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Model: motobrew/qwen-dpo-v3
Source: Original Platform

README.md

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+---
+base_model: motobrew/qwen3-adv-comp-v34
+datasets:
+- motobrew/alf-dpo-from-top-alf93-v0
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- dpo
+- unsloth
+- qwen
+- alignment
+---
+
+# qwen-dpo-v3
+
+This model is a fine-tuned version of **motobrew/qwen3-adv-comp-v34** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
+
+## 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**: motobrew/qwen3-adv-comp-v34
+- **Method**: DPO (Direct Preference Optimization)
+- **Epochs**: 1
+- **Learning rate**: 2e-06
+- **Beta**: 0.02
+- **Max sequence length**: 1024
+
+## Usage
+You can use this model directly with `transformers`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "motobrew/qwen-dpo-v3"
+
+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]))
+
+```
+
+## Sources & License (IMPORTANT)
+
+* **Training Data**: [motobrew/alf-dpo-from-top-alf93-v0]
+* **License**: MIT License. (As per dataset terms).
+* **Compliance**: Users must follow the original base model's license terms.