Text Generation
PEFT
Safetensors
qlora
lora
qwen2.5
log-analysis
incident-triage
root-cause-analysis
devops
sre
structured-output
local-inference
learning-grade
conversational
Instructions to use auro-rirum/LogSage-Qwen2.5-7B-QLoRA-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use auro-rirum/LogSage-Qwen2.5-7B-QLoRA-v0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-7B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "auro-rirum/LogSage-Qwen2.5-7B-QLoRA-v0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88f4102c7c68ea6a75292bfea71047cdd5766b6ff82e49ec395e0ce0cf13ff3a
- Size of remote file:
- 80.8 MB
- SHA256:
- 9a391717b3e8ce061d35ef66946ff7e9b2e93cb0e92d831eaba239cfaed3fe3d
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