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Model: TucanoBR/Tucano-1b1-Instruct Source: Original Platform
2026-05-01 23:37:32 +08:00
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--- a/EVALS-1b1-instruct.csv
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+model,average,calame_pt,lambada_pt,enem,bluex,oab_exams,assin2_rte,assin2_sts,faquad_nli,hatebr,hatespeech_pt,tweet_br,arc_pt,hellaswag_pt,truthfulqa
+Tucano-1b1-sft,33.66,56.74,34.66,21.06,24.61,26.20,33.42,0.87,43.97,33.33,41.23,40.65,30.60,42.83,41.17
+Tucano-1b1-dpo,35.44,56.55,35.53,21.06,26.01,26.47,67.78,8.88,43.97,31.28,41.23,22.03,30.77,43.50,41.14
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+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
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+   9. Accepting Warranty or Additional Liability. While redistributing
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--- a/README.md
+++ b/README.md
@@ -0,0 +1,445 @@
+---
+language:
+- pt
+license: apache-2.0
+library_name: transformers
+tags:
+- text-generation-inference
+datasets:
+- nicholasKluge/instruct-aira-dataset-v3
+- cnmoro/GPT4-500k-Augmented-PTBR-Clean
+- rhaymison/orca-math-portuguese-64k
+- nicholasKluge/reward-aira-dataset
+metrics:
+- perplexity
+pipeline_tag: text-generation
+widget:
+- text: "<instruction>Cite algumas bandas de rock brasileiras famosas.</instruction>"
+  example_title: Exemplo
+- text: "<instruction>Invente uma história sobre um encanador com poderes mágicos.</instruction>"
+  example_title: Exemplo
+- text: "<instruction>Qual cidade é a capital do estado do Rio Grande do Sul?</instruction>"
+  example_title: Exemplo
+- text: "<instruction>Diga o nome de uma maravilha culinária característica da cosinha Portuguesa?</instruction>"
+  example_title: Exemplo
+inference:
+  parameters:
+    repetition_penalty: 1.2
+    temperature: 0.1
+    top_k: 50
+    top_p: 1.0
+    max_new_tokens: 150
+co2_eq_emissions:
+  emissions: 21890
+  source: CodeCarbon
+  training_type: pre-training
+  geographical_location: Germany
+  hardware_used: NVIDIA A100-SXM4-80GB
+model-index:
+- name: Tucano-1b1-Instruct
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: CALAME-PT
+      type: NOVA-vision-language/calame-pt
+      split: all
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc
+      value: 56.55
+      name: accuracy
+    source:
+      url: https://huggingface.co/datasets/NOVA-vision-language/calame-pt
+      name: Context-Aware LAnguage Modeling Evaluation for Portuguese
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: LAMBADA-PT
+      type: TucanoBR/lambada-pt
+      split: train
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc
+      value: 35.53
+      name: accuracy
+    source:
+      url: https://huggingface.co/datasets/TucanoBR/lambada-pt
+      name: LAMBADA-PT
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: ENEM Challenge (No Images)
+      type: eduagarcia/enem_challenge
+      split: train
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc
+      value: 21.06
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
+      name: Open Portuguese LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BLUEX (No Images)
+      type: eduagarcia-temp/BLUEX_without_images
+      split: train
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc
+      value: 26.01
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
+      name: Open Portuguese LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: OAB Exams
+      type: eduagarcia/oab_exams
+      split: train
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc
+      value: 26.47
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
+      name: Open Portuguese LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Assin2 RTE
+      type: assin2
+      split: test
+      args:
+        num_few_shot: 15
+    metrics:
+    - type: f1_macro
+      value: 67.78
+      name: f1-macro
+    source:
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
+      name: Open Portuguese LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Assin2 STS
+      type: eduagarcia/portuguese_benchmark
+      split: test
