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

b0185c2

verified ·

1 Parent(s): 7a36923

Update README.md

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README.md +4 -8

README.md CHANGED Viewed

@@ -37,7 +37,7 @@ 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 this week
 - **Nandi-Mini-500M (Base + Instruct)** — Pre-Training Going On
 - **Nandi-Mini-1B (Base + Instruct)** — Pre-Training Going On
@@ -68,18 +68,14 @@ import torch
 model_name = "Rta-AILabs/Nandi-Mini-150M-Instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     trust_remote_code=True,
     dtype=torch.bfloat16
-).cuda()
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    trust_remote_code=True,
-    device_map="auto",
-).eval()
 prompt = "Explain newton's second law of motion"

 We’re just getting started with the Nandi series 🚀
+- **Nandi-Mini-150M-Specialized-Model-for-Tool-Calling** — Coming Soon this week
 - **Nandi-Mini-500M (Base + Instruct)** — Pre-Training Going On
 - **Nandi-Mini-1B (Base + Instruct)** — Pre-Training Going On
 model_name = "Rta-AILabs/Nandi-Mini-150M-Instruct"
+device = "cuda" if torch.cuda.is_available() else "cpu"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     trust_remote_code=True,
     dtype=torch.bfloat16
+).device()
 prompt = "Explain newton's second law of motion"