Instructions to use Sandroeth/cali-0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sandroeth/cali-0.1B with Transformers:

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

pipe = pipeline("text-generation", model="Sandroeth/cali-0.1B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Sandroeth/cali-0.1B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sandroeth/cali-0.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sandroeth/cali-0.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sandroeth/cali-0.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sandroeth/cali-0.1B

SGLang

How to use Sandroeth/cali-0.1B 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 "Sandroeth/cali-0.1B" \
    --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": "Sandroeth/cali-0.1B",
		"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 "Sandroeth/cali-0.1B" \
        --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": "Sandroeth/cali-0.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sandroeth/cali-0.1B with Docker Model Runner:
```
docker model run hf.co/Sandroeth/cali-0.1B
```

CALI

CALI (Computer Assistant Lightweight Intelligence) is an experimental lightweight language model trained from scratch on a limited-scale bilingual dataset consisting of Indonesian and English text.

The model was developed to explore lightweight transformer architectures, small-model efficiency, and language model training under constrained computational resources and limited datasets.

CALI is NOT a large-scale foundation model and was NOT trained on massive internet-scale datasets like modern commercial language models.

Important Notes

Due to the relatively small pretraining corpus, the model may exhibit noticeable bias toward the most recent or dominant domains seen during training. Continued pretraining, fine-tuning, or alignment is strongly recommended depending on the intended use case.

Model Details

Property	Value
Parameters	121M
Layers	11
Hidden Size	768
Attention Heads	4
KV Heads	1
Head Dimension	192
FFN Dimension	2304
Context Length	1024
Vocabulary Size	32000

Architecture

For a detailed explanation of the CALI architecture:

View Architecture Details

Pretraining

CALI was trained entirely from scratch using carefully selected and filtered datasets designed for research purposes rather than maximizing dataset size.

The training corpus includes:

English text
Indonesian text
Wikipedia
News articles
General documents
Source code

Benchmark Results

Below is the performance comparison of CALI-0.1B against several prominent Small Language Models (SLMs) in the 100M+ parameter range.

Model Name	Piqa	MMLU Math	ARC-Challenge	HellaSwag
CALI-0.1B	54.19%	28.04%	24.66%	27.00%
SmolLM2-135M	58.50%	29.90%	31.10%	43.20%
GPT-X2-125M	51.60%	27.80%	27.80%	40.50%
SmolLM-135M	56.30%	28.80%	28.80%	42.70%
MobileLLM-R1-140M-base	49.90%	24.70%	24.70%	33.90%
GPT-X-125M	50.80%	26.70%	26.70%	36.50%
GPT-2 (124M)	39.50%	22.60%	22.60%	31.50%
GPT-Neo-125M	39.40%	22.90%	22.90%	30.40%
OPT-125M	40.20%	22.90%	22.90%	31.40%

Note: The CALI-0.1B scores are strict raw accuracies (acc / acc_norm) obtained directly from the evaluation tracker logs.

Architecture graph for Sandroeth/cali-0.1B. Open in hfviewer

Training Progress

Tokens	Step	Final Loss
250M	13,564	3.53
350M	18,989	3.53
450M	24,415	4.69
614M	33,356	2.71

Notes

Experimental transformer architecture
Requires a custom inference implementation
Uses Grouped-Query Attention (GQA)
Intended for research and experimentation

Citation

If you use or reference this model in your research or projects, please cite:

@article{cali2026,
  title        = {CALI 0.1B},
  author       = {Sandroeth},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/Sandroeth/cali-0.1B}
}

Author

Sandroeth

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F32

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