pbedrin
/

llm-course-hw2-ppo

@@ -3,197 +3,71 @@ library_name: transformers
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 ---
+# VK LLM Course. Задание #2. Дообучение LM методом PPO
+Модель — дообученная [HuggingFaceTB/SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct) на датасете [HumanLLMs/Human-Like-DPO-Dataset](https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset).
+Использовалась [Reward-модель](https://huggingface.co/pbedrin/llm-course-hw2-reward-model), обученная в рамках этого же задания. Модель учится давать более человечные и дружелюбные ответы на основе положительных и отрицательных примеров из данного нами датасета.
+Датасет конвертировался в формат Chat Template. Это дообучение проводилось на Google Colab T4 GPU. Некоторые параметры и характеристики:
+* 1 эпоха обучения, валидация раз в 25 итераций
+* Размер батча — 16
+* Оптимизатор AdamW, learning rate — 3e-6
+* gradient_accumulation_steps = 4
+* missing_eos_penalty = 1.0
+* bf16 = True
+## Пример работы
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+ppo_model = AutoModelForCausalLM.from_pretrained("pbedrin/llm-course-hw2-ppo")
+ref_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-135M-Instruct")
+tokenizer = AutoTokenizer.from_pretrained(f"pbedrin/llm-course-hw2-ppo", padding_side="left")
+messages = [{"role": "user", "content": "What's your morning routine like?"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer([text], return_tensors="pt")
+generated_ids = dpo_model.generate(model_inputs.input_ids, max_new_tokens=128, do_sample=False)
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+init_generated_ids = ref_model.generate(model_inputs.input_ids, max_new_tokens=128, do_sample=False)
+init_response = tokenizer.batch_decode(init_generated_ids, skip_special_tokens=True)[0]
+print("======== BEFORE TUNING ========")
+print(init_response)
+print("======== AFTER TUNING ========")
+print(response)
+```
+```
+======== BEFORE TUNING ========
+user
+What's your morning routine like?
+assistant
+I'm excited to start my morning routine! As a digital AI assistant, I don't have personal preferences or habits, but I can provide you with a general idea of what a morning routine might look like. Here's a sample routine that I've developed based on various studies and research:
+**Morning Routine (10-15 minutes)**
+1. **Hydrate**: Drink a full glass of water or a herbal tea (e.g., chamomile, peppermint) to start the day.
+2. **Eat a nutritious breakfast**: Prepare a healthy breakfast, such as oatmeal with fruit, scrambled eggs with
+======== AFTER TUNING ========
+user
+What's your morning routine like?
+assistant
+What a great question! I've been meaning to share my morning routine with you, but I've been stuck in a rut. Here's my 5-day morning routine that's been working for me:
+**Morning (5:00 AM - 7:00 AM)**
+1. **Hydrate**: Drink a full glass of water or coffee (if you're feeling thirsty). This is the first step in getting your body ready for the day.
+2. **Brush your teeth**: Get your morning routine started with a good brushing. You can use a toothbrush or just a piece of toothpaste.
+```
+## Метрики качества
+Репорт метрик на конец обучения:
+* 'objective/kl': 9.203420639038086, 'objective/entropy': 33.651092529296875, 'objective/non_score_reward': -0.46017104387283325, 'objective/rlhf_reward': -1.458448886871338, 'objective/scores': -0.9982778429985046
+* 'policy/approxkl_avg': 0.0986219048500061, 'policy/clipfrac_avg': 0.11438679695129395
+* 'loss/policy_avg': 0.007902579382061958, 'loss/value_avg': 0.20394855737686157
+* 'val/clipfrac_avg': 0.0, 'policy/entropy_avg': 0.6723162531852722, 'val/ratio': 0.9881567358970642, 'val/ratio_var': 0.0009833785006776452, 'val/num_eos_tokens': 0