OmniAssist SFT IT Helpdesk
This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct. It has been trained using TRL.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 16,7 MB
- Modelo base
- Qwen/Qwen2.5-1.5B-Instruct
- Versão
- vRevision 134e86d
- Atualizado
- 22 de out. de 2026
- Publicador
- @YashShukla007
- Downloads
- 7
- Parâmetros
- 5B
- Fonte
- Hugging Face
Descrição da fonte
Model Card for it_helpdesk
This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-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="None", 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
- PEFT 0.17.0
- TRL: 0.20.0
- Transformers: 4.56.0
- Pytorch: 2.11.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.22.2
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}}
}
Fontes
1 fonte
Fontes
1 fonteVerificado 22 de out.
Artefatos de modelo
2 artefatos
Artefatos de modelo
2 artefatosTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: peft model_name: it_helpdesk tags: - base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct - lora - sft - transformers - trl licence: license pipeline_tag: text-generation --- # Model Card for it_helpdesk This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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="None", 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 - PEFT 0.17.0 - TRL: 0.20.0 - Transformers: 4.56.0 - Pytorch: 2.11.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.22.2 ## 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}} } ```
Source context: 7 downloads · 0 likes · Pipeline text-generation · Library peft · Repo YashShukla007/OmniAssist-SFT-IT-Helpdesk
Source context: 22 downloads · 0 likes · Pipeline text-generation · Library peft · Repo YashShukla007/OmniAssist-SFT-IT-Helpdesk
