diff --git "a/novel-translation/09_tune-lf-medium-py3.11.ipynb" "b/novel-translation/09_tune-lf-medium-py3.11.ipynb"
new file mode 100644--- /dev/null
+++ "b/novel-translation/09_tune-lf-medium-py3.11.ipynb"
@@ -0,0 +1,4311 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "eb33b19f-1206-41ee-84e2-e6258a12eef7",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.11.0rc1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "if 'dbutils' in locals():\n",
+    "    dbutils.library.restartPython()\n",
+    "\n",
+    "!python --version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "0ea8b46b-839b-445b-8043-ccdf4e920ace",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "6d394937-6c99-4a7c-9d32-7600a280032f",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "workding dir: /Workspace/Users/donghao.huang@mastercard.com/llm-finetuning\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "\n",
+    "workding_dir = str(Path.cwd().parent)\n",
+    "os.chdir(workding_dir)\n",
+    "sys.path.append(workding_dir)\n",
+    "print(\"workding dir:\", workding_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "8affcc96-edf6-4489-b656-afb475a038e6",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "need_to_setup_env = False\n",
+    "need_to_setup_env"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "72f9cf79-7b0d-4d9e-90a0-1fa5251b947f",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "if need_to_setup_env:\n",
+    "    %pip config set global.index-url https://artifacts.forge.mastercard.com/artifactory/api/pypi/python/simple\n",
+    "    %pip install tf-keras\n",
+    "    %pip install -q --upgrade accelerate einops xformers torchvision\n",
+    "    %pip install -r requirements.txt\n",
+    "    !cd ../LLaMA-Factory && pip install -e .[torch,bitsandbytes] && FLASH_ATTENTION_FORCE_BUILD=TRUE pip install --upgrade flash-attn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "c06c61fd-4c6f-4099-bd3b-46188ab835d7",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "workding dir: /Workspace/Users/donghao.huang@mastercard.com/llm-finetuning\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.chdir(workding_dir)\n",
+    "sys.path.append(workding_dir)\n",
+    "print(\"workding dir:\", workding_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "9f67ec60-2f24-411c-84eb-0dd664b44775",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading env vars from: /Workspace/Users/donghao.huang@mastercard.com/llm-finetuning/.env\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dotenv import find_dotenv, load_dotenv\n",
+    "\n",
+    "found_dotenv = find_dotenv(\".env\")\n",
+    "\n",
+    "if len(found_dotenv) == 0:\n",
+    "    found_dotenv = find_dotenv(\".env.example\")\n",
+    "print(f\"loading env vars from: {found_dotenv}\")\n",
+    "load_dotenv(found_dotenv, override=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "f1597656-8042-4878-9d3b-9ebfb8dd86dc",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "('unsloth/Qwen2-1.5B-Instruct',\n",
+       " True,\n",
+       " None,\n",
+       " None,\n",
+       " 2048,\n",
+       " 10,\n",
+       " None,\n",
+       " 'datasets/mac/mac.tsv',\n",
+       " 'results/mac-results_lf-r3.csv',\n",
+       " 'TRUE',\n",
+       " 'true')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "model_name = os.getenv(\"MODEL_NAME\")\n",
+    "token = os.getenv(\"HF_TOKEN\") or None\n",
+    "load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n",
+    "local_model = os.getenv(\"LOCAL_MODEL\")\n",
+    "hub_model = os.getenv(\"HUB_MODEL\")\n",
+    "num_train_epochs = int(os.getenv(\"NUM_TRAIN_EPOCHS\") or 0)\n",
