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, dtype="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
```

Sandroeth commited on 8 days ago

Commit

2e4a1f6

verified ·

1 Parent(s): b74c3b1

Update README.md

Browse files

Files changed (1) hide show

README.md +17 -0

README.md CHANGED Viewed

@@ -62,6 +62,23 @@ Dataset meliputi:
 ---
 ## Progress Training
 | Tokens | Step | Final Loss |

 ---
+Below is the performance comparison of CALI-0.1B against other prominent Small Language Models (SLMs) in the 100M+ parameter tier.
+| 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: For CALI-0.1B, the scores represent strict raw accuracies (`acc` / `acc_norm`) extracted directly from the evaluation tracker logs.*
 ## Progress Training
 | Tokens | Step | Final Loss |