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Model: hwshhh/deepseek-merged-2
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---
library_name: peft
model_name: deepseek-rjua-qa
tags:
- base_model:adapter:/root/notebook/dataset/deepseek-ai/deepseek-coder-1.3b-base
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
base_model: /root/notebook/dataset/deepseek-ai/deepseek-coder-1.3b-base
---
# Model Card for deepseek-rjua-qa
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="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 0.16.0
- TRL: 0.19.1
- Transformers: 4.53.2
- Pytorch: 2.6.0+metax2.33.0.5
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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}}
}
```

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"tie_word_embeddings": false,
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