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TeenyTinyLlama-460m-Chat-GGUF

Text Generation
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GGUF
Portuguese
alignment
instruction tuned
text generation
conversation
assistant
ggml
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TeenyTinyLlama-460m-Chat-GGUF / README.md
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metadata
base_model: nicholasKluge/TeenyTinyLlama-460m-Chat
co2_eq_emissions:
  emissions: 2.53
  geographical_location: United States of America
  hardware_used: NVIDIA A100-SXM4-40GB
  source: CodeCarbon
  training_type: fine-tuning
datasets:
  - nicholasKluge/instruct-aira-dataset-v2
inference: true
language:
  - pt
library_name: transformers
license: apache-2.0
metrics:
  - accuracy
model-index:
  - name: TeenyTinyLlama-460m-Chat
    results:
      - dataset:
          args:
            num_few_shot: 3
          name: ENEM Challenge (No Images)
          split: train
          type: eduagarcia/enem_challenge
        metrics:
          - name: accuracy
            type: acc
            value: 20.29
        source:
          name: Open Portuguese LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m-Chat
        task:
          name: Text Generation
          type: text-generation
      - dataset:
          args:
            num_few_shot: 3
          name: BLUEX (No Images)
          split: train
          type: eduagarcia-temp/BLUEX_without_images
        metrics:
          - name: accuracy
            type: acc
            value: 25.45
        source:
          name: Open Portuguese LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m-Chat
        task:
          name: Text Generation
          type: text-generation
      - dataset:
          args:
            num_few_shot: 3
          name: OAB Exams
          split: train
          type: eduagarcia/oab_exams
        metrics:
          - name: accuracy
            type: acc
            value: 26.74
        source:
          name: Open Portuguese LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m-Chat
        task:
          name: Text Generation
          type: text-generation
      - dataset:
          args:
            num_few_shot: 15
          name: Assin2 RTE
          split: test
          type: assin2
        metrics:
          - name: f1-macro
            type: f1_macro
            value: 43.77
        source:
          name: Open Portuguese LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m-Chat
        task:
          name: Text Generation
          type: text-generation
      - dataset:
          args:
            num_few_shot: 15
          name: Assin2 STS
          split: test
          type: eduagarcia/portuguese_benchmark
        metrics:
          - name: pearson
            type: pearson
            value: 4.52
        source:
          name: Open Portuguese LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m-Chat
        task:
          name: Text Generation
          type: text-generation
      - dataset:
          args:
            num_few_shot: 15
          name: FaQuAD NLI
          split: test
          type: ruanchaves/faquad-nli
        metrics:
          - name: f1-macro
            type: f1_macro
            value: 34
        source:
          name: Open Portuguese LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m-Chat
        task:
          name: Text Generation
          type: text-generation
      - dataset:
          args:
            num_few_shot: 25
          name: HateBR Binary
          split: test
          type: ruanchaves/hatebr
        metrics:
          - name: f1-macro
            type: f1_macro
            value: 33.49
        source:
          name: Open Portuguese LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m-Chat
        task:
          name: Text Generation
          type: text-generation
      - dataset:
          args:
            num_few_shot: 25
          name: PT Hate Speech Binary
          split: test
          type: hate_speech_portuguese
        metrics:
          - name: f1-macro
            type: f1_macro
            value: 22.99
        source:
          name: Open Portuguese LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m-Chat
        task:
          name: Text Generation
          type: text-generation
      - dataset:
          args:
            num_few_shot: 25
          name: tweetSentBR
          split: test
          type: eduagarcia-temp/tweetsentbr
        metrics:
          - name: f1-macro
            type: f1_macro
            value: 18.13
        source:
          name: Open Portuguese LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicholasKluge/TeenyTinyLlama-460m-Chat
        task:
          name: Text Generation
          type: text-generation
model_creator: nicholasKluge
model_name: TeenyTinyLlama-460m-Chat
pipeline_tag: text-generation
quantized_by: afrideva
tags:
  - alignment
  - instruction tuned
  - text generation
  - conversation
  - assistant
  - gguf
  - ggml
  - quantized
widget:
  - example_title: Exemplo
    text: >-
      <s><instruction>Cite algumas bandas de rock famosas da década de
      1960.</instruction>
  - example_title: Exemplo
    text: <s><instruction>Quantos planetas existem no sistema solar?</instruction>
  - example_title: Exemplo
    text: <s><instruction>Qual é o futuro do ser humano?</instruction>
  - example_title: Exemplo
    text: <s><instruction>Qual o sentido da vida?</instruction>
  - example_title: Exemplo
    text: <s><instruction>Como imprimir hello world em python?</instruction>
  - example_title: Exemplo
    text: >-
      <s><instruction>Invente uma história sobre um encanador com poderes
      mágicos.</instruction>

TeenyTinyLlama-460m-Chat-GGUF

Quantized GGUF model files for TeenyTinyLlama-460m-Chat from nicholasKluge

Original Model Card:

TeenyTinyLlama-460m-Chat

TeenyTinyLlama is a pair of small foundational models trained in Brazilian Portuguese.