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: pearson
+      value: 8.88
+      name: pearson
+    source:
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
+      name: Open Portuguese LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: FaQuAD NLI
+      type: ruanchaves/faquad-nli
+      split: test
+      args:
+        num_few_shot: 15
+    metrics:
+    - type: f1_macro
+      value: 43.97
+      name: f1-macro
+    source:
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
+      name: Open Portuguese LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HateBR Binary
+      type: ruanchaves/hatebr
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: f1_macro
+      value: 31.28
+      name: f1-macro
+    source:
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
+      name: Open Portuguese LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: PT Hate Speech Binary
+      type: hate_speech_portuguese
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: f1_macro
+      value: 41.23
+      name: f1-macro
+    source:
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
+      name: Open Portuguese LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: tweetSentBR
+      type: eduagarcia-temp/tweetsentbr
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: f1_macro
+      value: 22.03
+      name: f1-macro
+    source:
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
+      name: Open Portuguese LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: ARC-Challenge (PT)
+      type: arc_pt
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 30.77
+      name: normalized accuracy
+    source:
+      url: https://github.com/nlp-uoregon/mlmm-evaluation
+      name: Evaluation Framework for Multilingual Large Language Models
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (PT)
+      type: hellaswag_pt
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 43.50
+      name: normalized accuracy
+    source:
+      url: https://github.com/nlp-uoregon/mlmm-evaluation
+      name: Evaluation Framework for Multilingual Large Language Models
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (PT)
+      type: truthfulqa_pt
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 41.14
+      name: bleurt
+    source:
+      url: https://github.com/nlp-uoregon/mlmm-evaluation
+      name: Evaluation Framework for Multilingual Large Language Models
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Alpaca-Eval (PT)
+      type: alpaca_eval_pt
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: lc_winrate
+      value: 8.80
+      name: length controlled winrate
+    source:
+      url: https://github.com/tatsu-lab/alpaca_eval
+      name: AlpacaEval
+base_model:
+- TucanoBR/Tucano-1b1
+---
+# Tucano-1b1-Instruct
+
+<img src="./logo.png" alt="An illustration of a Tucano bird showing vibrant colors like yellow, orange, blue, green, and black." height="200">
+
+## Model Summary
+
+Tucano-1b1-Instruct is a fine-tuned version of [Tucano-1b1](https://huggingface.co/TucanoBR/Tucano-1b1). **[Tucano](https://huggingface.co/TucanoBR)** is a series of decoder-transformers natively pretrained in Portuguese. All Tucano models were trained on **[GigaVerbo](https://huggingface.co/datasets/TucanoBR/GigaVerbo)**, a concatenation of deduplicated Portuguese text corpora amounting to 200 billion tokens.
+
+The fine-tuning process was divided into two stages:
+
+- Supervised fine-tuning (SFT) using the [TucanoBR/Tucano-SFT](https://huggingface.co/datasets/TucanoBR/Tucano-SFT), a concatenation of three different instruction tuning datasets ([`cnmoro/GPT4-500k-Augmented-PTBR-Clean`](https://huggingface.co/datasets/cnmoro/GPT4-500k-Augmented-PTBR-Clean), [`rhaymison/orca-math-portuguese-64k`](https://huggingface.co/datasets/rhaymison/orca-math-portuguese-64k), [`nicholasKluge/instruct-aira-dataset-v3`](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset-v3)).
+- Direct Preference Optimization (DPO) using the [nicholasKluge/reward-aira-dataset](https://huggingface.co/datasets/nicholasKluge/reward-aira-dataset).
+
+Read our preprint [here](https://arxiv.org/abs/2411.07854).
