55 lines
1.3 KiB
Markdown
55 lines
1.3 KiB
Markdown
---
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- takami2022/structured_data_merged_v2v5_0222
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language:
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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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- merged
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- sft
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- structured-output
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---
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# qwen3-4b-sft-merged-v2v5ver1
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This repository provides a **merged 16-bit model** produced by fine-tuning
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**Qwen/Qwen3-4B-Instruct-2507** with QLoRA (4-bit, Unsloth) and then merging the LoRA adapter
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into the base model weights.
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This model is **fully self-contained** — no adapter loading required.
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Intended as the base model for subsequent DPO training.
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## Training Configuration
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- Base model: Qwen/Qwen3-4B-Instruct-2507
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- Dataset: takami2022/structured_data_merged_v2v5_0222
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- Method: QLoRA (4-bit) → merged to 16-bit
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- Max sequence length: 1024
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- Epochs: 3
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- Learning rate: 1e-06
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- LoRA: r=64, alpha=128
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- CoT mask: enabled
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "takami2022/qwen3-4b-sft-merged-v2v5ver1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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```
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## Notes
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- This merged model is the output of SFT and serves as the starting point for DPO.
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