Instructions to use jodiox/olmo3-7b-zh-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use jodiox/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 jodiox/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 jodiox/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 jodiox/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="jodiox/olmo3-7b-zh-lora", max_seq_length=2048, )
jodiox/olmo3-7b-zh-lora
基于 allenai/Olmo-3-7B-Instruct 的 LoRA SFT 微调版本。
训练配置
| 参数 | 值 |
|---|---|
| 基座模型 | allenai/Olmo-3-7B-Instruct |
| 量化 | 4bit (bitsandbytes) |
| LoRA 秩 r | 16 |
| LoRA alpha | 16 |
| 学习率 | 1e-4 |
| Epochs | 2 |
| 数据集 | jodiox/my-sft-dataset |
| 训练框架 | unsloth + TRL SFTTrainer |
用法
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="jodiox/olmo3-7b-zh-lora",
max_seq_length=2048,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
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Model tree for jodiox/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