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Nusantara-4b-Indo-Chat

Text Generation
Transformers
Safetensors
Indonesian
English
qwen2
conversational
convAI
text-generation-inference
Inference Endpoints
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metadata
library_name: transformers
widget:
  - text: Halo apa kabar?
    example_title: Identity
  - text: Apa yang dapat kamu lakukan?
    example_title: Capabilities
  - text: buatlah coding untuk Hello World.
    example_title: Coding
pipeline_tag: text-generation
tags:
  - convAI
  - conversational
license: apache-2.0
language:
  - id
  - en

Model Card for Model ID

Model Description

Nusantara is a series of Open Weight Language Model of Indonesia Language (Bahasa Indonesia). Nusantara is based from Qwen1.5 Language Model, finetuned by domain specific of datasets. As Chat-implemented language model, Nusantara is capable to do Question-Answering and respond to instructions given in Bahasa Indonesia.

  • Developed by: Kalis AI /
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Model type: [More Information Needed]
  • Language(s) (NLP): [More Information Needed]
  • License: [More Information Needed]
  • Finetuned from model: Qwen1.5-4B

Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen1.5-72B-Chat",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("kalisai/Nusantara-4B-Indo-Chat")

prompt = "Berikan saya resep memasak nasi goreng yang lezat."
messages = [
    {"role": "system", "content": "Kamu adalah Nusantara, asisten AI yang pintar."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Direct Use

[More Information Needed]

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: [More Information Needed]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation

If you use the Nusantara language model in your research or project, please cite it as:

@article{Nusantara,
  title={Nusantara: A Series of Language Model in Bahasa Indonesia},
  author={Zulfikar Aji Kusworo},
  publisher={Hugging Face}
  journal={Hugging Face Repository},
  year={2024}
}

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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