This repository contains a version of TeenyTinyLlama-460m (TeenyTinyLlama-460m-Chat) fine-tuned on the Instruct-Aira Dataset version 2.0.

Details

  • Number of Epochs: 3
  • Batch size: 4
  • Optimizer: torch.optim.AdamW (warmup_steps = 1e3, learning_rate = 1e-5, epsilon = 1e-8)
  • GPU: 1 NVIDIA A100-SXM4-40GB
  • Carbon emissions stats are logged in this file.

This repository has the source code used to train this model.

Intended Uses

The primary intended use of TeenyTinyLlama is to research the challenges related to developing language models for low-resource languages. Checkpoints saved during training are intended to provide a controlled setting for performing scientific experiments. You may also further fine-tune and adapt TeenyTinyLlama for deployment, as long as your use is following the Apache 2.0 license. If you decide to use pre-trained TeenyTinyLlama as a basis for your fine-tuned model, please conduct your own risk and bias assessment.

Out-of-scope Use

TeenyTinyLlama is not intended for deployment. It is not a product and should not be used for human-facing interactions.

TeenyTinyLlama models are Brazilian Portuguese language only and are not suitable for translation or generating text in other languages.

TeenyTinyLlama has not been fine-tuned for downstream contexts in which language models are commonly deployed.

Usage

The following special tokens are used to mark the user side of the interaction and the model's response:

<instruction>What is a language model?</instruction>A language model is a probability distribution over a vocabulary.</s>

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/TeenyTinyLlama-460m-Chat')
model = AutoModelForCausalLM.from_pretrained('nicholasKluge/TeenyTinyLlama-460m-Chat')

model.eval()
model.to(device)

question =  input("Entre seu prompt aqui: ")

inputs = tokenizer("<instruction>" + question + "</instruction>", return_tensors="pt").to(device)

responses = model.generate(**inputs, num_return_sequences=2)

print(f"Pergunta: 👤 {question}\n")

for i, response in  enumerate(responses):
    print(f'Resposta {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, "")}')

The model will output something like:

>>>Question: 👤 Qual a capital do Brasil?

>>>Response 1: 🤖 A capital do Brasil é Brasília.
>>>Response 2: 🤖 A capital do Brasil é Brasília.

The chat template for this model is:

{{bos_token}}
{% for message in messages %}
    {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}
        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
    {% endif %}
    {% if message['role'] == 'user' %}
        {{ '<instruction>' + message['content'].strip() + '</instruction>'}}
    {% elif message['role'] == 'assistant' %}
        {{ message['content'].strip() + eos_token}}
    {% else %}
        {{ raise_exception('Only user and assistant roles are supported!') }}
    {% endif %}
{% endfor %}

Limitations

Like almost all other language models trained on large text datasets scraped from the web, the TTL pair exhibited behavior that does not make them an out-of-the-box solution to many real-world applications, especially those requiring factual, reliable, nontoxic text generation. Our models are all subject to the following:

  • Hallucinations: This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination.

  • Biases and Toxicity: This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.

  • Unreliable Code: The model may produce incorrect code snippets and statements. These code generations should not be treated as suggestions or accurate solutions.

  • Language Limitations: The model is primarily designed to understand standard Brazilian Portuguese. Other languages might challenge its comprehension, leading to potential misinterpretations or errors in response.

  • Repetition and Verbosity: The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.

Hence, even though our models are released with a permissive license, we urge users to perform their risk analysis on these models if intending to use them for real-world applications and also have humans moderating the outputs of these models in applications where they will interact with an audience, guaranteeing users are always aware they are interacting with a language model.

Evaluations

During our training runs, both models showed consistent convergence. At no point did our evaluation curves show signs of overfitting or saturation. In the case of our 460m parameter model, we intentionally trained past the optimal point by approximately 75,000 steps to assess if there were any signs of saturation, but our evaluations consistently gave better results. We hypothesize that our models are under-trained but can improve if further trained to pass the Chinchilla optimal range.

Processed Tokens Perplexity Energy Consumption (kWh) Emissions (KgCO2eq)
8.1M 20.49 9.40 3.34
1.6B 16.90 18.82 6.70
2.4B 15.43 28.59 10.16
3.2B 14.64 38.20 13.57
4.0B 14.08 48.04 17.07
4.9B 13.61 57.74 20.52
5.7B 13.25 67.32 23.92
6.5B 12.87 76.84 27.30
7.3B 12.57 86.40 30.70
8.1B 12.27 96.19 34.18
9.0B 11.96 106.06 37.70
9.8B 11.77 115.69 41.31

Benchmarks

Evaluations on benchmarks were performed using the Language Model Evaluation Harness (by EleutherAI). Laiviet translated the tasks from the LM-Evaluation-Harness we used. The results of models marked with an "*" were extracted from the Open LLM Leaderboard.

