---
title: Test-LoRA
canonical_url: "https://www.modelscope.cn/models/ConicCat/Test-LoRA"
md_url: "https://www.modelscope.cn/models/ConicCat/Test-LoRA.md"
repository: ConicCat/Test-LoRA
last_updated: 2026-03-17
base_model:
  - allenai/Olmo-3-7B-Instruct
base_model_relation: finetune
parameters: 639.6M
tensor_type:
  - F32
  - BF16
library_name:
  - transformer
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 16
stars: 0
tags:
  - generated_from_trainer
  - dpo
  - trl
---

# Test-LoRA

> Test-LoRA - ConicCat 在 ModelScope 开源的模型。Model Card for HumanLine

ConicCat/Test-LoRA 是 ModelScope 魔搭社区上的 639.6M 参数机器学习模型，基于 allenai/Olmo-3-7B-Instruct 构建。

- **Repository**: ConicCat/Test-LoRA
- **Parameters**: 639.6M
- **Base model**: allenai/Olmo-3-7B-Instruct
- **Tags**: generated_from_trainer, dpo, trl
- **Downloads**: 16
- **Stars**: 0
- **Last updated**: 2026-03-17

Source: https://www.modelscope.cn/models/ConicCat/Test-LoRA

---

# Model Card for HumanLine

This model is a fine-tuned version of [allenai/Olmo-3-7B-Instruct](https://huggingface.co/allenai/Olmo-3-7B-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 DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).

### Framework versions

- TRL: 0.26.2
- Transformers: 5.0.0rc2
- Pytorch: 2.8.0+cu128
- Datasets: 4.4.2
- Tokenizers: 0.22.2

## Citations

Cite DPO as:

```bibtex
@inproceedings{rafailov2023direct,
    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
    year         = 2023,
    booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```

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}}
}
```