README.md

---
language:
- id
license: apache-2.0
tags:
- Indonesian
- Chat
- Instruct
base_model:
- meta-llama/Llama-3.2-3B-Instruct
datasets:
- NekoFi/alpaca-gpt4-indonesia-cleaned
pipeline_tag: text-generation
model-index:
- name: FinMatcha-3B-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 75.48
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=xMaulana/FinMatcha-3B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 23.19
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=xMaulana/FinMatcha-3B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 12.39
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=xMaulana/FinMatcha-3B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 2.57
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=xMaulana/FinMatcha-3B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 5.02
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=xMaulana/FinMatcha-3B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 24.24
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=xMaulana/FinMatcha-3B-Instruct
      name: Open LLM Leaderboard
---

![image/jpeg](https://huggingface.co/xMaulana/FinMatcha-3B-Instruct/resolve/main/image.jpg)

# FinMatcha-3B-Instruct

FinMatcha is a powerful Indonesian-focused large language model (LLM) fine-tuned from the [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) base model. The model has been trained to handle a variety of conversation, with a special emphasis on understanding and generating Indonesian text.

This model has been fine-tuned on a wide array of Indonesian datasets, making it adept at handling the nuances of the Indonesian language, from formal to colloquial speech. It also supports English for bilingual applications.

## Model Details

- **Finetuned from model**: [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
- **Dataset**: [NekoFi/alpaca-gpt4-indonesia-cleaned](https://huggingface.co/datasets/NekoFi/alpaca-gpt4-indonesia-cleaned)
- **Model Size**: 3B  
- **License**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)  
- **Languages**: Indonesian, English

## How to use

### Installation

To use the Finmatcha model, install the required dependencies:

```bash
pip install transformers>=4.45
```

### Usage

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "xMaulana/FinMatcha-3B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

inputs = tokenizer("Bagaimanakah sebuah negara dapat terbentuk?", return_tensors="pt").to("cuda")
outputs = model.generate(inputs.input_ids, 
                          max_new_tokens = 2048,
                          pad_token_id=tokenizer.pad_token_id,
                          eos_token_id=tokenizer.eos_token_id,
                          temperature=0.7,
                          do_sample=True, 
                          top_k=5, 
                          top_p=0.9,
                          repetition_penalty=1.1
                         )
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations

- The model is primarily focused on the Indonesian language and may not perform as well on non-Indonesian tasks.
- As with all LLMs, cultural and contextual biases can be present.

## License

The model is licensed under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0).

## Contributing

We welcome contributions to enhance and improve Finmatcha. Feel free to open issues or submit pull requests for improvements.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/xMaulana__FinMatcha-3B-Instruct-details)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |24.13|
|IFEval (0-Shot)    |75.94|
|BBH (3-Shot)       |23.27|
|MATH Lvl 5 (4-Shot)|12.16|
|GPQA (0-shot)      | 3.47|
|MuSR (0-shot)      | 5.40|
|MMLU-PRO (5-shot)  |24.54|


# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_xMaulana__FinMatcha-3B-Instruct)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |23.81|
|IFEval (0-Shot)    |75.48|
|BBH (3-Shot)       |23.19|
|MATH Lvl 5 (4-Shot)|12.39|
|GPQA (0-shot)      | 2.57|
|MuSR (0-shot)      | 5.02|
|MMLU-PRO (5-shot)  |24.24|