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

a2fa178

verified ·

1 Parent(s): 9457222

Updated readme.md

Browse files

Files changed (1) hide show

README.md +24 -14

README.md CHANGED Viewed

@@ -100,12 +100,17 @@ Human-Eval, IfEval & GSM8K have been evaluated using Greedy-decoding for now for
 ```python
 !pip install transformers=='5.4.0'
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "Rta-AILabs/Nandi-mini-150M-Instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -113,14 +118,18 @@ model = AutoModelForCausalLM.from_pretrained(
     device_map="auto",
 ).eval()
-prompt = """
-Explain Newton's second Law of Motion
-"""
-model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
-outputs = model.generate(
-    **model_inputs,
-    max_new_tokens=50,
     do_sample=True,
     temperature=0.3,
     top_k=20,
@@ -128,10 +137,11 @@ outputs = model.generate(
     top_p=0.95
 )
-response = tokenizer.decode(
-    outputs[0],
-    skip_special_tokens=True,
-)
 print(response)
 ```

 ```python
 !pip install transformers=='5.4.0'
+from transformers import AutoTokenizer, AutoModelForCausalLM
+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,
     device_map="auto",
 ).eval()
+prompt = "Explain newton's second law of motion"
+messages = [
+    {"role": "user", "content": prompt}
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **inputs,
+    max_new_tokens=500,
     do_sample=True,
     temperature=0.3,
     top_k=20,
     top_p=0.95
 )
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```