Instructions to use complexly/olmo3-7b-zh-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use complexly/olmo3-7b-zh-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("complexly/olmo3-7b-zh-lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use complexly/olmo3-7b-zh-lora with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for complexly/olmo3-7b-zh-lora to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for complexly/olmo3-7b-zh-lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for complexly/olmo3-7b-zh-lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="complexly/olmo3-7b-zh-lora", max_seq_length=2048, )
complexly/olmo3-7b-zh-lora
Lora版本:基于allenai/Olmo-3-7B-Instruct,使用complexly/my-sft-dataset(根据国际能源署能源技术展望2026英文报告生成的中文问答对)进行微调,掌握能源技术领域的新知识和逻辑。
数据来源
- 训练数据:complexly/my-sft-dataset
关键训练配置
- Learning Rate:1.0e-4
- Warmup:5%
- Epochs:4 epoch
- r: 16
- lora_alpha: 16
- per_device_train_batch_size: 8
- gradient_accumulation_steps: 2
Inference Providers NEW
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Model tree for complexly/olmo3-7b-zh-lora
Base model
allenai/Olmo-3-1025-7B Finetuned
allenai/Olmo-3-7B-Instruct-SFT Finetuned
allenai/Olmo-3-7B-Instruct-DPO Finetuned
allenai/Olmo-3-7B-Instruct