README.md

---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: Philosopher
tags:
- generated_from_trainer
- sft
- trl
licence: license
---

# Model Card for Philosopher

This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
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:
    
```bibtex
@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}}
}
```