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
```

aksinghyaani commited on Apr 2

Commit

459a8eb

verified ·

1 Parent(s): 63c78ab

Update README.md

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README.md +3 -3

README.md CHANGED Viewed

@@ -95,10 +95,11 @@ Mobile-LLM model checkpoints are not publicly available; their results are repor
 ## 🚀 Usage
 ```python
-! pip install transformers=='5.4.0'
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model_name = "Rta-AILabs/Nandi-mini-150M"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
@@ -107,7 +108,6 @@ model = AutoModelForCausalLM.from_pretrained(
     model_name,
     trust_remote_code=True,
     device_map="auto",
-    torch_dtype=torch.bfloat16
 )
 prompt = "Newton's Second Law"
@@ -116,7 +116,7 @@ model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
 outputs = model.generate(
     **model_inputs,
     max_new_tokens=200,
-    do_sample=False,
     temperature=0.3,
     top_k=20,
     repetition_penalty=1.1,

 ## 🚀 Usage
 ```python
+!pip install transformers=='5.4.0'
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model_name = "Rta-AILabs/Nandi-mini-150M"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
     model_name,
     trust_remote_code=True,
     device_map="auto",
 )
 prompt = "Newton's Second Law"
 outputs = model.generate(
     **model_inputs,
     max_new_tokens=200,
+    do_sample=True,
     temperature=0.3,
     top_k=20,
     repetition_penalty=1.1,