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Model: maritaca-ai/sabia-7b Source: Original Platform
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README.md
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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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Resenha: Gostei muito do filme, é o melhor do ano!
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Classe: positiva
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Resenha: O filme deixa muito a desejar.
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Classe: negativa
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Resenha: Apesar de longo, valeu o ingresso.
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Classe:"""
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input_ids = tokenizer(prompt, return_tensors="pt")
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output = model.generate(
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input_ids["input_ids"].to("cuda"),
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max_length=1024,
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eos_token_id=tokenizer.encode("\n")) # Stop generation when a "\n" token is dectected
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# The output contains the input tokens, so we have to skip them.
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output = output[0][len(input_ids["input_ids"][0]):]
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print(tokenizer.decode(output, skip_special_tokens=True))
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```
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If your GPU does not have enough RAM, try using int8 precision.
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However, expect some degradation in the model output quality when compared to fp16 or bf16.
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```python
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model = LlamaForCausalLM.from_pretrained(
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"maritaca-ai/sabia-7b",
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device_map="auto",
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low_cpu_mem_usage=True,
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load_in_8bit=True, # Requires pip install bitsandbytes
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)
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```
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## Results in Portuguese
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Below we show the results on the Poeta benchmark, which consists of 14 Portuguese datasets.
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For more information on the Normalized Preferred Metric (NPM), please refer to our paper.
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|Model | NPM |
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|--|--|
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|LLaMA-1-7B| 33.0|
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|LLaMA-2-7B| 43.7|
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|Sabiá-7B| 48.5|
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## Results in English
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Below we show the average results on 6 English datasets: PIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, and OpenBookQA.
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|Model | NPM |
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|--|--|
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|LLaMA-1-7B| 50.1|
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|Sabiá-7B| 49.0|
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## Citation
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Please use the following bibtex to cite our paper:
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```
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@InProceedings{10.1007/978-3-031-45392-2_15,
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author="Pires, Ramon
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and Abonizio, Hugo
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and Almeida, Thales Sales
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and Nogueira, Rodrigo",
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editor="Naldi, Murilo C.
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and Bianchi, Reinaldo A. C.",
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title="Sabi{\'a}: Portuguese Large Language Models",
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booktitle="Intelligent Systems",
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year="2023",
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publisher="Springer Nature Switzerland",
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address="Cham",
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pages="226--240",
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isbn="978-3-031-45392-2"
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}
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```
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# [Open Portuguese LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/maritaca-ai/sabia-7b)
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| Metric | Value |
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|--------------------------|---------|
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|Average |**47.09**|
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|ENEM Challenge (No Images)| 55.07|
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|BLUEX (No Images) | 47.71|
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|OAB Exams | 41.41|
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|Assin2 RTE | 46.68|
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|Assin2 STS | 1.89|
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|FaQuAD NLI | 58.34|
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|HateBR Binary | 61.93|
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|PT Hate Speech Binary | 64.13|
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|tweetSentBR | 46.64|
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.37.2",
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"use_cache": true,
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"vocab_size": 32000
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}
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"transformers_version": "4.37.2"
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}
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||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
}
|
||||
}
|
||||
93385
tokenizer.json
Normal file
93385
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
||||
size 499723
|
||||
33
tokenizer_config.json
Normal file
33
tokenizer_config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
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|
||||
"__type": "AddedToken",
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||||
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||||
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||||
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|
||||
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||||
},
|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
},
|
||||
"model_max_length": 2048,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
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|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user