Qwen35 9b NVFP4 Podcast Cleaner Sft Think
This model is a fine-tuned version of None. It has been trained using TRL.
Fuente del modelo
- Tipo
- LoRA
- Tamaño del archivo
- 888 MB
- Versión
- vadapter_model
- Actualizado
- 2 oct 2026
- Descargas
- 0
- Fuente
- Hugging Face
Descripción de la fuente
Model Card for qwen35-9b-nvfp4-podcast-cleaner-sft-think
This model is a fine-tuned version of None. 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="hudsongouge/qwen35-9b-nvfp4-podcast-cleaner-sft-think", 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: 1.8.0
- Transformers: 5.9.0
- Pytorch: 2.13.0+cu130
- Datasets: 5.0.0
- 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}
}
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 2 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- library_name: transformers model_name: qwen35-9b-nvfp4-podcast-cleaner-sft-think tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for qwen35-9b-nvfp4-podcast-cleaner-sft-think This model is a fine-tuned version of [None](https://huggingface.co/None). 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="hudsongouge/qwen35-9b-nvfp4-podcast-cleaner-sft-think", 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: 1.8.0 - Transformers: 5.9.0 - Pytorch: 2.13.0+cu130 - Datasets: 5.0.0 - 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 hudsongouge/qwen35-9b-nvfp4-podcast-cleaner-sft-think
