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Upload 4-bit-NF4 - Migration QLoRA DPO

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README.md ADDED
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+ ---
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+ language:
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+ - ja
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+ - ko
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+ - en
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+ license: apache-2.0
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+ base_model: Qwen/Qwen3.5-9B
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+ tags:
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+ - migration
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+ - mainframe
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+ - cobol
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+ - jcl
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+ - assembler
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+ - qlora
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+ - dpo
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+ - ofkms
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # OFKMS Migration Design - Qwen3.5-9B DPO (4-bit-NF4)
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+
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+ Mainframe migration design specialized model fine-tuned from Qwen3.5-9B.
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+
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+ ## Model Description
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+
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+ This model is fine-tuned for **COBOL/JCL/Assembler migration design** tasks,
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+ trained on TmaxSoft Japan's proprietary migration knowledge base.
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+
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+ - **Base Model**: Qwen3.5-9B
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+ - **Fine-tuning**: QLoRA (DPO)
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+ - **Training Data**: 1,288 SFT entries + 1,288 DPO pairs
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+ - **Languages**: Japanese (primary), Korean, English
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+ - **Variant**: 4-bit-NF4
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+
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+ ## Training Details
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+
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+ - **Method**: QLoRA (rank=64, alpha=128)
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+ - **Trainable params**: 174M / 8.4B (2.09%)
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+ - **Epochs**: 3
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+ - **Batch size**: 4 (gradient accumulation: 16, effective: 64)
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+ - **Learning rate**: 2e-5 (cosine schedule)
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+ - **Hardware**: NVIDIA A100 40GB
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+
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+ ## Supported Tasks
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+
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+ - COBOL source pattern analysis and conversion rules
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+ - JCL to OpenFrame JCL migration
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+ - Assembler to C/OFASM migration
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+ - Migration design document generation
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+ - Error pattern diagnosis (ABEND codes, JES messages)
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model = AutoModelForCausalLM.from_pretrained("jtmaxsoft/OFKMS-Migration-Qwen3.5-9B-DPO-4bit")
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+ tokenizer = AutoTokenizer.from_pretrained("jtmaxsoft/OFKMS-Migration-Qwen3.5-9B-DPO-4bit")
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+
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+ prompt = "COBOL PERFORM statement OpenFrame migration pattern"
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+ outputs = model.generate(**inputs, max_new_tokens=512)
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+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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+ ```
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+
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+ ## Organization
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+
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+ [TmaxSoft Japan](https://huggingface.co/jtmaxsoft)
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