Instructions to use picard47at/punctuation_1024_0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use picard47at/punctuation_1024_0.6B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("picard47at/punctuation_1024_0.6B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
End of training
Browse files- README.md +7 -7
- adapter_model.safetensors +1 -1
README.md
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---
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base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
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library_name: transformers
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model_name:
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tags:
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- generated_from_trainer
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- unsloth
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licence: license
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---
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# Model Card for
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This model is a fine-tuned version of [unsloth/qwen3-0.6b-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-0.6b-unsloth-bnb-4bit).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="picard47at/
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/picardtseng-pesi/punctuation/runs/
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.
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- Transformers: 4.
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- Pytorch: 2.7.0
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- Datasets: 3.6.0
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- Tokenizers: 0.21.0
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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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
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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---
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base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
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library_name: transformers
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model_name: punctuation_1024_0.6B
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tags:
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- generated_from_trainer
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- unsloth
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licence: license
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---
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# Model Card for punctuation_1024_0.6B
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This model is a fine-tuned version of [unsloth/qwen3-0.6b-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-0.6b-unsloth-bnb-4bit).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="picard47at/punctuation_1024_0.6B", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/picardtseng-pesi/punctuation/runs/yzr3536d)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.18.1
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- Transformers: 4.52.4
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- Pytorch: 2.7.0
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- Datasets: 3.6.0
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- Tokenizers: 0.21.0
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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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},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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adapter_model.safetensors
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