SmolLM2-1.7B-telegram-chats

Model Card for SmolLM2-1.7B-telegram-chats

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-1.7B on the Offlin33er/telegram-chat-export-synthetic dataset. It has been trained using TRL.

Quantized GGUF build: Offlin33er/SmolLM2-1.7B-telegram-chats-GGUF (f16 + Q4_K_M, runs in LM Studio / llama.cpp). Live demo: Offlin33er/telegram-chat-generator — generate fake Telegram-export chats on a free CPU Space.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Offlin33er/SmolLM2-1.7B-telegram-chats", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

<img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio" title="Visualize in Trackio" width="150" height="24"/>

This model was trained with SFT.

Framework versions

• TRL: 1.13.0
• Transformers: 5.17.0
• Pytorch: 2.14.0
• Datasets: 5.0.1
• Tokenizers: 0.23.2

Citations

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}