vault backup: 2024-09-10 14:43:37

Affected files:
Unsloth.md

Unsloth.md

@@ -592,6 +592,76 @@ Adjusting the `LoraConfig` parameters allows you to balance model performance an
 - **Description**: Used for down-projection, typically reducing the dimensionality of the input.
 - **Impact**: Compresses the input to a lower-dimensional space, useful for reducing computational complexity and controlling the model size.
 
+-----
+# 🏆Reward Modelling - DPO & ORPO
+
+To use DPO or ORPO with Unsloth, follow the steps below:
+
+DPO (Direct Preference Optimization), ORPO (Odds Ratio Preference Optimization), PPO, Reward Modelling all work with Unsloth.
+
+We have Google Colab notebooks for reproducing ORPO and DPO Zephyr:
+
+- [ORPO notebook](https://colab.research.google.com/drive/11t4njE3c4Lxl-07OD8lJSMKkfyJml3Tn?usp=sharing)
+- [DPO Zephyr notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)
+
+We're also in 🤗Hugging Face's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth).
+
+## DPO Code
+
+```
+from unsloth import FastLanguageModel, PatchDPOTrainer
+from unsloth import is_bfloat16_supported
+PatchDPOTrainer()
+import torch
+from transformers import TrainingArguments
+from trl import DPOTrainer
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/zephyr-sft-bnb-4bit",
+    max_seq_length = max_seq_length,
+    dtype = None,
+    load_in_4bit = True,
+)
+
+# Do model patching and add fast LoRA weights
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 64,
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 64,
+    lora_dropout = 0, # Supports any, but = 0 is optimized
+    bias = "none",    # Supports any, but = "none" is optimized
+    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+    random_state = 3407,
+    max_seq_length = max_seq_length,
+)
+
+dpo_trainer = DPOTrainer(
+    model = model,
+    ref_model = None,
+    args = TrainingArguments(
+        per_device_train_batch_size = 4,
+        gradient_accumulation_steps = 8,
+        warmup_ratio = 0.1,
+        num_train_epochs = 3,
+        fp16 = not is_bfloat16_supported(),
+        bf16 = is_bfloat16_supported(),
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        seed = 42,
+        output_dir = "outputs",
+    ),
+    beta = 0.1,
+    train_dataset = YOUR_DATASET_HERE,
+    # eval_dataset = YOUR_DATASET_HERE,
+    tokenizer = tokenizer,
+    max_length = 1024,
+    max_prompt_length = 512,
+)
+dpo_trainer.train()
+```
 ----------
 # ⚠️Errors