+
+## Details
+
+- **Architecture:** a Transformer-based model pre-trained via causal language modeling
+- **Size:** 1,100,048,384 parameters
+- **Context length:** 2048 tokens
+- **Dataset:**
+  - [cnmoro/GPT4-500k-Augmented-PTBR-Clean](https://huggingface.co/datasets/cnmoro/GPT4-500k-Augmented-PTBR-Clean)
+  - [rhaymison/orca-math-portuguese-64k](https://huggingface.co/datasets/rhaymison/orca-math-portuguese-64k)
+  - [nicholasKluge/instruct-aira-dataset-v3](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset-v3)
+  - [nicholasKluge/reward-aira-dataset](https://huggingface.co/datasets/nicholasKluge/reward-aira-dataset)
+- **Language:** Portuguese
+- **Training time**: ~ 12 hours
+- **Emissions:** 22 KgCO2 (Germany)
+- **Total energy consumption:** 58 kWh
+
+This repository has the [source code](https://github.com/Nkluge-correa/Tucano) used to train this model. The main libraries used are:
+
+- [PyTorch](https://github.com/pytorch/pytorch)
+- [Transformers](https://github.com/huggingface/transformers)
+- [Datasets](https://github.com/huggingface/datasets)
+- [Tokenizers](https://github.com/huggingface/tokenizers)
+- [Sentencepiece](https://github.com/google/sentencepiece)
+- [Accelerate](https://github.com/huggingface/accelerate)
+- [FlashAttention](https://github.com/Dao-AILab/flash-attention)
+- [Liger Kernel](https://github.com/linkedin/Liger-Kernel)
+- [Codecarbon](https://github.com/mlco2/codecarbon)
+- [TRL](https://github.com/huggingface/trl)
+
+## Intended Uses
+
+The primary intended use of the Tucano models is to serve as foundations for research and development involving native Portuguese language modeling. Checkpoints saved during training are designed to provide a controlled setting for performing comparative experiments, specifically regarding the effects of active pretraining on the performance of currently available benchmarks. You may also fine-tune and adapt Tucano models for deployment if your use follows the Apache 2.0 license. If you decide to use the Tucano models as a basis for your fine-tuned model, please conduct your own risk and bias assessment.
+
+## Out-of-scope Use
+
+- Tucano models are **not intended for deployment**. They are not an out-of-the-box product and should not be used for human-facing interactions.
+
+- Tucano models are for **the Portuguese language only** and are unsuitable for text generation tasks in other languages.
+
+- Tucano models have **not been fine-tuned** for downstream tasks.
+
+## Basic usage
+
+Using the `pipeline`:
+
+```python
+from transformers import pipeline
+
+generator = pipeline("text-generation", model="TucanoBR/Tucano-1b1-Instruct")
+
+completions  = generator("<instruction>Qual cidade é a capital do estado do Rio Grande do Sul?</instruction>", num_return_sequences=2, max_new_tokens=100)
+
+for comp in completions:
+  print(f"🤖 {comp['generated_text']}")
+```
+
+Using the `AutoTokenizer` and `AutoModelForCausalLM`:
+
+```python
+from transformers import GenerationConfig, TextGenerationPipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Specify the model and tokenizer
+model_id = "TucanoBR/Tucano-1b1-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+# Specify the generation parameters as you like
+generation_config = GenerationConfig(
+    **{
+    "do_sample": True,
+    "max_new_tokens": 2048,
+    "renormalize_logits": True,
+    "repetition_penalty": 1.2,
+    "temperature": 0.1,
+    "top_k": 50,
+    "top_p": 1.0,
+    "use_cache": True, 
+  }
+)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+generator = TextGenerationPipeline(model=model, task="text-generation", tokenizer=tokenizer, device=device)
+
+# Generate text
+prompt = "<instruction>Qual cidade é a capital do estado do Rio Grande do Sul?</instruction>"
+completion = generator(prompt, generation_config=generation_config)
+print(completion[0]['generated_text'])
+```
+
+## Limitations
+
+Like almost all other language models trained on large text datasets scraped from the web, the Tucano models show behavior that does not make them an out-of-the-box solution to many real-world applications, especially those requiring factual, reliable, and nontoxic text generation. Tucano models are all subject to the following:
+
+- **Hallucinations:** Tucano models can produce content that can be mistaken as true facts, but are misleading or entirely false, i.e., hallucination.
+
+- **Biases and Toxicity:** Tucano models inherit the social and historical stereotypes from the data used to train them. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.
+
+- **Unreliable Code:** Tucano models may produce incorrect code snippets and statements. These code generations should not be treated as suggestions or accurate solutions.
+
+- **Language Limitations:** Tucano models are primarily designed to interact with Portuguese. Other languages might challenge its comprehension, leading to potential misinterpretations or errors in response.
+
+- **Repetition and Verbosity:** Tucano models 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 them if they intend to use them for real-world applications.
