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Upload LoRA adapter (README written by author)

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  1. README.md +9 -9
  2. adapter_config.json +4 -4
  3. adapter_model.safetensors +1 -1
README.md CHANGED
@@ -1,7 +1,7 @@
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  ---
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- base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
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  datasets:
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- - daichira/structured-5k-mix-sft
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  language:
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  - en
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  license: apache-2.0
@@ -13,10 +13,10 @@ tags:
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  - structured-output
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  ---
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- Qwen3-4b-Structured-with-QLoRA
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  This repository provides a **LoRA adapter** fine-tuned from
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- **unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit** using **QLoRA (4-bit, Unsloth)**.
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  This repository contains **LoRA adapter weights only**.
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  The base model must be loaded separately.
@@ -31,10 +31,10 @@ while intermediate reasoning (Chain-of-Thought) is masked.
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  ## Training Configuration
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- - Base model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
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  - Method: QLoRA (4-bit)
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- - Max sequence length: 1024
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- - Epochs: 1
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  - Learning rate: 1e-04
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  - LoRA: r=64, alpha=128
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@@ -45,7 +45,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
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  from peft import PeftModel
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  import torch
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- base = "unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit"
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  adapter = "your_id/your-repo"
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  tokenizer = AutoTokenizer.from_pretrained(base)
@@ -59,7 +59,7 @@ model = PeftModel.from_pretrained(model, adapter)
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  ## Sources & Terms (IMPORTANT)
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- Training data: daichira/structured-5k-mix-sft
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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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  ---
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+ base_model: Qwen/Qwen3-4B-Instruct-2507
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  datasets:
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+ - u-10bei/structured_data_with_cot_dataset_512_v2
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  language:
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  - en
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  license: apache-2.0
 
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  - structured-output
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  ---
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+ Qwen3-4b-structured-SFT
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  This repository provides a **LoRA adapter** fine-tuned from
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+ **Qwen/Qwen3-4B-Instruct-2507** using **QLoRA (4-bit, Unsloth)**.
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  This repository contains **LoRA adapter weights only**.
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  The base model must be loaded separately.
 
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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: 512
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+ - Epochs: 2
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  - Learning rate: 1e-04
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  - LoRA: r=64, alpha=128
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  from peft import PeftModel
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  import torch
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+ base = "Qwen/Qwen3-4B-Instruct-2507"
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  adapter = "your_id/your-repo"
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  tokenizer = AutoTokenizer.from_pretrained(base)
 
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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.
adapter_config.json CHANGED
@@ -33,13 +33,13 @@
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  "rank_pattern": {},
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  "revision": null,
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  "target_modules": [
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- "o_proj",
 
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  "k_proj",
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  "q_proj",
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  "gate_proj",
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- "v_proj",
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- "up_proj",
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- "down_proj"
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  ],
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  "target_parameters": null,
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  "task_type": "CAUSAL_LM",
 
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  "rank_pattern": {},
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  "revision": null,
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  "target_modules": [
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+ "v_proj",
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+ "up_proj",
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  "k_proj",
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  "q_proj",
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  "gate_proj",
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+ "down_proj",
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+ "o_proj"
 
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  ],
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  "target_parameters": null,
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  "task_type": "CAUSAL_LM",
adapter_model.safetensors CHANGED
@@ -1,3 +1,3 @@
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