Instructions to use JerrySiRi/Qwen3-30B-A3B-lora-tulu-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JerrySiRi/Qwen3-30B-A3B-lora-tulu-sft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B") model = PeftModel.from_pretrained(base_model, "JerrySiRi/Qwen3-30B-A3B-lora-tulu-sft") - Notebooks
- Google Colab
- Kaggle
qwen3-30B-A3B-32-64-5k-no-gate
PEFT LoRA adapter fine-tuned from Qwen/Qwen3-30B-A3B on rl-research/dr-tulu-sft-data.
Training Details
- LoRA rank: 32, alpha: 64
- Target modules: q_proj, v_proj, k_proj, up_proj, down_proj, gate_proj, o_proj
- Trained with LlamaFactory on 2x GPUs, 3 epochs, cosine LR schedule.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model_id = "Qwen/Qwen3-30B-A3B"
base = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = PeftModel.from_pretrained(base, "qwen3-30B-A3B-32-64-5k-no-gate")
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