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

2e8c58c

verified ·

1 Parent(s): e3827cb

Update README.md

Browse files

Files changed (1) hide show

README.md +17 -7

README.md CHANGED Viewed

@@ -109,16 +109,16 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 prompt = "Newton's Second Law"
-inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
 outputs = model.generate(
-    inputs,
     max_new_tokens=200,
-    do_sample=True,
-    temperature=0.3,
     top_k=20,
-    top_p=0.95,
-    repetition_penalty=1.105
 )
 response = tokenizer.decode(
@@ -127,4 +127,14 @@ response = tokenizer.decode(
 )
 print(response)
-```

 )
 prompt = "Newton's Second Law"
+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,
+    top_p=0.95
 )
 response = tokenizer.decode(
 )
 print(response)
+```
+## 📬 Feedback & Suggestions
+We’d love to hear your thoughts, feedback, and ideas!
+- **Email:** support@rtaailabs.com
+- **LinkedIn:** https://www.linkedin.com/company/rta-ai-lab
+- **X (Twitter):** https://x.com