Instructions to use TanishkB/RandomNumberGenerator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TanishkB/RandomNumberGenerator with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TanishkB/RandomNumberGenerator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TanishkB/RandomNumberGenerator")
model = AutoModelForCausalLM.from_pretrained("TanishkB/RandomNumberGenerator", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TanishkB/RandomNumberGenerator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TanishkB/RandomNumberGenerator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TanishkB/RandomNumberGenerator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TanishkB/RandomNumberGenerator

SGLang

How to use TanishkB/RandomNumberGenerator 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 "TanishkB/RandomNumberGenerator" \
    --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": "TanishkB/RandomNumberGenerator",
		"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 "TanishkB/RandomNumberGenerator" \
        --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": "TanishkB/RandomNumberGenerator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TanishkB/RandomNumberGenerator with Docker Model Runner:
```
docker model run hf.co/TanishkB/RandomNumberGenerator
```

Model Card for Philosopher

This model is a fine-tuned version of google/gemma-3-270m-it. It has been trained using TRL.

Quick start

from transformers import pipeline

# Load text-generation pipeline
generator = pipeline(
    "text-generation",
    model="TanishkB/RandomNumberGenerator",
    device=-1   # use 0 if you have GPU
)

print("Chat with it — type 'exit' to quit.")

while True:
    user_input = input(">> ").strip()
    if user_input.lower() in ("exit", "quit"):
        break

    # Build single-turn prompt (no history)
    prompt = f"User: {user_input}\nAssistant:"

    # Generate reply
    response = generator(
        prompt,
        max_new_tokens=64,
        return_full_text=False
    )[0]["generated_text"]

    # Clean up model output (remove repeated labels if any)
    reply = response.strip()
    if reply.lower().startswith("assistant:"):
        reply = reply[len("assistant:"):].strip()

    print(reply)

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.21.0
Transformers: 4.55.1
Pytorch: 2.6.0+cu124
Datasets: 4.0.0
Tokenizers: 0.21.4

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

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Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for TanishkB/RandomNumberGenerator

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

(1168)

this model