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 4

Commit

c6a866f

verified ·

1 Parent(s): 4cbe55e

updated Readme.md

Browse files

Files changed (1) hide show

README.md +18 -0

README.md CHANGED Viewed

@@ -92,6 +92,22 @@ 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. Human-Eval & GSM8K have been evaluated using Greedy-decoding for now for all models.
 ## Tokenization Fertility Score across Languages
 | Language  | SmolLM3-3B | Qwen3-0.6B-Base | Sarvam-1 | Nandi-Mini-150M |
@@ -110,6 +126,7 @@ Mobile-LLM model checkpoints are not publicly available; their results are repor
 | Assamese  | 9.26       | 8.13            | 4.31     | **1.51**             |
 ## 🚀 Usage
 ```python
@@ -153,6 +170,7 @@ print(response)
 ```
 ## 📬 Feedback & Suggestions
 We’d love to hear your thoughts, feedback, and ideas!

 ### 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. Human-Eval & GSM8K have been evaluated using Greedy-decoding for now for all models.
+## Performance onf Finetuned Tasks
+#### CrossSum-Hindi (CHRF) Results
+We finetuned our model and other open source models on [Google's IndicGenBench](https://github.com/google-research-datasets/indic-gen-bench/) Crossum-Hindi. Nandi-mini-150M was able to outperform other models.
+| Base Model              | Before Finetune | After Finetune |
+|------------------------|-----------------|----------------|
+| Qwen-2-0.5-Base        | 0.09            | 4.22           |
+| Qwen2.5-0.5B-Base      | 0.43            | 4.18           |
+| SmolLM-135M-Base       | 0.09            | 2.55           |
+| SmolLM-360M-Base       | 0.09            | 2.99           |
+| SmolLM2-135M-Base      | 0.09            | 2.67           |
+| SmolLM2-360M-Base      | 0.12            | 3.51           |
+| Nandi-mini-150M        | 0.10            | **4.37**       |
 ## Tokenization Fertility Score across Languages
 | Language  | SmolLM3-3B | Qwen3-0.6B-Base | Sarvam-1 | Nandi-Mini-150M |
 | Assamese  | 9.26       | 8.13            | 4.31     | **1.51**             |
 ## 🚀 Usage
 ```python
 ```
 ## 📬 Feedback & Suggestions
 We’d love to hear your thoughts, feedback, and ideas!