+    "data_path = os.getenv(\"DATA_PATH\")\n",
+    "results_path = os.getenv(\"RESULTS_PATH\")\n",
+    "\n",
+    "max_seq_length = 2048  # Choose any! We auto support RoPE Scaling internally!\n",
+    "dtype = (\n",
+    "    None  # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
+    ")\n",
+    "\n",
+    "model_name, load_in_4bit, local_model, hub_model, max_seq_length, num_train_epochs, dtype, data_path, results_path, os.getenv(\"DISABLE_MLFLOW_INTEGRATION\"), os.getenv(\"WANDB_DISABLED\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "e3ab54ba-7b6d-4817-bf2e-c5d711508b58",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fri Jul  5 08:59:30 2024       \r\n+---------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 535.54.03              Driver Version: 535.54.03    CUDA Version: 12.2     |\r\n|-----------------------------------------+----------------------+----------------------+\r\n| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\r\n|                                         |                      |               MIG M. |\r\n|=========================================+======================+======================|\r\n|   0  Tesla T4                       Off | 00000001:00:00.0 Off |                    0 |\r\n| N/A   31C    P8               9W /  70W |      2MiB / 15360MiB |      0%      Default |\r\n|                                         |                      |                  N/A |\r\n+-----------------------------------------+----------------------+----------------------+\r\n                                                                                         \r\n+---------------------------------------------------------------------------------------+\r\n| Processes:                                                                            |\r\n|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\r\n|        ID   ID                                                             Usage      |\r\n|=======================================================================================|\r\n|  No running processes found                                                           |\r\n+---------------------------------------------------------------------------------------+\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "b2a43943-9324-4839-9a47-cfa72de2244b",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.11.0rc1\r\nName: flash-attn\nVersion: 2.5.9.post1\nSummary: Flash Attention: Fast and Memory-Efficient Exact Attention\nHome-page: https://github.com/Dao-AILab/flash-attention\nAuthor: Tri Dao\nAuthor-email: trid@cs.stanford.edu\nLicense: \nLocation: /local_disk0/.ephemeral_nfs/envs/pythonEnv-40f92d71-6c52-44a3-a1ef-62cdea633f68/lib/python3.11/site-packages\nRequires: einops, torch\nRequired-by: \nCPU times: user 10.5 ms, sys: 15.1 ms, total: 25.6 ms\nWall time: 4.37 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "!python --version\n",
+    "!pip show flash-attn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "df316e8f-0710-445e-b6f9-fd67ebfeac5a",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current Directory:\r\n/Workspace/Users/donghao.huang@mastercard.com/llm-finetuning/llama-factory\r\nconfig/llama3_8b_lora_sft.yaml:\r\n {\r\n  \"model_name_or_path\": \"gradientai/Llama-3-8B-Instruct-Gradient-1048k\",\r\n  \"stage\": \"sft\",\r\n  \"do_train\": true,\r\n  \"finetuning_type\": \"lora\",\r\n  \"lora_target\": \"all\",\r\n  \"quantization_bit\": 4,\r\n  \"loraplus_lr_ratio\": 16.0,\r\n  \"dataset\": \"alpaca_mac\",\r\n  \"template\": \"llama3\",\r\n  \"cutoff_len\": 