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Model: KazumaTsuboi/dpo-qwen-cot-merged
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- u-10bei/dpo-dataset-qwen-cot
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- dpo
+- unsloth
+- qwen
+- alignment
+---
+
+# ＜qwen3-4b-dpo-qwen-cot-merged＞
+
+This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
+
+This repository contains the **full-merged 16-bit weights**. No adapter loading is required.
+
+## Training Objective
+This model has been optimized using DPO to align its responses with preferred outputs, focusing on improving reasoning (Chain-of-Thought) and structured response quality based on the provided preference dataset.
+
+## Training Configuration
+- **Base model**: KazumaTsuboi/LLM_Competition (derived from Qwen/Qwen3-4B-Instruct-2507)
+- **Method**: DPO (Direct Preference Optimization)
+- **Epochs**: 1
+- **Learning rate**: 1e-06
+- **Beta**: 0.05
+- **Max sequence length**: 1024
+- **LoRA Config**: r=16, alpha=16 (merged into base)
+
+## Usage
+Since this is a merged model, you can use it directly with `transformers`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "your_id/your-repo-name"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Test inference
+prompt = "Your question here"
+inputs = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0]))