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Model: cyumizou/qwen3-4b-structured-output-merged-stage-a
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- u-10bei/structured_data_with_cot_dataset_512_v2
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- structured-output
+- merged-weights
+- sft
+- qlora
+---
+
+qwen3-4b-structured-output-merged-stage-a
+
+This repository provides a **merged (fully materialized) model** derived from
+**Qwen/Qwen3-4B-Instruct-2507**. The weights were obtained by training a **LoRA adapter**
+and then **merging the adapter into the base model weights** (merge-and-unload).
+
+✅ **You can load this model directly with `AutoModelForCausalLM.from_pretrained()`**
+❌ This is **NOT** an adapter-only repository.
+
+## What this model is for (StageA)
+
+This model corresponds to **StageA** in a two-stage training procedure.
+
+**StageA goal:** stabilize *output mode* for structured generation:
+- reduce non-structured preambles (e.g., "Here/Sure")
+- reduce code-fences (```json / ```xml / ```yaml)
+- output only the required structured format reliably
+
+This merged model is intended to be used as a stable starting point for StageB
+(TOML failure-pattern mitigation) without drifting back to chatty preambles.
+
+## Training Objective
+
+Improve **structured output reliability** (JSON / YAML / XML / TOML / CSV),
+especially eliminating non-structured preambles that break parsers.
+
+## Training Configuration (StageA)
+
+- Base model: Qwen/Qwen3-4B-Instruct-2507
+- Method: QLoRA (4-bit) with LoRA adapter, then merged into base weights
+- Max sequence length: 1024
+- Training length: 1 epoch(s) (or step-limited, if configured)
+- Learning rate: 2e-05
+- LoRA: r=8, alpha=16
+
+Note: In StageA, loss is applied to the full assistant output to suppress preambles
+(if you used full-loss). If you used output-only loss, replace this sentence accordingly.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "your_id/your-repo"  # this repo
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,   # or float16 depending on your environment
+    device_map="auto",
+)
+```
+## Compliance / Notes
+
+This model is derived only from the organizer-approved base model
+(Qwen/Qwen3-4B-Instruct-2507) and uses no architecture changes.
+
+The merge operation is used only to integrate post-training results
+(SFT/LoRA) under the same architecture.
+
+## Sources & Terms (IMPORTANT)
+
+Training data: u-10bei/structured_data_with_cot_dataset_512_v2
+
+Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
+Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.