1024,\r\n  \"max_samples\": 4528,\r\n  \"overwrite_cache\": true,\r\n  \"preprocessing_num_workers\": 16,\r\n  \"output_dir\": \"/Workspace/Users/donghao.huang@mastercard.com/lf-saves/llama3-8b/lora/sft/\",\r\n  \"logging_steps\": 10,\r\n  \"save_steps\": 560,\r\n  \"plot_loss\": true,\r\n  \"overwrite_output_dir\": true,\r\n  \"per_device_train_batch_size\": 1,\r\n  \"gradient_accumulation_steps\": 8,\r\n  \"learning_rate\": 0.0001,\r\n  \"num_train_epochs\": 6.0,\r\n  \"lr_scheduler_type\": \"cosine\",\r\n  \"warmup_ratio\": 0.1,\r\n  \"bf16\": true,\r\n  \"ddp_timeout\": 180000000,\r\n  \"val_size\": 0.01,\r\n  \"per_device_eval_batch_size\": 1,\r\n  \"eval_strategy\": \"steps\",\r\n  \"eval_steps\": 560,\r\n  \"report_to\": \"none\"\r\n}\r\n2024-07-05 08:59:40.790008: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n[2024-07-05 08:59:50,632] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)\r\n07/05/2024 08:59:58 - WARNING - llamafactory.hparams.parser - We recommend enable `upcast_layernorm` in quantized training.\r\n07/05/2024 08:59:58 - INFO - llamafactory.hparams.parser - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.bfloat16\r\n[INFO|tokenization_utils_base.py:2161] 2024-07-05 08:59:59,223 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--gradientai--Llama-3-8B-Instruct-Gradient-1048k/snapshots/8697fb25cb77c852311e03b4464b8467471d56a4/tokenizer.json\r\n[INFO|tokenization_utils_base.py:2161] 2024-07-05 08:59:59,223 >> loading file added_tokens.json from cache at None\r\n[INFO|tokenization_utils_base.py:2161] 2024-07-05 08:59:59,223 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--gradientai--Llama-3-8B-Instruct-Gradient-1048k/snapshots/8697fb25cb77c852311e03b4464b8467471d56a4/special_tokens_map.json\r\n[INFO|tokenization_utils_base.py:2161] 2024-07-05 08:59:59,223 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--gradientai--Llama-3-8B-Instruct-Gradient-1048k/snapshots/8697fb25cb77c852311e03b4464b8467471d56a4/tokenizer_config.json\r\n[WARNING|logging.py:313] 2024-07-05 08:59:59,517 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\r\n07/05/2024 08:59:59 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>\r\n07/05/2024 08:59:59 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>\r\n07/05/2024 08:59:59 - INFO - llamafactory.data.loader - Loading dataset alpaca_mac.json...\r\n\rConverting format of dataset (num_proc=16):   0%| | 0/4528 [00:00<?, ? examples/\rConverting format of dataset (num_proc=16): 100%|█| 4528/4528 [00:00<00:00, 4511\rConverting format of dataset (num_proc=16): 100%|█| 4528/4528 [00:00<00:00, 2532\r\n\rRunning tokenizer on dataset (num_proc=16):   0%| | 0/4528 [00:00<?, ? examples/\rRunning tokenizer on dataset (num_proc=16):   6%| | 283/4528 [00:00<00:08, 500.4\rRunning tokenizer on dataset (num_proc=16):  12%|▏| 566/4528 [00:00<00:04, 830.0\rRunning tokenizer on dataset (num_proc=16):  19%|▏| 849/4528 [00:00<00:03, 1104.\rRunning tokenizer on dataset (num_proc=16):  25%|▎| 1132/4528 [00:01<00:02, 1305\rRunning tokenizer on dataset (num_proc=16):  31%|▎| 1415/4528 [00:01<00:02, 1518\rRunning tokenizer on dataset (num_proc=16):  38%|▍| 1698/4528 [00:01<00:01, 1657\rRunning tokenizer on dataset (num_proc=16):  44%|▍| 1981/4528 [00:01<00:01, 1724\rRunning tokenizer on dataset (num_proc=16):  50%|▌| 2264/4528 [00:01<00:01, 1759\rRunning tokenizer on dataset (num_proc=16):  56%|▌| 2547/4528 [00:01<00:01, 1752\rRunning tokenizer on dataset (num_proc=16):  62%|▋| 2830/4528 [00:01<00:00, 1770\rRunning tokenizer on dataset (num_proc=16):  69%|▋| 3113/4528 [00:02<00:00, 1866\rRunning tokenizer