Instructions to use INTISARI/intisari-indonesian-chat-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use INTISARI/intisari-indonesian-chat-v4 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf INTISARI/intisari-indonesian-chat-v4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf INTISARI/intisari-indonesian-chat-v4:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf INTISARI/intisari-indonesian-chat-v4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf INTISARI/intisari-indonesian-chat-v4:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf INTISARI/intisari-indonesian-chat-v4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf INTISARI/intisari-indonesian-chat-v4:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf INTISARI/intisari-indonesian-chat-v4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf INTISARI/intisari-indonesian-chat-v4:Q4_K_M

Use Docker

docker model run hf.co/INTISARI/intisari-indonesian-chat-v4:Q4_K_M

LM Studio
Jan
Ollama
How to use INTISARI/intisari-indonesian-chat-v4 with Ollama:
```
ollama run hf.co/INTISARI/intisari-indonesian-chat-v4:Q4_K_M
```

Unsloth Studio

How to use INTISARI/intisari-indonesian-chat-v4 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for INTISARI/intisari-indonesian-chat-v4 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for INTISARI/intisari-indonesian-chat-v4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for INTISARI/intisari-indonesian-chat-v4 to start chatting

Docker Model Runner
How to use INTISARI/intisari-indonesian-chat-v4 with Docker Model Runner:
```
docker model run hf.co/INTISARI/intisari-indonesian-chat-v4:Q4_K_M
```

Lemonade

How to use INTISARI/intisari-indonesian-chat-v4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull INTISARI/intisari-indonesian-chat-v4:Q4_K_M

Run and chat with the model

lemonade run user.intisari-indonesian-chat-v4-Q4_K_M

List all available models

lemonade list

Atomic Chat

INTISARI-CHAT-V4

INTISARI-CHAT-V4 adalah LLM - LARGE LANGUAGE MODEL BAHASA INDONESIA Model chat Bahasa Indonesia berukuran kecil (~58.5M params) untuk percakapan natural casual.

Demo

Coba model secara langsung:

https://intisari.flatseek.io

Statistik

Parameter	Nilai
Parameters	58.5M
Training Samples	50,047
Context Length	512 tokens
Epochs	3
Validation Loss	1.236
Validation PPL	3.44
Validation Accuracy	71.0%

Evaluasi

Metric	Value
Avg tokens	13.21
Distinct-2	73.8%
Distinct-3	89.0%
Degeneration Rate	3.0%
Topic Drift	15.0%

Arsitektur

vocab_size:     8196
n_layers:        20
n_heads:         8
n_kv_heads:     2
hidden_dim:      512
ffn_dim:         1408
context_length:   512
rope_theta:      10000
activation:      SwiGLU
normalization:   RMSNorm

Training

Model dilatih menggunakan Flatbuild dengan 50,047 samples percakapan casual Bahasa Indonesia.

Topik percakapan:

Greeting & Opening
Small Talk
Question → Answer
Follow-up
Acknowledgement
Empathy & Emotional Support
Humor & Banter
Requests & Refusal
Topic Management
Conversation Ending

Training History

Epoch	Val Loss	PPL	Accuracy
1	2.296	9.94	49.0%
2	1.665	5.28	61.1%
3	1.236	3.44	71.0%

Turn Distribution

Turns	Percentage
2 turns	64.5%
3 turns	7.0%
4 turns	22.0%
5+ turns	6.5%

Evaluasi Generation

Prinsip Dataset

Teach the model how to converse naturally in Indonesian, not how to answer knowledge questions.

Prioritas: Naturalness > Coherence > Appropriateness > Diversity

Contoh Generation

Greeting

Prompt: <|user|>\nHalo\n<|assistant|>\n Output: Halo, aku baik. Kamu?

Food

Prompt: <|user|>\nUdah makan?\n<|assistant|>\n Output: Udah, kamu?

Emotion

Prompt: <|user|>\nGue capek banget\n<|assistant|>\n Output: Parah juga ya. Banyak kerjaan?

Casual

Prompt: <|user|>\nWeekend mau ngapain?\n<|assistant|>\n Output: Nggak tau nih. Lo?

Keterbatasan

Model kecil (~58.5M params), kemampuan terbatas
Tidak punya pengetahuan dunia yang luas
Generasi masih pendek

Lisensi

Apache 2.0

Dilatih menggunakan Flatbuild

Downloads last month: 140

GGUF

Model size

60.6M params

Architecture

llama

Hardware compatibility

4-bit

View +1 variant

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intisari-indonesian-chat-v4