Smart Calendar Qwen 0.5b Sft
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct. It has been trained using TRL.
Fonte do modelo
- Tipo
- LoRA
- Tamanho do arquivo
- 8,27 MB
- Modelo base
- Qwen/Qwen2.5-0.5B-Instruct
- Versão
- vadapter_model
- Atualizado
- 10 de out. de 2026
- Publicador
- @kohantika
- Downloads
- 0
- Parâmetros
- 5B
- Fonte
- Hugging Face
Descrição da fonte
Model Card for smart-calendar-qwen-0.5b-sft
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct. It has been trained using TRL.
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="kohantika/smart-calendar-qwen-0.5b-sft", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Training procedure
This model was trained with SFT.
Framework versions
- TRL: 1.2.0
- Transformers: 5.6.2
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.4
- Tokenizers: 0.22.2
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}
}
Fontes
1 fonte
Fontes
1 fonteVerificado 18 de set.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: transformers model_name: smart-calendar-qwen-0.5b-sft tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for smart-calendar-qwen-0.5b-sft This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python 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="kohantika/smart-calendar-qwen-0.5b-sft", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 1.2.0 - Transformers: 5.6.2 - Pytorch: 2.10.0+cu128 - Datasets: 4.8.4 - Tokenizers: 0.22.2 ## Citations Cite TRL as: ```bibtex @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} } ```
Source context: 0 downloads · 0 likes · Library transformers · Repo kohantika/smart-calendar-qwen-0.5b-sft
Source context: 0 downloads · 0 likes · Library transformers · Repo kohantika/smart-calendar-qwen-0.5b-sft