+
+## Evaluations
+
+To evaluate the `Instruct` versions of our models, we used [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval) 2.0 with length-controlled win rates, a fast and relatively cheap evaluation method that is highly correlated with human preferences and evaluations of pairwise comparisons. To learn more about our evaluation read [our documentation](https://github.com/Nkluge-correa/Tucano/blob/main/evaluations/README.md).
+
+|                         | Avg. Length | Wins | Base Wins | Total Matches | Length-Controlled Win Rate (%) | LC Std. Error |
+|-------------------------|-------------|------|-----------|---------------|--------------------------------|---------------|
+| Llama-3.2-3B-Instruct   | 1609        | 257  | 548       | 805           | 21.06                          | 0.075         |
+| **Tucano-2b4-Instruct** | 1843        | 151  | 654       | 805           | 13.00                          | 0.071         |
+| **Tucano-1b1-Instruct** | 1667        | 124  | 681       | 805           | 8.80                           | 0.083         |
+| Llama-3.2-1B-Instruct   | 1429        | 99   | 706       | 805           | 7.15                           | 0.057         |
+| TeenyTinyLlama-460m-Chat| 1333        | 28   | 777       | 805           | 2.84                           | 0.059         |
+| Sabiá-7b                | 5011        | 1    | 804       | 805           | 0.076                          | 0.0043        |
+| Gervásio-7b             | 5740        | 1    | 804       | 805           | 0.026                          | 0.0016        |
+
+## Cite as 🤗
+
+```latex
+@misc{correa2024tucanoadvancingneuraltext,
+      title={{Tucano: Advancing Neural Text Generation for Portuguese}}, 
+      author={Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
+      year={2024},
+      eprint={2411.07854},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2411.07854}, 
+}
+
+@article{correa2025tucanoadvancingneuraltext,
+    title={{Tucano: Advancing Neural Text Generation for Portuguese}},
+    author={Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
+    journal={Patterns},
+    publisher={Elsevier},
+    year={2025},
+    doi={10.1016/j.patter.2025.101325},
+    url={https://doi.org/10.1016/j.patter.2025.101325},
+    issn={2666-3899}
+}
+```
+
+## Aknowlegments
+
+We gratefully acknowledge the granted access to the [Marvin cluster](https://www.hpc.uni-bonn.de/en/systems/marvin) hosted by [University of Bonn](https://www.uni-bonn.de/en) along with the support provided by its High Performance Computing \& Analytics Lab.
+
+## License
+
+Tucano is licensed under the Apache License, Version 2.0. For more details, see the [LICENSE](LICENSE) file.
--- a/alpaca-eval.csv
+++ b/alpaca-eval.csv
@@ -0,0 +1,3 @@
+,win_rate,standard_error,mode,avg_length,n_wins,n_wins_base,n_draws,n_total,discrete_win_rate,length_controlled_winrate,lc_standard_error
+Tucano-1b1-dpo,15.419484227948896,1.160056594824009,community,1667,124,681,0,805,15.403726708074533,8.800275585416614,0.08352323105119325
+Tucano-1b1-sft,7.893483774877183,0.830060323275043,community,1075,58,747,0,805,7.204968944099378,6.376247766233091,0.07082994421269635
--- a/config.json
+++ b/config.json
@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5632,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 22,
+  "num_key_value_heads": 4,
+  "pad_token_id": 3,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "use_cache": false,
+  "vocab_size": 32002
+}
--- a/emissions-1b1-instruct.csv
+++ b/emissions-1b1-instruct.csv
@@ -0,0 +1,3 @@
+timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue,n_nodes
+2024-09-30T07:04:42,Tucano-sft,354e308a-920a-4f70-903f-1ec02bb535ef,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,36612.863234638,10.13847230867952,0.0002769101188209696,112.5,2131.3615768643012,365.62500000000006,1.144150400307572,21.761199203945356,3.7083065413816914,26.613656145634646,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34,3.11.3,2.7.1,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.0932,50.7263,975,machine,N,1.0,2
