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

f5213b6

verified ·

1 Parent(s): 4331f85

updated readme.md

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@@ -67,7 +67,7 @@ The model is trained on English and a diverse set of Indic languages, including:
 ## 📊 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   |
@@ -77,7 +77,7 @@ The model is trained on English and a diverse set of Indic languages, including:
 ## 📊 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   |

 ## 📊 Benchmark Comparison (Nandi-150M Focus)
+| 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   |
 ## 📊 Model Benchmark Comparison With 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   |