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OmniAssist SFT IT Helpdesk

This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct. It has been trained using TRL.

TypeOtherModèle de baseQwen/Qwen2.5-1.5B-InstructVersionRevision 134e86dÉditeur@YashShukla007CClasse

Source du modèle

Type
Other
Taille du fichier
16,7 MB
Modèle de base
Qwen/Qwen2.5-1.5B-Instruct
Version
vRevision 134e86d
Mis à jour
22 oct. 2026
Téléchargements
7
Paramètres
5B
Source
Hugging Face

Description de la source

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}}
}

Sources

1 source
Hugging FacePrincipalVersion 134e86d292860201d8f4dafae365147eec89f229
huggingface.co/YashShukla007/OmniAssist-SFT-IT-Helpdesk/tree/134e86d292860201d8f4dafae365147eec89f229

Vérifié 22 oct.

Artefacts du modèle

2 artefacts

adapter_model.safetensors

safetensors · 16,7 MB · SHA-256 92950f606d4f…c61e · Hugging Face

Télécharger

checkpoint-150/adapter_model.safetensors

safetensors · 16,7 MB · SHA-256 92950f606d4f…c61e · Hugging Face

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Extraits de sources

3 extraits

--- 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