Instructions to use FrontiersMind/Nandi-Mini-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrontiersMind/Nandi-Mini-150M with Transformers:

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

pipe = pipeline("text-generation", model="FrontiersMind/Nandi-Mini-150M", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FrontiersMind/Nandi-Mini-150M", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FrontiersMind/Nandi-Mini-150M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrontiersMind/Nandi-Mini-150M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrontiersMind/Nandi-Mini-150M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FrontiersMind/Nandi-Mini-150M

SGLang

How to use FrontiersMind/Nandi-Mini-150M 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 "FrontiersMind/Nandi-Mini-150M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrontiersMind/Nandi-Mini-150M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "FrontiersMind/Nandi-Mini-150M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrontiersMind/Nandi-Mini-150M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FrontiersMind/Nandi-Mini-150M with Docker Model Runner:
```
docker model run hf.co/FrontiersMind/Nandi-Mini-150M
```

vishesh-t27 commited on Apr 2

Commit

6c4fbcc

verified ·

1 Parent(s): 655e347

added tokenizer's fertility score in Readme

Browse files

Files changed (1) hide show

README.md +18 -0

README.md CHANGED Viewed

@@ -92,6 +92,24 @@ The model is trained on English and a diverse set of Indic languages, including:
 ### Note
 Mobile-LLM model checkpoints are not publicly available; their results are reported directly from the original paper. All other models have been evaluated using `lm-eval` under a consistent setup.
 ## 🚀 Usage
 ```python

 ### Note
 Mobile-LLM model checkpoints are not publicly available; their results are reported directly from the original paper. All other models have been evaluated using `lm-eval` under a consistent setup.
+## Tokenization Fertility Score across Languages
+| Language  | SmolLM3-3B | Qwen3-0.6B-Base | Sarvam-30B | Nandi-Mini-150M |
+|----------|------------|-----------------|------------|------------------|
+| English  | 1.17       | 1.16            | 1.18       | 1.18             |
+| Bengali  | 8.66       | 7.51            | 1.46       | 1.44             |
+| Gujarati | 10.47      | 9.37            | 1.70       | 1.53             |
+| Hindi    | 2.71       | 5.14            | 1.23       | 1.32             |
+| Kannada  | 16.43      | 12.96           | 2.08       | 1.90             |
+| Malayalam| 17.77      | 14.56           | 2.81       | 2.05             |
+| Marathi  | 3.73       | 6.70            | 1.77       | 1.55             |
+| Oriya    | 19.07      | 15.75           | 1.77       | 2.68             |
+| Punjabi  | 9.23       | 8.66            | 1.42       | 1.42             |
+| Tamil    | 13.56      | 10.93           | 2.35       | 2.05             |
+| Telugu   | 15.40      | 13.38           | 2.09       | 1.77             |
+| Assamese | 9.26       | 8.13            | 2.38       | 1.51             |
 ## 🚀 Usage
 ```python