66 lines
1.9 KiB
Markdown
66 lines
1.9 KiB
Markdown
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-4B-Instruct-2507
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- TakaTaka3/qwen3-4b-lora-adapter_V4
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pipeline_tag: text-generation
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datasets:
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- u-10bei/structured_data_with_cot_dataset_512_v2
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---
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# TakaTaka3/Qwen3-4B-Instruct-2507-sft-merged_V2
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This repository provides a **Merged model** fine-tuned from
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**Qwen/Qwen3-4B-Instruct-2507** using **QLoRA (4-bit, Unsloth)**.
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This repository contains the merged model that merged **base model (Qwen/Qwen3-4B-Instruct-2507)** and **LoRA adapter weights (TakaTaka3/qwen3-4b-lora-adapter_V4)**
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.
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## Training Objective
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This adapter is trained to improve **structured output accuracy**
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(JSON / YAML / XML / TOML / CSV).
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Loss is applied only to the final assistant output,
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while intermediate reasoning (Chain-of-Thought) is masked.
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## Training Configuration
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- Base model: Qwen/Qwen3-4B-Instruct-2507
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- Method: QLoRA (4-bit)
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- Max sequence length: 2048
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- Epochs: 1
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- Learning rate: 2e-06
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- LoRA: r=64, alpha=128
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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 = "TakaTaka3/Qwen3-4B-Instruct-2507-sft-merged_V2"
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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.float16,
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device_map="auto"
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)
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# Test inference
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prompt = "Your question here"
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inputs = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0]))
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```
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## Sources & Terms (IMPORTANT)
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Training data: u-10bei/structured_data_with_cot_dataset_512_v2
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Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
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Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.
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