RichardErkhov
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README.md
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Quantization made by Richard Erkhov.
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[Github](https://github.com/RichardErkhov)
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[Discord](https://discord.gg/pvy7H8DZMG)
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[Request more models](https://github.com/RichardErkhov/quant_request)
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sabia-7b - GGUF
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- Model creator: https://huggingface.co/maritaca-ai/
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- Original model: https://huggingface.co/maritaca-ai/sabia-7b/
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [sabia-7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q2_K.gguf) | Q2_K | 2.36GB |
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| [sabia-7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.IQ3_XS.gguf) | IQ3_XS | 2.6GB |
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| [sabia-7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.IQ3_S.gguf) | IQ3_S | 2.75GB |
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| [sabia-7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
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| [sabia-7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.IQ3_M.gguf) | IQ3_M | 2.9GB |
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| [sabia-7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q3_K.gguf) | Q3_K | 3.07GB |
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| [sabia-7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
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| [sabia-7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
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| [sabia-7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
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| [sabia-7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q4_0.gguf) | Q4_0 | 3.56GB |
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| [sabia-7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
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| [sabia-7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
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| [sabia-7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q4_K.gguf) | Q4_K | 3.8GB |
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| [sabia-7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
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| [sabia-7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q4_1.gguf) | Q4_1 | 3.95GB |
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| [sabia-7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q5_0.gguf) | Q5_0 | 4.33GB |
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| [sabia-7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
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| [sabia-7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q5_K.gguf) | Q5_K | 4.45GB |
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| [sabia-7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
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| [sabia-7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q5_1.gguf) | Q5_1 | 4.72GB |
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| [sabia-7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q6_K.gguf) | Q6_K | 5.15GB |
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| [sabia-7b.Q8_0.gguf](https://huggingface.co/RichardErkhov/maritaca-ai_-_sabia-7b-gguf/blob/main/sabia-7b.Q8_0.gguf) | Q8_0 | 4.88GB |
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Original model description:
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---
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language:
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- pt
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model-index:
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- name: sabia-7b
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: ENEM Challenge (No Images)
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type: eduagarcia/enem_challenge
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split: train
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args:
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num_few_shot: 3
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metrics:
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- type: acc
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value: 55.07
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name: accuracy
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BLUEX (No Images)
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type: eduagarcia-temp/BLUEX_without_images
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split: train
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args:
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num_few_shot: 3
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metrics:
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- type: acc
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value: 47.71
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name: accuracy
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: OAB Exams
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type: eduagarcia/oab_exams
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split: train
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args:
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num_few_shot: 3
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metrics:
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- type: acc
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value: 41.41
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name: accuracy
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Assin2 RTE
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type: assin2
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split: test
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args:
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num_few_shot: 15
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metrics:
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- type: f1_macro
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value: 46.68
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name: f1-macro
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Assin2 STS
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type: eduagarcia/portuguese_benchmark
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split: test
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args:
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num_few_shot: 15
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metrics:
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- type: pearson
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value: 1.89
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name: pearson
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: FaQuAD NLI
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type: ruanchaves/faquad-nli
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split: test
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args:
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num_few_shot: 15
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metrics:
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- type: f1_macro
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value: 58.34
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name: f1-macro
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HateBR Binary
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type: ruanchaves/hatebr
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: f1_macro
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value: 61.93
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name: f1-macro
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: PT Hate Speech Binary
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type: hate_speech_portuguese
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: f1_macro
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value: 64.13
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name: f1-macro
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: tweetSentBR
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type: eduagarcia-temp/tweetsentbr
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: f1_macro
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value: 46.64
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name: f1-macro
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
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name: Open Portuguese LLM Leaderboard
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---
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Sabiá-7B is Portuguese language model developed by [Maritaca AI](https://www.maritaca.ai/).
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**Input:** The model accepts only text input.
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**Output:** The Model generates text only.
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**Model Architecture:** Sabiá-7B is an auto-regressive language model that uses the same architecture of LLaMA-1-7B.
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**Tokenizer:** It uses the same tokenizer as LLaMA-1-7B.
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**Maximum sequence length:** 2048 tokens.
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**Pretraining data:** The model was pretrained on 7 billion tokens from the Portuguese subset of ClueWeb22, starting with the weights of LLaMA-1-7B and further trained for an additional 10 billion tokens, approximately 1.4 epochs of the training dataset.
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**Data Freshness:** The pretraining data has a cutoff of mid-2022.
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**License:** The licensing is the same as LLaMA-1's, restricting the model's use to research purposes only.
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**Paper:** For more details, please refer to our paper: [Sabiá: Portuguese Large Language Models](https://arxiv.org/pdf/2304.07880.pdf)
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## Few-shot Example
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Given that Sabiá-7B was trained solely on a language modeling objective without fine-tuning for instruction following, it is recommended for few-shot tasks rather than zero-shot tasks, like in the example below.
