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

Libraries

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

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

pipe = pipeline("text-generation", model="FrontiersMind/Nandi-Mini-150M-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FrontiersMind/Nandi-Mini-150M-Instruct 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-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrontiersMind/Nandi-Mini-150M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use FrontiersMind/Nandi-Mini-150M-Instruct 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-Instruct" \
    --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": "FrontiersMind/Nandi-Mini-150M-Instruct",
		"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 "FrontiersMind/Nandi-Mini-150M-Instruct" \
        --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": "FrontiersMind/Nandi-Mini-150M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

vishesh-t27 commited on Apr 13

Commit

7f282bf

verified ·

1 Parent(s): a2fa178

Updated Readme.md

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@@ -38,7 +38,7 @@ Nandi-Mini-150M-Instruct brings the following key features:
 We’re just getting started with the Nandi series 🚀
-- **Nandi-Mini-150M-Tool-Calling** — Coming Soon
 - **Nandi-Mini-500M (Base + Instruct)** — Pre-Training Going On
 - **Nandi-Mini-1B (Base + Instruct)** — Pre-Training Going On
@@ -66,33 +66,6 @@ The model is trained on English and a diverse set of Indic languages, including:
 - Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Odia
-## Benchmark Results
-## 📊 Benchmark Comparison (~150M Class)
-| Model Name         | Parameters | Tokens(B) | HellaSwag | Winogrande | GPQA  | MMLU | GSM8K | HumanEval | Average |
-|------------------|---------------|------------------|----------|------------|------|------|-------|-----------|---------|
-| Mobile-LLM-125M  | 125           | 1000             | 38.90    | 53.10  | -    | -    | -     | -         | -       |
-| SmolLM-135M-Base | 135           | 600              | 42.66| 53.03  | 25.44| 25.30| 1.36  | 0.00      | 24.63   |
-| SmolLM2-135M-Base| 135           | 2000             | 43.13| 53.27  | 22.09| 24.09| 1.74  | 0.00      | 24.05   |
-| **Nandi-Mini-150M-Base-Instruct** | **150**     | **500**          | 37.20    | 52.32      | **28.57** | **28.86** | **2.58** | **4.27** | **25.63** |
-## 📊 Model Benchmark Comparison With Slightly Bigger Models (350M–600M Class)
-| Model Name            | Parameters | Tokens(B) | HellaSwag | Winogrande | GPQA  | MMLU | GSM8K | HumanEval | Average |
-|---------------------|---------------|------------------|----------|------------|------|------|-------|-----------|---------|
-| Mobile-LLM-360M     | 350           | 1000             | 49.60    | 56.59      | -    | -    | -     | -         | -       |
-| Qwen-2-0.5-Base     | 500           | 12000            | 49.01    | 57.69      | 27.23| 44.06| 10.61 | 22.56     | 35.19   |
-| Qwen2.5-0.5B-Base   | 500           | 18000            | 52.16    | 56.82      | 24.10| 47.41| 4.77  | 29.87     | 35.86   |
-| Qwen3-0.6B-Base     | 600           | 36000            | 53.77    | 59.19      | 30.80| 50.34| 15.31 | 28.04     | 39.58   |
-| SmolLM-360M-Base    | 360           | 600              | 53.33    | 57.22      | 21.20| 24.92| 2.19  | 1.21      | 26.68   |
-| SmolLM2-360M-Base   | 360           | 4000            | 56.30    | 59.19      | 25.22| 25.55| 2.88  | 0.00      | 28.19   |
-| **Nandi-Mini-150M-Base** | **150**       | 500          | 37.20| 52.32  | 28.57 | 28.86 | 2.58 | 4.27 | 25.63 |
-### Note
-Human-Eval, IfEval & GSM8K have been evaluated using Greedy-decoding for now for all models.
 ## 🚀 Usage

 We’re just getting started with the Nandi series 🚀
+- **Nandi-Mini-150M-Tool-Calling** — Coming Soon this week
 - **Nandi-Mini-500M (Base + Instruct)** — Pre-Training Going On
 - **Nandi-Mini-1B (Base + Instruct)** — Pre-Training Going On
 - Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Odia
 ## 🚀 Usage