on dataset (num_proc=16):  75%|▊| 3396/4528 [00:02<00:00, 1937\rRunning tokenizer on dataset (num_proc=16):  81%|▊| 3679/4528 [00:02<00:00, 1988\rRunning tokenizer on dataset (num_proc=16):  88%|▉| 3962/4528 [00:02<00:00, 2034\rRunning tokenizer on dataset (num_proc=16):  94%|▉| 4245/4528 [00:02<00:00, 2042\rRunning tokenizer on dataset (num_proc=16): 100%|█| 4528/4528 [00:02<00:00, 2095\rRunning tokenizer on dataset (num_proc=16): 100%|█| 4528/4528 [00:02<00:00, 1599\r\ninput_ids:\r\n[128000, 128006, 882, 128007, 271, 5618, 15025, 279, 2768, 8620, 1495, 1139, 6498, 323, 3493, 1193, 279, 25548, 2262, 11, 4400, 775, 627, 37087, 6271, 245, 100815, 121991, 110139, 113265, 108057, 1811, 128009, 128006, 78191, 128007, 271, 18433, 358, 574, 2682, 555, 264, 39935, 45586, 13, 128009]\r\ninputs:\r\n<|begin_of_text|><|start_header_id|>user<|end_header_id|>\r\n\r\nPlease translate the following Chinese text into English and provide only the translated content, nothing else.\r\n全仗着狐仙搭救。<|eot_id|><|start_header_id|>assistant<|end_header_id|>\r\n\r\nBecause I was protected by a fox fairy.<|eot_id|>\r\nlabel_ids:\r\n[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 18433, 358, 574, 2682, 555, 264, 39935, 45586, 13, 128009]\r\nlabels:\r\nBecause I was protected by a fox fairy.<|eot_id|>\r\n[INFO|configuration_utils.py:733] 2024-07-05 09:00:03,851 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--gradientai--Llama-3-8B-Instruct-Gradient-1048k/snapshots/8697fb25cb77c852311e03b4464b8467471d56a4/config.json\r\n[INFO|configuration_utils.py:800] 2024-07-05 09:00:03,852 >> Model config LlamaConfig {\r\n  \"_name_or_path\": \"gradientai/Llama-3-8B-Instruct-Gradient-1048k\",\r\n  \"architectures\": [\r\n    \"LlamaForCausalLM\"\r\n  ],\r\n  \"attention_bias\": false,\r\n  \"attention_dropout\": 0.0,\r\n  \"bos_token_id\": 128000,\r\n  \"eos_token_id\": 128001,\r\n  \"hidden_act\": \"silu\",\r\n  \"hidden_size\": 4096,\r\n  \"initializer_range\": 0.02,\r\n  \"intermediate_size\": 14336,\r\n  \"max_position_embeddings\": 1048576,\r\n  \"mlp_bias\": false,\r\n  \"model_type\": \"llama\",\r\n  \"num_attention_heads\": 32,\r\n  \"num_hidden_layers\": 32,\r\n  \"num_key_value_heads\": 8,\r\n  \"pretraining_tp\": 1,\r\n  \"rms_norm_eps\": 1e-05,\r\n  \"rope_scaling\": null,\r\n  \"rope_theta\": 3580165449.0,\r\n  \"tie_word_embeddings\": false,\r\n  \"torch_dtype\": \"bfloat16\",\r\n  \"transformers_version\": \"4.42.3\",\r\n  \"use_cache\": true,\r\n  \"vocab_size\": 128256\r\n}\r\n\r\n07/05/2024 09:00:03 - INFO - llamafactory.model.model_utils.quantization - Quantizing model to 4 bit with bitsandbytes.\r\n[INFO|modeling_utils.py:3556] 2024-07-05 09:00:03,878 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--gradientai--Llama-3-8B-Instruct-Gradient-1048k/snapshots/8697fb25cb77c852311e03b4464b8467471d56a4/model.safetensors.index.json\r\n[INFO|modeling_utils.py:1531] 2024-07-05 09:00:03,880 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.\r\n[INFO|configuration_utils.py:1000] 2024-07-05 09:00:03,881 >> Generate config GenerationConfig {\r\n  \"bos_token_id\": 128000,\r\n  \"eos_token_id\": 128001\r\n}\r\n\r\n\rLoading checkpoint shards:   0%|                          | 0/4 [00:00<?, ?it/s]\rLoading checkpoint shards:  25%|████▌             | 1/4 [00:01<00:03,  1.19s/it]\rLoading checkpoint shards:  50%|█████████         | 2/4 [00:02<00:02,  1.15s/it]\rLoading checkpoint shards:  75%|█████████████▌    | 3/4 [00:03<00:01,  1.12s/it]\rLoading checkpoint shards: 100%|██████████████████| 4/4 [00:03<00:00,  1.16it/s]\rLoading checkpoint shards: 100%|██████████████████| 4/4 [00:03<00:00,  1.04it/s]\r\n[INFO|modeling_utils.py:4364] 2024-07-05 09:00:08,031 >> All model checkpoint weights were used when initializing LlamaForCausalLM.