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Model: takami2022/qwen3-4b-sft-merged-v2v5ver1
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- takami2022/structured_data_merged_v2v5_0222
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- merged
+- sft
+- structured-output
+---
+
+# qwen3-4b-sft-merged-v2v5ver1
+
+This repository provides a **merged 16-bit model** produced by fine-tuning
+**Qwen/Qwen3-4B-Instruct-2507** with QLoRA (4-bit, Unsloth) and then merging the LoRA adapter
+into the base model weights.
+
+This model is **fully self-contained** — no adapter loading required.
+Intended as the base model for subsequent DPO training.
+
+## Training Configuration
+
+- Base model: Qwen/Qwen3-4B-Instruct-2507
+- Dataset: takami2022/structured_data_merged_v2v5_0222
+- Method: QLoRA (4-bit) → merged to 16-bit
+- Max sequence length: 1024
+- Epochs: 3
+- Learning rate: 1e-06
+- LoRA: r=64, alpha=128
+- CoT mask: enabled
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "takami2022/qwen3-4b-sft-merged-v2v5ver1"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+```
+
+## Notes
+
+- This merged model is the output of SFT and serves as the starting point for DPO.