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| Name | Quant method | Size |
|---|---|---|
| Nusantara-1.8b-Indo-Chat.Q2_K.gguf | Q2_K | 0.79GB |
| Nusantara-1.8b-Indo-Chat.Q3_K_S.gguf | Q3_K_S | 0.89GB |
| Nusantara-1.8b-Indo-Chat.Q3_K.gguf | Q3_K | 0.95GB |
| Nusantara-1.8b-Indo-Chat.Q3_K_M.gguf | Q3_K_M | 0.95GB |
| Nusantara-1.8b-Indo-Chat.Q3_K_L.gguf | Q3_K_L | 0.98GB |
| Nusantara-1.8b-Indo-Chat.IQ4_XS.gguf | IQ4_XS | 1.01GB |
| Nusantara-1.8b-Indo-Chat.Q4_0.gguf | Q4_0 | 1.04GB |
| Nusantara-1.8b-Indo-Chat.IQ4_NL.gguf | IQ4_NL | 1.05GB |
| Nusantara-1.8b-Indo-Chat.Q4_K_S.gguf | Q4_K_S | 1.08GB |
| Nusantara-1.8b-Indo-Chat.Q4_K.gguf | Q4_K | 1.13GB |
| Nusantara-1.8b-Indo-Chat.Q4_K_M.gguf | Q4_K_M | 1.13GB |
| Nusantara-1.8b-Indo-Chat.Q4_1.gguf | Q4_1 | 1.13GB |
| Nusantara-1.8b-Indo-Chat.Q5_0.gguf | Q5_0 | 1.22GB |
| Nusantara-1.8b-Indo-Chat.Q5_K_S.gguf | Q5_K_S | 1.24GB |
| Nusantara-1.8b-Indo-Chat.Q5_K.gguf | Q5_K | 1.28GB |
| Nusantara-1.8b-Indo-Chat.Q5_K_M.gguf | Q5_K_M | 1.28GB |
| Nusantara-1.8b-Indo-Chat.Q5_1.gguf | Q5_1 | 1.31GB |
| Nusantara-1.8b-Indo-Chat.Q6_K.gguf | Q6_K | 1.47GB |
| Nusantara-1.8b-Indo-Chat.Q8_0.gguf | Q8_0 | 1.82GB |

apply_chat_template to show you how to load the tokenizer and model and how to generate contents.1from transformers import AutoModelForCausalLM, AutoTokenizer
2device = "cuda" # the device to load the model onto
3
4model = AutoModelForCausalLM.from_pretrained(
5 "kalisai/Nusantara-1.8B-Indo-Chat",
6 torch_dtype="auto",
7 device_map="auto"
8)
9tokenizer = AutoTokenizer.from_pretrained("kalisai/Nusantara-1.8B-Indo-Chat")
10
11prompt = "Berikan saya resep memasak nasi goreng yang lezat."
12messages = [
13 {"role": "system", "content": "Kamu adalah Nusantara, asisten AI yang pintar."},
14 {"role": "user", "content": prompt}
15]
16text = tokenizer.apply_chat_template(
17 messages,
18 tokenize=False,
19 add_generation_prompt=True
20)
21model_inputs = tokenizer([text], return_tensors="pt").to(device)
22
23generated_ids = model.generate(
24 model_inputs.input_ids,
25 max_new_tokens=512
26)
27generated_ids = [
28 output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
29]
30
31response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]@misc{zulfikar_aji_kusworo_2024,
title={Nusantara: A Series of Versatile Open Weight Language Model of Bahasa Indonesia},
author={Zulfikar Aji Kusworo},
publisher={Hugging Face}
journal={Hugging Face Repository},
year={2024}
url = {https://huggingface.co/kalisai}
}