Talos Inquisitor v5
This model is a fine-tuned version of unsloth/Qwen2.5-7B-Instruct-bnb-4bit. It has been trained using TRL. Quick start
Source du modèle
- Type
- text-generation
- Taille du fichier
- 308 MB
- Modèle de base
- unsloth/Qwen2.5-7B-Instruct-bnb-4bit
- Version
- vadapter_model
- Mis à jour
- 14 oct. 2026
- Éditeur
- @kodiboynton
- Tâches
- Génération de texte
- Source
- Hugging Face
Description de la source
Model Card for talos-inquisitor-v5
This model is a fine-tuned version of unsloth/Qwen2.5-7B-Instruct-bnb-4bit. 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="kodiboynton/talos-inquisitor-v5", 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: 0.24.0
- Transformers: 5.5.0
- Pytorch: 2.10.0
- Datasets: 4.3.0
- 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
Sources
1 sourceVérifié 17 sept.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit library_name: transformers model_name: talos-inquisitor-v5 tags: - generated_from_trainer - hf_jobs - trl - unsloth - sft licence: license --- # Model Card for talos-inquisitor-v5 This model is a fine-tuned version of [unsloth/Qwen2.5-7B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct-bnb-4bit). 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="kodiboynton/talos-inquisitor-v5", 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: 0.24.0 - Transformers: 5.5.0 - Pytorch: 2.10.0 - Datasets: 4.3.0 - 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: 34 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo kodiboynton/talos-inquisitor-v5
Source context: 9 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo kodiboynton/talos-inquisitor-v5
