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

4331f85

verified ·

1 Parent(s): 39d4144

updated Readme.md

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README.md +36 -10

README.md CHANGED Viewed

@@ -16,14 +16,14 @@ pipeline_tag: text-generation
 library_name: transformers
 ---
-# Nandi-150M
 ## Introduction
-Nandi-150M is a compact, efficient multilingual language model designed for strong performance in resource-constrained environments. It is trained from scratch on **525 billion tokens** and supports **English and 10 Indic languages**.
-Nandi-150M focuses on maximizing performance per parameter through architectural efficiency rather than scale. It is optimized for edge devices, on-prem deployments, and low-latency applications, making it ideal for resource-constrained environments.
-Nandi-150M brings the following key features:
 - Strong **multilingual capability** across English and Indic languages
 - Efficient design enabling **high performance at small scale (150M parameters)**
@@ -34,10 +34,10 @@ Nandi-150M brings the following key features:
 We’re just getting started with the Nandi series 🚀
-- **Nandi-150M (Base)** — *Available now*
-- **Nandi-150M (Instruct)** — Coming soon (open-sourced)
-- **Nandi-500M (Base + Instruct)** — Planned next
-- **Nandi-1B (Base + Instruct)** — Final milestone in the current roadmap
 We are actively working on expanding the Nandi family to cover a wider range of use cases—from lightweight edge deployments to more capable instruction-tuned systems.
@@ -48,7 +48,7 @@ We are actively working on expanding the Nandi family to cover a wider range of
 Stay tuned!
-**This repo contains the base Nandi-150M model**, which has the following features:
 - Type: Causal Language Model
 - Training Stage: Pretraining (from scratch)
@@ -61,7 +61,33 @@ Stay tuned!
 The model is trained on English and a diverse set of Indic languages, including:
-- Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Odia
 ## 🚀 Usage

 library_name: transformers
 ---
+# Nandi-Mini-150M
 ## Introduction
+Nandi-Mini-150M is a compact, efficient multilingual language model designed for strong performance in resource-constrained environments. It is trained from scratch on **525 billion tokens** and supports **English and 10 Indic languages**.
+Nandi-Mini-150M focuses on maximizing performance per parameter through architectural efficiency rather than scale. It is optimized for edge devices, on-prem deployments, and low-latency applications, making it ideal for resource-constrained environments.
+Nandi-Mini-150M brings the following key features:
 - Strong **multilingual capability** across English and Indic languages
 - Efficient design enabling **high performance at small scale (150M parameters)**
 We’re just getting started with the Nandi series 🚀
+- **Nandi-Mini-150M (Base)** — *Available now*
+- **Nandi-Mini-150M (Instruct)** — Coming soon (open-sourced)
+- **Nandi-Mini-500M (Base + Instruct)** — Planned next
+- **Nandi-Mini-1B (Base + Instruct)** — Final milestone in the current roadmap
 We are actively working on expanding the Nandi family to cover a wider range of use cases—from lightweight edge deployments to more capable instruction-tuned systems.
 Stay tuned!
+**This repo contains the base Nandi-Mini-150M model**, which has the following features:
 - Type: Causal Language Model
 - Training Stage: Pretraining (from scratch)
 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 (Nandi-150M Focus)
+| Model Name         | Parameters (M) | Tokens Budget (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** | **150**     | **500**          | 37.20    | 52.32      | **28.57** | **28.86** | **2.58** | **4.27** | **25.63** |
+## 📊 Model Benchmark Comparison With Bigger Models (350M–600M Class)
+| Model Name            | Parameters (M) | Tokens Budget (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           | 40000            | 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 |
 ## 🚀 Usage