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azuki-digital/qwen3-4b-struct-lora-v11-merged
qwen3-4b-struct-lora-v11-merged is a text generation model from azuki-digital. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
This repository provides a LoRA-merged base model derived from:
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From the Hugging Face model README
This repository provides a LoRA-merged base model derived from:
azuki-digital/qwen3-4b-struct-lora-v4-merged
This is NOT a LoRA adapter.
This is a fully merged, standalone model.
This model is a structured-output specialized base model.
It was created by:
This allows future LoRA training to start from a better structured-output prior.
Typical workflow:
Qwen3-4B-Instruct
↓ LoRA (structured output training)
↓ merge
qwen3-4b-struct-lora-v4-merged
↓ new LoRA training (v11)
This model significantly stabilizes later SFT and improves convergence.
| Item | Value |
|---|---|
| Base model | azuki-digital/qwen3-4b-struct-lora-v4-merged |
| Method | LoRA SFT (no quantization, bf16) |
| Max sequence length | 4096 |
| Epochs | 2 |
| Learning rate | 1e-5 |
| Warmup ratio | 0.05 |
| Weight decay | 0.05 |
| LoRA r | 32 |
| LoRA alpha | 64 |
| LoRA dropout | 0.05 |
| Target modules | q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj |
| Mask CoT | Yes (after_marker) |
| Dataset | daichira/structured-3k-mix-sft |
This is a fully merged standalone model. No LoRA adapter is required.
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "azuki-digital/qwen3-4b-struct-lora-v11-merged"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
This model inherits the license of:
Users must comply with the original base model terms.