dominguesm
/

mambarim-110m

@@ -1,199 +1,119 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+language:
+- pt
+license: cc-by-4.0
+tags:
+- text-generation
+- pytorch
+- LLM
+- Portuguese
+- mamba
+datasets:
+- nicholasKluge/Pt-Corpus-Instruct
+inference:
+  parameters:
+    repetition_penalty: 1.2
+    temperature: 0.8
+    top_k: 50
+    top_p: 0.85
+    max_new_tokens: 150
+widget:
+- text: "O Natal é uma"
+  example_title: Exemplo
+- text: "A muitos anos atrás, em uma galáxia muito distante, vivia uma raça de"
+  example_title: Exemplo
+- text: "Em meio a um escândalo, a frente parlamentar pediu ao Senador Silva para"
+  example_title: Exemplo
+pipeline_tag: text-generation
 ---
+# Mambarim-110M
+<p align="center">
+  <img width="350" alt="Camarim Logo" src="https://raw.githubusercontent.com/DominguesM/mambarim-110M/main/assets/mambarim-bg.png">
+</p>
+</br>
+##  Model Summary
+WIP
+## Details
+- **Architecture:** a Mamba model pre-trained via causal language modeling
+- **Size:** 119,930,880 parameters
+- **Context length:** 2048 tokens
+- **Dataset:** [Pt-Corpus Instruct](https://huggingface.co/datasets/nicholasKluge/Pt-Corpus-Instruct) (6.2B tokens)
+- **Language:** Portuguese
+- **Number of steps:** 758,423
+This repository has the [source code](https://github.com/DominguesM/mambarim-110M/) used to train this model.
+## Intended Uses
+WIP
+## Out-of-scope Use
+WIP
+## Basic usage
+You need to install `transformers` from `main` until `transformers=4.39.0` is released.
+```bash
+pip install git+https://github.com/huggingface/transformers@main
+```
+We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using:
+```bash
+pip install causal-conv1d>=1.2.0
+pip install mamba-ssm
+```
+You can use the classic `generate` API:
+```python
+>>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
+>>> import torch
+>>> tokenizer = AutoTokenizer.from_pretrained("dominguesm/mambarim-110m")
+>>> model = MambaForCausalLM.from_pretrained("dominguesm/mambarim-110m")
+>>> input_ids = tokenizer("O Natal é uma", return_tensors="pt")["input_ids"]
+>>> out = model.generate(
+    input_ids,
+    repetition_penalty=1.2,
+    temperature=0.8,
+    top_k=50,
+    top_p=0.85,
+    do_sample=True,
+    max_new_tokens=10
+)
+>>> print(tokenizer.batch_decode(out))
+["<s> O Natal é uma data em que as pessoas passam horas de lazer e"]
+```
+## Benchmarks
+Evaluations on Brazilian Portuguese benchmarks were performed using a [Portuguese implementation of the EleutherAI LM Evaluation Harness](https://github.com/eduagarcia/lm-evaluation-harness-pt) (created by [Eduardo Garcia](https://github.com/eduagarcia/lm-evaluation-harness-pt)).
+|                    | **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       |
+| **Mambarim-110M**  | 40.64          | 3.11           | 13.90     | 14.76    | 00.15          | 49.00      | 20.27         | 17.72       |
+| Samba-1.1B         | 33.33          | 1.30           | 8.07      | 10.22    | 17.72          | 35.79      | 15.03         | 17.35       |