ARC HellaSwag MMLU TruthfulQA Average
Pythia-410m 24.83* 41.29* 25.99* 40.95* 33.26
TTL-460m 29.40 33.00 28.55 41.10 33.01
Bloom-560m 24.74* 37.15* 24.22* 42.44* 32.13
Xglm-564M 25.56 34.64* 25.18* 42.53 31.97
OPT-350m 23.55* 36.73* 26.02* 40.83* 31.78
TTL-160m 26.15 29.29 28.11 41.12 31.16
Pythia-160m 24.06* 31.39* 24.86* 44.34* 31.16
OPT-125m 22.87* 31.47* 26.02* 42.87* 30.80
GPorTuguese-2 22.48 29.62 27.36 41.44 30.22
Gpt2-small 21.48* 31.60* 25.79* 40.65* 29.97
Multilingual GPT 23.81 26.37* 25.17* 39.62 28.73

Evaluations on Brazilian Portuguese benchmarks were performed using a Portuguese implementation of the EleutherAI LM Evaluation Harness (created by Eduardo Garcia).

ASSIN2 RTE ASSIN2 STS BLUEX ENEM FAQUAD NLI HateBR OAB Exams Average
Qwen-1.8B 64.83 19.53 26.15 30.23 43.97 33.33 27.20 35.03
TinyLlama-1.1B 58.93 13.57 22.81 22.25 43.97 36.92 23.64 31.72
TTL-460m 53.93 12.66 22.81 19.87 49.01 33.59 27.06 31.27
XGLM-564m 49.61 22.91 19.61 19.38 43.97 33.99 23.42 30.41
Bloom-1b7 53.60 4.81 21.42 18.96 43.97 34.89 23.05 28.67
TTL-160m 53.36 2.58 21.84 18.75 43.97 36.88 22.60 28.56
OPT-125m 39.77 2.00 21.84 17.42 43.97 47.04 22.78 27.83
Pythia-160 33.33 12.81 16.13 16.66 50.36 41.09 22.82 27.60
OLMo-1b 34.12 9.28 18.92 20.29 43.97 41.33 22.96 27.26
Bloom-560m 33.33 8.48 18.92 19.03 43.97 37.07 23.05 26.26
Pythia-410m 33.33 4.80 19.47 19.45 43.97 33.33 23.01 25.33
OPT-350m 33.33 3.65 20.72 17.35 44.71 33.33 23.01 25.15
GPT-2 small 33.26 0.00 10.43 11.20 43.52 33.68 13.12 20.74
GPorTuguese 33.33 3.85 14.74 3.01 28.81 33.33 21.23 19.75
Samba-1.1B 33.33 1.30 8.07 10.22 17.72 35.79 15.03 17.35

Fine-Tuning Comparisons

To further evaluate the downstream capabilities of our models, we decided to employ a basic fine-tuning procedure for our TTL pair on a subset of tasks from the Poeta benchmark. We apply the same procedure for comparison purposes on both BERTimbau models, given that they are also LLM trained from scratch in Brazilian Portuguese and have a similar size range to our models. We used these comparisons to assess if our pre-training runs produced LLM capable of producing good results ("good" here means "close to BERTimbau") when utilized for downstream applications.

Models IMDB FaQuAD-NLI HateBr Assin2 AgNews Average
BERTimbau-large 93.58 92.26 91.57 88.97 94.11 92.10
BERTimbau-small 92.22 93.07 91.28 87.45 94.19 91.64
TTL-460m 91.64 91.18 92.28 86.43 94.42 91.19
TTL-160m 91.14 90.00 90.71 85.78 94.05 90.34

All the shown results are the higher accuracy scores achieved on the respective task test sets after fine-tuning the models on the training sets. All fine-tuning runs used the same hyperparameters, and the code implementation can be found in the model cards of our fine-tuned models.

Cite as 🤗 


@misc{correa24ttllama,
  title = {TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese},
  author = {Corr{\^e}a, Nicholas Kluge and Falk, Sophia and Fatimah, Shiza and Sen, Aniket and De Oliveira, Nythamar},
  journal={arXiv preprint arXiv:2401.16640},
  year={2024}
}

Funding

This repository was built as part of the RAIES (Rede de Inteligência Artificial Ética e Segura) initiative, a project supported by FAPERGS - (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), Brazil.

License

TeenyTinyLlama-460m-Chat is licensed under the Apache License, Version 2.0. See the LICENSE file for more details.