+2024-11-08T17:36:35,Tucano-dpo,fa6b7e78-e5d0-45e3-9855-f2be2d9f0d71,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,6364.964171009138,1.612778411754893,0.000253383737665124,112.5,748.4520727803413,188.89137554168704,0.1989048316897971,3.701681019953554,0.3329839390244344,4.233569790667786,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34,3.11.3,2.7.2,128,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,503.71033477783203,machine,N,1.0,1
--- a/evals-dpo.yaml
+++ b/evals-dpo.yaml
@@ -0,0 +1,15 @@
+arc_pt: 30.77
+assin2_rte: 67.78
+assin2_sts: 8.88
+bluex: 26.01
+calame_pt: 56.55 
+enem: 21.06
+faquad_nli: 43.97
+hatebr: 31.28
+hatespeech_pt: 41.23
+hellaswag_pt: 43.50 
+lambada_pt: 35.53 
+oab_exams: 26.47
+step: 6300
+truthfulqa: 41.14
+tweet_br: 22.03
--- a/evals-sft.yaml
+++ b/evals-sft.yaml
@@ -0,0 +1,15 @@
+arc_pt: 30.60
+assin2_rte: 33.42
+assin2_sts: 0.87
+bluex: 24.61
+calame_pt: 56.74
+enem: 21.06
+faquad_nli: 43.97
+hatebr: 33.33
+hatespeech_pt: 41.23
+hellaswag_pt: 42.83
+lambada_pt: 34.66
+oab_exams: 26.20
+step: 21000
+truthfulqa: 41.17
+tweet_br: 40.65
--- a/flax_model.msgpack
+++ b/flax_model.msgpack
@@ -0,0 +1,3 @@
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--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 3,
+  "do_sample": true,
+  "max_new_tokens": 1024,
+  "renormalize_logits": true,
+  "repetition_penalty": 1.2,
+  "temperature": 0.1,
+  "top_k": 50,
+  "top_p": 1.0,
+  "use_cache": true, 
+  "transformers_version": "4.42.3"
+}
--- a/logo.png
+++ b/logo.png
--- a/model.safetensors
+++ b/model.safetensors
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--- a/pytorch_model.bin
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--- a/results-multilingual-dpo.json
+++ b/results-multilingual-dpo.json
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+{
+  "results": {
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+      "acc_stderr": 0.012569442967524477,
+      "acc_norm": 0.3076923076923077,
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+      "mc2": 0.4114457861517422,
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+  "versions": {
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+    "hellaswag_pt": 1,
+    "truthfulqa_pt": 1
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+  "config": {
+    "model": "hf-auto",
+    "model_args": "pretrained=/lustre/mlnvme/data/asen_hpc-mula/Tucano-1b1-DPO",
+    "batch_size": 1,
+    "device": "cuda:0",
+    "no_cache": false,
+    "limit": null,
+    "bootstrap_iters": 100000,
+    "description_dict": {}
+  }
+}
--- a/results-multilingual-sft.json
+++ b/results-multilingual-sft.json
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+{
+  "results": {
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+      "acc_norm": 0.305982905982906,
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+      "mc2": 0.411729137477953,
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+  "versions": {
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+    "hellaswag_pt": 1,
+    "truthfulqa_pt": 1
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+  "config": {
+    "model": "hf-auto",
+    "model_args": "pretrained=/lustre/mlnvme/data/asen_hpc-mula/checkpoints-llama/slurm_job_17066349/step_21084",
+    "batch_size": 1,
+    "device": "cuda:0",
+    "no_cache": false,
+    "limit": null,
+    "bootstrap_iters": 100000,
+    "description_dict": {}
+  }
+}
--- a/results-pt-dpo.json
+++ b/results-pt-dpo.json
--- a/results-pt-sft.json
+++ b/results-pt-sft.json
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,34 @@
+{
+  "additional_special_tokens": [
+    "<instruction>",
+    "</instruction>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
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+  "add_eos_token": false,
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+  "chat_template": "{% 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 %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "eos_token_id": 2,
+  "legacy": false,
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+  "pad_token": "<pad>",
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+  "padding_side": "right",
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+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "unk_token_id": 0,
+  "use_default_system_prompt": false
+}
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