GPT4All: Pilihan Model Bahasa Indonesia

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Berikut ini beberapa **model lokal (gguf) yang mendukung Bahasa Indonesia** dan bisa langsung dipakai di GPT4All + Open WebUI atau melalui CLI `llama.cpp`:

---

      1. 1. **MiaLatte‑Indo‑Mistral‑7B (Q4\_K\_M gguf)**
  • Versi instruksi dari Mistral‑7B khusus bahasa Indonesia 🇮🇩
  • Format: `.gguf`, quantization Q4\_K\_M (\~4.5 GB)
  • Cepat, seimbang antara kualitas dan ukuran ([toolify.ai][1], [huggingface.co][2])
    • Unduh lewat CLI:**

```bash pip install huggingface-hub huggingface-cli download mradermacher/MiaLatte-Indo-Mistral-7b-GGUF \

 MiaLatte-Indo-Mistral-7b.Q4_K_M.gguf --local-dir ~/gpt4all/models

```

---

      1. 2. **Mistral‑7B v0.1 (TheBloke)**
  • Model foundation multilingual, sangat efisien & cepat
  • Format: `.gguf`, quant Q4\_K\_M (\~4.11 GB) ([dataloop.ai][3])
    • Unduh:**

```bash huggingface-cli download TheBloke/Mistral-7B-v0.1-GGUF \

 mistral-7b-v0.1.Q4_K_M.gguf --local-dir ~/gpt4all/models

```

---

      1. 3. **Nous‑Hermes‑2‑Mixtral‑8x7B (advanced multilingual)**
  • Model besar hybrid Mixtral, fine‑tuned berkualitas tinggi
  • Pilihan Q4\_K\_M (\~28.5 GB RAM) hingga Q5\_K\_M (\~33 GB) ([dataloop.ai][4], [toolify.ai][1])
    • Unduh (Q4\_K\_M):**

```bash huggingface-cli download TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF \

 nous-hermes-2-mixtral-8x7b-dpo.Q4_K_M.gguf --local-dir ~/gpt4all/models

```

---

    1. 🚀 Cara pakai di GPT4All/Open WebUI

1. Letakkan file `.gguf` di `~/gpt4all/models/`. 2. Akses WebUI (`http://<server-ip>:3000`) → Settings → Local Models → **Add model** → arahkan ke file. 3. Pilih quantization Q4\_K\_M (balancing performa dan RAM).

Untuk CLI via `llama.cpp` atau `gpt4all-backend`:

```bash ./gpt4all-backend/build/bin/gpt4all-lora-quantized-ggml \

 -m ~/gpt4all/models/mistral-7b-v0.1.Q4_K_M.gguf

```

atau

```bash llama.cpp/main -m ~/gpt4all/models/MiaLatte-Indo-Mistral-7b.Q4_K_M.gguf \

 -p "Halo, apa kabar?" -n 256

```

---

    1. 🧭 Rekomendasi
  • **Server RAM ≤16 GB**: pakai **MiaLatte-Indo-Mistral** (4–6 GB).
  • **RAM 16–32 GB**: **Mistral‑7B v0.1**.
  • **RAM ≥32 GB + GPU optional**: **Nous‑Hermes‑2‑Mixtral‑8x7B** untuk performa terbaik.

---

Kalau butuh bantuan konfigurasi lebih lanjut (systemd, GPU, daemon service), tinggal bilang aja ya!

[1]: https://www.toolify.ai/ai-model/thebloke-nous-hermes-2-mixtral-8x7b-dpo-gguf?utm_source=chatgpt.com "TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF - Toolify.ai" [2]: https://huggingface.co/mradermacher/MiaLatte-Indo-Mistral-7b-GGUF?utm_source=chatgpt.com "mradermacher/MiaLatte-Indo-Mistral-7b-GGUF - Hugging Face" [3]: https://dataloop.ai/library/model/thebloke_mistral-7b-v01-gguf/?utm_source=chatgpt.com "Mistral 7B V0.1 GGUF · Models - Dataloop" [4]: https://dataloop.ai/library/model/thebloke_nous-hermes-llama2-gguf/?utm_source=chatgpt.com "Nous Hermes Llama2 GGUF · Models - Dataloop"