{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mxl/anaconda3/envs/my_llm/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, set_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading checkpoint shards: 100%|██████████| 4/4 [00:04<00:00,  1.17s/it]\n",
      "WARNING:root:A <class 'transformers.models.llama.modeling_llama.LlamaForCausalLM'> model is loaded from '/data/public/model/Meta-Llama-3-8B-Instruct/', and no v_head weight is found. This IS expected if you are not resuming PPO training.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForCausalLMWithValueHead.from_pretrained(\n",
    "        '/data/public/model/Meta-Llama-3-8B-Instruct/',\n",
    "        load_in_8bit=True,\n",
    "        # peft_config=lora_config,\n",
    "        device_map='auto',\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LlamaDecoderLayer(\n",
       "  (self_attn): LlamaSdpaAttention(\n",
       "    (q_proj): Linear8bitLt(in_features=4096, out_features=4096, bias=False)\n",
       "    (k_proj): Linear8bitLt(in_features=4096, out_features=1024, bias=False)\n",
       "    (v_proj): Linear8bitLt(in_features=4096, out_features=1024, bias=False)\n",
       "    (o_proj): Linear8bitLt(in_features=4096, out_features=4096, bias=False)\n",
       "    (rotary_emb): LlamaRotaryEmbedding()\n",
       "  )\n",
       "  (mlp): LlamaMLP(\n",
       "    (gate_proj): Linear8bitLt(in_features=4096, out_features=14336, bias=False)\n",
       "    (up_proj): Linear8bitLt(in_features=4096, out_features=14336, bias=False)\n",
       "    (down_proj): Linear8bitLt(in_features=14336, out_features=4096, bias=False)\n",
       "    (act_fn): SiLU()\n",
       "  )\n",
       "  (input_layernorm): LlamaRMSNorm()\n",
       "  (post_attention_layernorm): LlamaRMSNorm()\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.pretrained_model.model.layers[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data3/mxl/anaconda3/envs/my_llm/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating train split: 160800 examples [00:00, 246619.83 examples/s]\n",
      "Generating test split: 8552 examples [00:00, 191860.63 examples/s]\n"
     ]
    }
   ],
   "source": [
    "ds = load_dataset('./datasets/hh-rlhf/', split='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sfasdf [1, 2, 3]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"sfasdf {}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1_2_3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'_'.join(map(str, [1, 2, 3]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "RiC",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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