\r\n\r\n[INFO|modeling_utils.py:4372] 2024-07-05 09:00:08,031 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at gradientai/Llama-3-8B-Instruct-Gradient-1048k.\r\nIf your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.\r\n[INFO|configuration_utils.py:955] 2024-07-05 09:00:08,056 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--gradientai--Llama-3-8B-Instruct-Gradient-1048k/snapshots/8697fb25cb77c852311e03b4464b8467471d56a4/generation_config.json\r\n[INFO|configuration_utils.py:1000] 2024-07-05 09:00:08,057 >> Generate config GenerationConfig {\r\n  \"bos_token_id\": 128000,\r\n  \"do_sample\": true,\r\n  \"eos_token_id\": [\r\n    128001,\r\n    128009\r\n  ],\r\n  \"max_length\": 4096,\r\n  \"temperature\": 0.6,\r\n  \"top_p\": 0.9\r\n}\r\n\r\n07/05/2024 09:00:08 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.\r\n07/05/2024 09:00:08 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.\r\n07/05/2024 09:00:08 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.\r\n07/05/2024 09:00:08 - INFO - llamafactory.model.adapter - Fine-tuning method: LoRA\r\n07/05/2024 09:00:08 - INFO - llamafactory.model.model_utils.misc - Found linear modules: gate_proj,v_proj,o_proj,k_proj,up_proj,down_proj,q_proj\r\n07/05/2024 09:00:08 - INFO - llamafactory.model.loader - trainable params: 20,971,520 || all params: 8,051,232,768 || trainable%: 0.2605\r\n[INFO|trainer.py:642] 2024-07-05 09:00:09,078 >> Using auto half precision backend\r\ntraining_args.resume_from_checkpoint: None\r\n07/05/2024 09:00:09 - INFO - llamafactory.train.trainer_utils - Using LoRA+ optimizer with loraplus lr ratio 16.00.\r\n[INFO|trainer.py:2128] 2024-07-05 09:00:09,496 >> ***** Running training *****\r\n[INFO|trainer.py:2129] 2024-07-05 09:00:09,496 >>   Num examples = 4,482\r\n[INFO|trainer.py:2130] 2024-07-05 09:00:09,496 >>   Num Epochs = 6\r\n[INFO|trainer.py:2131] 2024-07-05 09:00:09,496 >>   Instantaneous batch size per device = 1\r\n[INFO|trainer.py:2134] 2024-07-05 09:00:09,496 >>   Total train batch size (w. parallel, distributed & accumulation) = 8\r\n[INFO|trainer.py:2135] 2024-07-05 09:00:09,496 >>   Gradient Accumulation steps = 8\r\n[INFO|trainer.py:2136] 2024-07-05 09:00:09,496 >>   Total optimization steps = 3,360\r\n[INFO|trainer.py:2137] 2024-07-05 09:00:09,500 >>   Number of trainable parameters = 20,971,520\r\n\r  0%|                                                  | 0/3360 [00:00<?, ?it/s]\r  0%|                                       | 1/3360 [00:24<23:13:32, 24.89s/it]\r  0%|                                       | 2/3360 [00:50<23:50:05, 25.55s/it]\r  0%|                                       | 3/3360 [01:14<22:51:20, 24.51s/it]\r  0%|                                       | 4/3360 [01:33<21:02:08, 22.57s/it]\r  0%|                                       | 5/3360 [02:02<23:03:53, 24.75s/it]\r  0%|                                       | 6/3360 [02:29<23:55:47, 25.69s/it]\r  0%|                                       | 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2.0833333333333336e-05, 'epoch': 0.12}\r\n\r  2%|▊                                     | 70/3360 [29:03<22:37:06, 24.75s/it]\r  2%|▊                                     | 71/3360 [29:27<22:18:57, 24.43s/it]\r  2%|▊                                     | 72/3360 [29:51<22:11:29, 24.30s/it]\r  2%|▊                                     | 73/3360 [30:19<23:11:26, 25.40s/it]\r  2%|▊                                     | 74/3360 [30:46<23:37:59, 25.89s/it]\r  2%|▊                                     | 75/3360 [31:16<24:43:13, 27.09s/it]\r  2%|▊                                     | 76/3360 [31:39<23:35:14, 25.86s/it]\r  2%|▊                                     | 77/3360 [32:07<24:12:33, 26.55s/it]\r  2%|▉                                     | 78/3360 [32:33<24:05:03, 26.42s/it]\r  2%|▉                                     | 79/3360 [33:04<25:15:23, 27.71s/it]\r  2%|▉                                     | 80/3360 [33:28<24:09:50, 26.52s/it]\r                                                       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+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "!./scripts/tune-lf.sh config/llama3_8b_lora_sft.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
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+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Workspace/Users/donghao.huang@mastercard.com/llm-finetuning\r\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/html": [
+       "<style scoped>\n",
+       "  .ansiout {\n",
+       "    display: block;\n",
+       "    unicode-bidi: embed;\n",
+       "    white-space: pre-wrap;\n",
+       "    word-wrap: break-word;\n",
+       "    word-break: break-all;\n",
+       "    font-family: \"Menlo\", \"Monaco\", \"Consolas\", \"Ubuntu Mono\", \"Source Code Pro\", monospace;\n",
+       "    font-size: 13px;\n",
+       "    color: #555;\n",
+       "    margin-left: 4px;\n",
+       "    line-height: 19px;\n",
+       "  }\n",
+       "</style>"
+      ]
+     },
+     "metadata": {
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+       "arguments": {},
+       "data": "",
+       "errorSummary": "",
+       "errorTraceType": null,
+       "metadata": {},
+       "type": "ipynbError"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def evaluate_model_all_epochs(model_name, num_train_epochs, adapter_path_base):\n",
+    "    os.environ[\"MODEL_NAME\"] = model_name \n",
+    "    for i in range(num_train_epochs + 1):\n",
+    "        print(f\"Epoch {i}\")\n",
+    "        if i == 0:\n",
+    "            os.unsetenv(\"ADAPTER_NAME_OR_PATH\")\n",
+    "        else:\n",
+    "            adapter_path = f\"{adapter_path_base}/checkpoint-{560 * i}\"\n",
+    "            os.environ[\"ADAPTER_NAME_OR_PATH\"] = adapter_path\n",
+    "            \n",
+    "        !python llm_toolkit/eval_lf.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
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+    {
+     "output_type": "display_data",
+     "data": {
+      "text/html": [
+       "<style scoped>\n",
+       "  .ansiout {\n",
+       "    display: block;\n",
+       "    unicode-bidi: embed;\n",
+       "    white-space: pre-wrap;\n",
+       "    word-wrap: break-word;\n",
+       "    word-break: break-all;\n",
+       "    font-family: \"Menlo\", \"Monaco\", \"Consolas\", \"Ubuntu Mono\", \"Source Code Pro\", monospace;\n",
+       "    font-size: 13px;\n",
+       "    color: #555;\n",
+       "    margin-left: 4px;\n",
+       "    line-height: 19px;\n",
+       "  }\n",
+       "</style>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.databricks.v1+output": {
+       "arguments": {},
+       "data": "",
+       "errorSummary": "",
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+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "evaluate_model_all_epochs(\"gradientai/Llama-3-8B-Instruct-Gradient-1048k\", num_train_epochs, \"/Workspace/Users/donghao.huang@mastercard.com/lf-saves/llama3-8b/lora/sft\")"
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