Instructions to use num1notsvn/wicara-56m-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use num1notsvn/wicara-56m-chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="num1notsvn/wicara-56m-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("num1notsvn/wicara-56m-chat")
model = AutoModelForCausalLM.from_pretrained("num1notsvn/wicara-56m-chat", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use num1notsvn/wicara-56m-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "num1notsvn/wicara-56m-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "num1notsvn/wicara-56m-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/num1notsvn/wicara-56m-chat

SGLang

How to use num1notsvn/wicara-56m-chat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "num1notsvn/wicara-56m-chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "num1notsvn/wicara-56m-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "num1notsvn/wicara-56m-chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "num1notsvn/wicara-56m-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use num1notsvn/wicara-56m-chat with Docker Model Runner:
```
docker model run hf.co/num1notsvn/wicara-56m-chat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Wicara 56M Chat

Weight-efficient Indonesian Conversational Architecture, Research Artifact

A 56-million-parameter Indonesian conversational model that we trained from scratch on a single consumer laptop (NVIDIA RTX 4050 6 GB).

Wicara (from Sanskrit vicāra): speech, discourse.

Data, tokenizer, architecture, training, and SFT pipeline were built directly in PyTorch — no off-the-shelf model, no external API. This model is the conversational and instruction-tuned version (SFT v5), fine-tuned directly on top of our base model: num1notsvn/wicara-56m-base.

Author: Bagus Ardin Prayoga (@num1notsvn)
Base Model: num1notsvn/wicara-56m-base
Language: Indonesian (id)
Parameters: 56.0M (45.5M non-embedding)
Architecture: LLaMA-style (Pre-norm RMSNorm, RoPE, SwiGLU, Grouped-Query Attention, Tied Embeddings)
Type: Conversational / Instruction-tuned (SFT v5)
License: Apache-2.0
Source Code: GitHub - bagusardin25/WicaraLLM

Architecture Specifications

Component	Specification
Total Parameters	56,027,520
Non-Embedding Parameters	45,541,120
Layers (`n_layer`)	10
Hidden Dimension (`d_model`)	640
Attention Mechanism	Grouped-Query Attention (10 query heads / 5 KV heads, GQA 2:1)
Feed-Forward Dimension (`d_ffn`)	1,728 (SwiGLU)
Head Dimension (`head_dim`)	64
Context Length	512 tokens
Vocabulary Size	16,384 byte-level BPE (4.26 chars/token on Indonesian text)
Positional Embedding	RoPE ($\theta = 10000.0$)
Normalization	Pre-norm RMSNorm ($\epsilon = 10^{-5}$)
Embedding Tying	Yes (`lm_head.weight == tok_emb.weight`)

Training Details

Pretraining (`wicara-56m-base`)

Data: 1.12 billion tokens (3,985,535 documents) curated exclusively from Indonesian texts:
- OpenSubtitles v2024 (43.2%)
- FineWeb-2 ind_Latn (18.7%)
- Indonesian Wikipedia (15.5%)
- Cendol v2 (11.5%)
- Aya Collection (10.8%)
- TED2020 (0.3%)
Compute: Single RTX 4050 6 GB laptop (~11.1 hours, 28,017 tokens/sec).
Validation Loss: 3.0505 (Perplexity: 21.2).

Supervised Fine-Tuning (SFT v5 - `wicara-56m-chat`)

Base Checkpoint: Initialized directly from wicara-56m-base (step 17,010).
Data: ~40,000 conversational and instruction examples mined from Aya Collection, Cendol v2, and subtitles, augmented with 591 curated handwritten samples to anchor persona ("Wicara") and constrain hallucinations.
Loss Masking: Masked cross-entropy loss computed strictly over assistant turn tokens (user and system prompts are unmasked).
Safety: Built-in deterministic guardrails for crisis prompts and special-token input sanitization (src/infer/pencegat.py).

Quickstart & Usage

You can run Wicara using the Hugging Face transformers library directly:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "num1notsvn/wicara-56m-chat"
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(device)

# Prepare conversation
messages = [
    {"role": "system", "content": "Kamu adalah asisten AI berbahasa Indonesia yang ramah."},
    {"role": "user", "content": "Halo! Siapa namamu dan kamu bisa apa?"},
]

# Apply chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(device)

# Generate response
outputs = model.generate(
    **inputs,
    max_new_tokens=160,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.15,
    do_sample=True,
    eos_token_id=[tokenizer.eos_token_id, 2],
)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Limitations & Intended Use

Resource-Constrained Research: Wicara 56M is designed to demonstrate efficient small-language-model architecture for Indonesian dialogue running locally on edge and consumer devices.
Factual Knowledge: Due to its compact size (56M parameters), the model should not be used as an authoritative factual reference without external verification or RAG (Retrieval-Augmented Generation).
Context Length: The context window is 512 tokens.

Citation & License

Released under the Apache-2.0 License.

@misc{ardin2026wicara,
  title={Wicara: A Weight-efficient Indonesian Conversational Architecture Built from Scratch},
  author={Bagus Ardin Prayoga},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/num1notsvn/wicara-56m-chat}}
}

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