Upload LoRA adapter (README written by author)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+base_model: unsloth/Qwen3-4B-Instruct-2507
+datasets:
+- u-10bei/structured_data_with_cot_dataset_512_v2
+language:
+- en
+- ja
+library_name: peft
+license: other
+pipeline_tag: text-generation
+tags:
+- unsloth
+- qlora
+- lora
+- peft
+- qwen
+- structured-output
+- format-conversion
+- information-extraction
+- chain-of-thought
+- cot-masking
+- assistant-only-loss
+- structeval-t
+---
+# lora_structeval_t_qwen3_4b_0118
+
+This repository provides a **LoRA adapter** fine-tuned from **unsloth/Qwen3-4B-Instruct-2507** using **Unsloth (QLoRA, 4-bit base)**.
+
+* **Contents:** LoRA adapter weights (PEFT) + tokenizer files (if present)
+* **Does not include:** Base model weights, training dataset files
+
+---
+
+## Training Objective
+
+This adapter was trained for **structured output quality** (format conversion / structured serialization) while avoiding learning verbose chain-of-thought.
+
+**Loss design**
+- The model sees the full conversation context (system + user + assistant).
+- **Loss is applied only to the final assistant turn** ("assistant-only loss").
+- Additionally, when an Output marker is present, loss is applied only to:
+  - `OUTPUT_LEARN_MODE="after_marker"`
+  - Markers searched: `Output:, OUTPUT:, Final:, Answer:, Result:, Response:`
+  - With `MASK_COT=True` this typically means learning **the content after `Output:`** (suppressing CoT-style "Approach:" text).
+
+This setup is intended to improve *final answer correctness and formatting* without encouraging the model to emit chain-of-thought.
+
+---
+
+## Training Configuration (Key)
+
+* **Run stamp (UTC):** `2026-01-18_062458Z`
+* **Base model:** `unsloth/Qwen3-4B-Instruct-2507`
+* **Dataset:** `u-10bei/structured_data_with_cot_dataset_512_v2`
+* **Method:** QLoRA (4-bit base) + LoRA adapters (PEFT)
+* **Max sequence length:** `512`
+* **Seed:** `3407`
+* **Train/Val split:** val_ratio=0.05
+
+### Hyperparameters
+
+* Epochs: `2`
+* LR: `0.0001`
+* Warmup ratio: `0.1`
+* Weight decay: `0.05`
+* Per-device train batch: `2`
+* Gradient accumulation: `8` (effective batch ≈ `16`)
+* LR scheduler: cosine
+* Precision: fp16 (T4-friendly)
+
+### LoRA
+
+* r: `64`
+* alpha: `128`
+* dropout: `0.0`
+* target_modules: `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
+
+---
+
+## Prompt / Output Style (Dataset-aligned)
+
+The training dataset uses chat messages and often includes a short reasoning header followed by a final structured output.
+With the default masking setup, the adapter is optimized primarily for the final structured segment.
+
+Typical assistant response shape:
+
+* `Approach:` (may be present, but often masked from loss)
+* `Output:` (structured data begins here; primary training target)
+
+You can encourage concise responses by explicitly requesting: "Return ONLY the final structured output."
+
+---
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+base_id = "unsloth/Qwen3-4B-Instruct-2507"
+adapter_id = "daichira/lora_structeval_t_qwen3_4b_0118"
+
+tokenizer = AutoTokenizer.from_pretrained(base_id, use_fast=True)
+
+model = AutoModelForCausalLM.from_pretrained(
+    base_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(model, adapter_id)
+model.eval()
+
+# Example: run generation with your preferred chat template usage.
+````
+
+---
+
+## Limitations / Notes
+
+* This is a **LoRA adapter**, not a standalone model. You must load `unsloth/Qwen3-4B-Instruct-2507` separately.
+* Format correctness depends on your decoding settings and prompt discipline. For strict tasks, consider:
+
+  * `temperature=0` (or low), `top_p=1.0`
+  * Post-validation (JSON/YAML/TOML/XML parsers) where applicable
+* The adapter is specialized for **structured serialization/format conversion**; it may not improve general chat ability.
+
+---
+
+## Sources & Terms (IMPORTANT)
+
+- **Training dataset:** `u-10bei/structured_data_with_cot_dataset_512_v2` (referenced on Hugging Face Hub)
+- This repository contains **LoRA adapter weights only** and does **not** redistribute the training dataset.
+- You are responsible for complying with:
+  - The **dataset license/terms** as stated in the dataset repository.
+  - The **base model license/terms** for `unsloth/Qwen3-4B-Instruct-2507` (these apply to derivatives/adapters as well).
+
+---
+
+## License
+
+* **Adapter repo license field:** `other` (model card metadata)
+* **Important:** Base model terms for `unsloth/Qwen3-4B-Instruct-2507` apply. Dataset terms for `u-10bei/structured_data_with_cot_dataset_512_v2` apply.

adapter_config.json

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+  "auto_mapping": {
+    "base_model_class": "Qwen3ForCausalLM",
+    "parent_library": "transformers.models.qwen3.modeling_qwen3",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit",
+  "bias": "none",
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+  "ensure_weight_tying": false,
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+  "loftq_config": {},
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+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
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+    "q_proj",
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+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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