Instructions to use tcotter/DeepSeek-R1-Qwen-1.5B-unsloth-bnb-4bit-LoRA-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tcotter/DeepSeek-R1-Qwen-1.5B-unsloth-bnb-4bit-LoRA-Adapter with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tcotter/DeepSeek-R1-Qwen-1.5B-unsloth-bnb-4bit-LoRA-Adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Uploaded model
- Developed by: tcotter
- License: apache-2.0
- Finetuned from model : unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit
Finetuned Qwen1.5B (R1 Distilled Version) on this dataset, which comes from this dataset but with an additional "summary" produced by an in-house synthetic data generator.
This LoRA is therefore a LoRA which helps the model return a "\n\nFinal Answer: ..." after it's reasoning and initial response steps.
See this paper for more details.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
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Base model
deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B