Tandogan
/

MNLP_M2_dpo_model

@@ -1,199 +1,79 @@
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 ---
+license: apache-2.0
+tags:
+  - dpo
+  - unsloth
+  - trl
+  - qwen
+  - instruction-tuning
+  - preference-modeling
+  - mnlp
+datasets:
+  - Tandogan/sft_dataset_final_train
+  - Tandogan/MNLP_M2_dpo_dataset
+base_model: Qwen/Qwen3-0.6B-Base
+inference: false
 ---
+# MNLP M2 DPO Model — Qwen3-0.6B Fine-Tuned with Direct Preference Optimization
+This repository contains a Direct Preference Optimization (DPO) model built on top of a supervised fine-tuned version of [`Qwen/Qwen3-0.6B-Base`](https://huggingface.co/Qwen/Qwen3-0.6B-Base), as part of the MNLP M2 project. The model is fine-tuned using a high-quality preference dataset to better align responses with human preferences.
+## Model Description
+- **Base Model**: [`Qwen/Qwen3-0.6B-Base`](https://huggingface.co/Qwen/Qwen3-0.6B-Base)
+- **SFT Checkpoint**: [`Tandogan/MNLP_M2_SFT`](https://huggingface.co/Tandogan/MNLP_M2_SFT)
+- **DPO Dataset**: [`Tandogan/MNLP_M2_dpo_dataset`](https://huggingface.co/datasets/Tandogan/MNLP_M2_dpo_dataset)
+- **Libraries**: [Unsloth](https://github.com/unslothai/unsloth), [TRL](https://github.com/huggingface/trl)
+## Training Procedure
+### Supervised Fine-Tuning (SFT)
+- **Dataset**: [`Tandogan/sft_dataset_final_train`](https://huggingface.co/datasets/Tandogan/sft_dataset_final_train)
+  (Alpaca-style prompt–completion pairs)
+- **Max sequence length**: 2048
+- **Epochs**: 4
+- **Optimizer**: AdamW (learning rate = `3e-5`, weight decay = `0`)
+- **Precision**: bf16
+- **Batch size**: 2 (gradient accumulation = 4)
+- **Scheduler**: Linear with 1% warmup
+- **Eval & Checkpointing**: Every epoch
+### Direct Preference Optimization (DPO)
+Two DPO fine-tuning experiments were run:
+#### 1. From Base Model (`Qwen3-0.6B-Base`)
+#### 2. From SFT Model ([`Tandogan/MNLP_M2_SFT`](https://huggingface.co/Tandogan/MNLP_M2_SFT))
+- **Dataset**: [`Tandogan/MNLP_M2_dpo_dataset`](https://huggingface.co/datasets/Tandogan/MNLP_M2_dpo_dataset)
+- **Max sequence length**: 2048 (prompt + completions truncated to 1024 each)
+- **Epochs**: 4
+- **Optimizer**: AdamW (learning rate = `2e-6`, weight decay = `0`)
+- **Precision**: bf16
+- **Batch size**: 2 (gradient accumulation = 4)
+- **Scheduler**: Cosine with 1% warmup
+- **DPO Beta**: 0.1
+- **Eval & Checkpointing**: Every epoch
+- **Monitoring**: Weights & Biases (WandB)
+- **Best Epoch Selection**: Based on validation loss
+## 📊 Intended Use
+This model is intended for research and experimentation with preference-based alignment and reward modeling. It is **not** production-ready and may produce hallucinated, biased, or unsafe outputs. Please evaluate carefully for downstream tasks.
+## 💾 How to Use
+You can use the model with the `transformers` and `trl` libraries for inference or evaluation:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained("Tandogan/MNLP_M2_dpo_model").to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("Tandogan/MNLP_M2_dpo_model")
+prompt = "Explain recursion in simple terms."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))