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```python
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import torch
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from transformers import LlamaTokenizer, LlamaForCausalLM
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tokenizer = LlamaTokenizer.from_pretrained("maritaca-ai/sabia-7b")
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model = LlamaForCausalLM.from_pretrained(
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"maritaca-ai/sabia-7b",
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device_map="auto", # Automatically loads the model in the GPU, if there is one. Requires pip install acelerate
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low_cpu_mem_usage=True,
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torch_dtype=torch.bfloat16 # If your GPU does not support bfloat16, change to torch.float16
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)
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prompt = """Classifique a resenha de filme como "positiva" ou "negativa".
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234 |
+
|
235 |
+
Resenha: Gostei muito do filme, é o melhor do ano!
|
236 |
+
Classe: positiva
|
237 |
+
|
238 |
+
Resenha: O filme deixa muito a desejar.
|
239 |
+
Classe: negativa
|
240 |
+
|
241 |
+
Resenha: Apesar de longo, valeu o ingresso.
|
242 |
+
Classe:"""
|
243 |
+
|
244 |
+
input_ids = tokenizer(prompt, return_tensors="pt")
|
245 |
+
|
246 |
+
output = model.generate(
|
247 |
+
input_ids["input_ids"].to("cuda"),
|
248 |
+
max_length=1024,
|
249 |
+
eos_token_id=tokenizer.encode("\n")) # Stop generation when a "\n" token is dectected
|
250 |
+
|
251 |
+
# The output contains the input tokens, so we have to skip them.
|
252 |
+
output = output[0][len(input_ids["input_ids"][0]):]
|
253 |
+
|
254 |
+
print(tokenizer.decode(output, skip_special_tokens=True))
|
255 |
+
```
|
256 |
+
|
257 |
+
If your GPU does not have enough RAM, try using int8 precision.
|
258 |
+
However, expect some degradation in the model output quality when compared to fp16 or bf16.
|
259 |
+
```python
|
260 |
+
model = LlamaForCausalLM.from_pretrained(
|
261 |
+
"maritaca-ai/sabia-7b",
|
262 |
+
device_map="auto",
|
263 |
+
low_cpu_mem_usage=True,
|
264 |
+
load_in_8bit=True, # Requires pip install bitsandbytes
|
265 |
+
)
|
266 |
+
```
|
267 |
+
|
268 |
+
## Results in Portuguese
|
269 |
+
|
270 |
+
Below we show the results on the Poeta benchmark, which consists of 14 Portuguese datasets.
|
271 |
+
|
272 |
+
For more information on the Normalized Preferred Metric (NPM), please refer to our paper.
|
273 |
+
|
274 |
+
|Model | NPM |
|
275 |
+
|--|--|
|
276 |
+
|LLaMA-1-7B| 33.0|
|
277 |
+
|LLaMA-2-7B| 43.7|
|
278 |
+
|Sabiá-7B| 48.5|
|
279 |
+
|
280 |
+
## Results in English
|
281 |
+
|
282 |
+
Below we show the average results on 6 English datasets: PIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, and OpenBookQA.
|
283 |
+
|
284 |
+
|Model | NPM |
|
285 |
+
|--|--|
|
286 |
+
|LLaMA-1-7B| 50.1|
|
287 |
+
|Sabiá-7B| 49.0|
|
288 |
+
|
289 |
+
|
290 |
+
## Citation
|
291 |
+
|
292 |
+
Please use the following bibtex to cite our paper:
|
293 |
+
```
|
294 |
+
@InProceedings{10.1007/978-3-031-45392-2_15,
|
295 |
+
author="Pires, Ramon
|
296 |
+
and Abonizio, Hugo
|
297 |
+
and Almeida, Thales Sales
|
298 |
+
and Nogueira, Rodrigo",
|
299 |
+
editor="Naldi, Murilo C.
|
300 |
+
and Bianchi, Reinaldo A. C.",
|
301 |
+
title="Sabi{\'a}: Portuguese Large Language Models",
|
302 |
+
booktitle="Intelligent Systems",
|
303 |
+
year="2023",
|
304 |
+
publisher="Springer Nature Switzerland",
|
305 |
+
address="Cham",
|
306 |
+
pages="226--240",
|
307 |
+
isbn="978-3-031-45392-2"
|
308 |
+
}
|
309 |
+
```
|
310 |
+
|
311 |
+
# [Open Portuguese LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
|
312 |
+
Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/maritaca-ai/sabia-7b)
|
313 |
+
|
314 |
+
| Metric | Value |
|
315 |
+
|--------------------------|---------|
|
316 |
+
|Average |**47.09**|
|
317 |
+
|ENEM Challenge (No Images)| 55.07|
|
318 |
+
|BLUEX (No Images) | 47.71|
|
319 |
+
|OAB Exams | 41.41|
|
320 |
+
|Assin2 RTE | 46.68|
|
321 |
+
|Assin2 STS | 1.89|
|
322 |
+
|FaQuAD NLI | 58.34|
|
323 |
+
|HateBR Binary | 61.93|
|
324 |
+
|PT Hate Speech Binary | 64.13|
|
325 |
+
|tweetSentBR | 46.64|
|
326 |
+
|
327 |
+
|
328 |
+
|