daichira/test-lora-repo

lorastructevaltqwen34b_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
• —targetmodules: `qproj, kproj, vproj, oproj, gateproj, upproj, downproj`

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

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.