---
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  We also release an unquantized [GGUF version](https://huggingface.co/PleIAs/Pleias-RAG-350M-gguf) for deployment on CPU. Our internal performance benchmarks suggest that waiting times are currently acceptable for most either even under constrained RAM: about 20 seconds for a complex generation including reasoning traces on 8g RAM and below. Since the model is unquantized, quality of text generation should be identical to the original model.
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+ Once integrated into a RAG system, Pleias-RAG-350M can also be use in a broader range of non-conversational use cases including user support or educational assistance. Through this release, we aims to make tiny model workable in production by relying systematically on an externalized memory.