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---
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}}
}
``` |