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Upload LoRA adapter (+ optional tokenizer files)

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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: Qwen/Qwen3-4B-Instruct-2507
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+ datasets:
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+ - u-10bei/dpo-dataset-qwen-cot
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+ language:
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+ - ja
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+ - en
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+ license: apache-2.0
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ tags:
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+ - peft
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+ - lora
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+ - dpo
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+ - sft
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+ - unsloth
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+ - structured-output
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+ - conversational
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+ - qwen
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+ ---
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+
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+ # LoRA: StructEval-T (Qwen3-4B-Instruct-2507) SFT+DPO (sftdpo_v1)
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+
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+ This repository contains a **LoRA adapter (PEFT)** for **StructEval-T style structured output tasks**.
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+ It is trained as **SFT → DPO** on top of **Qwen/Qwen3-4B-Instruct-2507**.
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+
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+ > ⚠️ This repo is **NOT** a full merged model.
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+ > Load the base model first, then apply this adapter.
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+
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+ ---
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+
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+ ## Model Details
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+
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+ - **Adapter type:** LoRA (PEFT)
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+ - **Base model:** Qwen/Qwen3-4B-Instruct-2507 (license: Apache-2.0)
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+ - **What this adapter aims to improve:**
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+ - Better compliance to *structured output* constraints (JSON / schema / format-sensitive tasks)
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+ - Better preference alignment via DPO on a preference dataset
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+
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+ ---
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+
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+ ## Training Data
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+
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+ - **DPO preference dataset (main):** `u-10bei/dpo-dataset-qwen-cot`
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+ - **SFT starting point (adapter):** `RinnRinnmini/lora_structeval_t_qwen3_4b_sft_v1`
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+ (This adapter was used as initialization before DPO.)
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+
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+ ---
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+
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+ ## Training Procedure
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+
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+ ### Stage 1) SFT (initialization)
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+ - Start from `Qwen/Qwen3-4B-Instruct-2507`
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+ - Apply SFT LoRA adapter `RinnRinnmini/lora_structeval_t_qwen3_4b_sft_v1` as the initialization point
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+
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+ ### Stage 2) DPO (this adapter)
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+ - **Trainer:** TRL `DPOTrainer`
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+ - **Framework:** Transformers + PEFT (+ Unsloth where applicable)
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+ - **Key hyperparameters**
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+ - epochs: 1
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+ - learning rate: 1e-07
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+ - beta: 0.1
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+ - max_length: 1024
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+ - max_prompt_length: 512
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+ - per_device_train_batch_size: 1
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+ - gradient_accumulation_steps: 8
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+ - optimizer: OptimizerNames.ADAMW_8BIT
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+
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+ ---
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+
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+ ## How to Use
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+
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+ ### Transformers + PEFT (recommended)
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ from peft import PeftModel
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+ import torch
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+
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+ BASE_ID = "Qwen/Qwen3-4B-Instruct-2507"
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+ # BASE_ID = "unsloth/Qwen3-4B-Instruct-2507"
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+
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+ ADAPTER_ID = "RinnRinnmini/qwen3-4b-structeval-sftdpo_v3-adapter" # this repo (LoRA adapter)
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+
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+ tok = AutoTokenizer.from_pretrained(BASE_ID, trust_remote_code=True, use_fast=True)
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+ if tok.pad_token is None:
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+ tok.pad_token = tok.eos_token
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+
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+ base = AutoModelForCausalLM.from_pretrained(
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+ BASE_ID,
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+ device_map="auto",
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+ torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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+ trust_remote_code=True,
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+ )
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+
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+ model = PeftModel.from_pretrained(base, ADAPTER_ID)
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+ model.eval()
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+
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+ messages = [
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+ {"role": "user", "content": "Return a JSON with keys a,b,c and integer values."}
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+ ]
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+ prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+
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+ inputs = tok(prompt, return_tensors="pt").to(model.device)
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+ with torch.no_grad():
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+ out = model.generate(
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+ **inputs,
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+ max_new_tokens=256,
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+ do_sample=False,
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+ pad_token_id=tok.eos_token_id,
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+ )
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+
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+ print(tok.decode(out[0], skip_special_tokens=True))
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+ ```
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+
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+ ---
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+
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+ ## Limitations
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+ - This is an **adapter-only** repository. You must load the base model separately.
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+ "loftq_config": {},
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+ "task_type": "CAUSAL_LM",
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+ "use_dora": false,
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+ "use_rslora": false
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