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sqllama-V0

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matt-tries-dl commited on
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4f1cd24
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1 Parent(s): b444d89

trained adaptor

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Files changed (4) hide show
  1. llama_test.ipynb +1837 -60
  2. res.txt +515 -0
  3. sqllama-out/adapter_config.json +18 -0
  4. sqllama-out/adapter_model.bin +3 -0
llama_test.ipynb CHANGED
@@ -39,15 +39,31 @@
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  "name": "stderr",
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  "output_type": "stream",
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  "text": [
 
 
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  "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
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  "The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. \n",
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  "The class this function is called from is 'LlamaTokenizer'.\n"
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  ]
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  },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  {
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  "data": {
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  "application/vnd.jupyter.widget-view+json": {
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- "model_id": "3ab80e2a1c0744e0af747ba63429a2af",
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  "version_major": 2,
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  "version_minor": 0
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  },
@@ -61,6 +77,7 @@
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  ],
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  "source": [
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  "from transformers import LlamaTokenizer, LlamaForCausalLM\n",
 
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  "tokenizer = LlamaTokenizer.from_pretrained(\n",
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  " \"decapoda-research/llama-7b-hf\")\n",
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  " \n",
@@ -68,9 +85,12 @@
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  "#tokenizer.padding_side = 'left'\n",
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  "model = LlamaForCausalLM.from_pretrained(\n",
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  " \"decapoda-research/llama-7b-hf\",\n",
 
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  " device_map=\"auto\",\n",
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  " torch_dtype=torch.float16\n",
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- ")\n"
 
 
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  ]
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  },
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  {
@@ -168,7 +188,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 4,
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  "metadata": {},
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  "outputs": [
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  {
@@ -232,7 +252,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 5,
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  "metadata": {},
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  "outputs": [
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  {
@@ -241,34 +261,35 @@
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  "text": [
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  "\n",
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  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
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- "### Question: What is the Displacement of the Iveco F1CE3481E Engine?\n",
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- "### Input: Table 2-1415821-6 has columns Model (text),Engine (text),Displacement (text),Valvetrain (text),Fuel system (text),Max. power at rpm (text),Max. torque at rpm (text). \n",
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- "### Answer: SELECT Displacement FROM 2-1415821-6 WHERE Engine = 'iveco f1ce3481e'\n",
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  "\n",
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  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
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- "### Question: What is the record of team utah?\n",
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- "### Input: Table 2-17355628-9 has columns Game (real),Date (text),Team (text),Score (text),High points (text),High rebounds (text),High assists (text),Location Attendance (text),Record (text). \n",
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- "### Answer: SELECT Record FROM 2-17355628-9 WHERE Team = 'utah'\n",
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  "\n",
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  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
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- "### Question: What is the home of the team with a 16-8 record?\n",
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- "### Input: Table 2-16188254-4 has columns Date (text),Visitor (text),Score (text),Home (text),Leading scorer (text),Attendance (text),Record (text). \n",
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- "### Answer: SELECT Home FROM 2-16188254-4 WHERE Record = '16-8'\n",
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  "\n",
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  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
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- "### Question: What week did the Galaxy play the Amsterdam Admirals?\n",
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- "### Input: Table 1-24814477-2 has columns Week (real),Date (text),Kickoff (text),Opponent (text),Final score (text),Team record (text),Game site (text),Attendance (real). \n",
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- "### Answer: SELECT Week FROM 1-24814477-2 WHERE Opponent = 'Amsterdam Admirals'\n",
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  "\n",
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  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
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- "### Question: How many caps did Mitchell Duke have overall?\n",
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- "### Input: Table 2-1257177-1 has columns Player (text),Country (text),Caps (real),Goals (text),Years Active (text). \n",
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- "### Answer: SELECT COUNT Caps FROM 2-1257177-1 WHERE Player = 'mitchell duke'\n"
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  ]
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  }
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  ],
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  "source": [
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  "import random\n",
 
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  "\n",
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  "# defined by WikiSQL\n",
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  "\n",
@@ -366,13 +387,13 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 26,
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  "metadata": {},
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  "outputs": [
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  {
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  "data": {
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  "application/vnd.jupyter.widget-view+json": {
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- "model_id": "4f44918087484dd58b958a64cabdecb6",
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  "version_major": 2,
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  "version_minor": 0
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  },
@@ -407,7 +428,7 @@
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  " target_modules=['q_proj','v_proj']\n",
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  ")\n",
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  "\n",
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- "modad = get_peft_model(model,lora_cfg)\n",
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  "\n",
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  "tokenizer.pad_token_id = 0\n",
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  "\n",
@@ -422,7 +443,7 @@
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  " padding=\"max_length\"\n",
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  " ))\n",
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  "\n",
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- "#data.remove_columns('prompt')\n",
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  "\n",
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  "targs = transformers.TrainingArguments(\n",
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  " per_device_train_batch_size=MICRO_BATCH,\n",
@@ -438,7 +459,7 @@
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  ")\n",
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  "\n",
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  "\n",
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- "modad.config.use_cache = False"
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  ]
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  },
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  {
@@ -451,7 +472,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 27,
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  "metadata": {},
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  "outputs": [
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  {
@@ -459,10 +480,10 @@
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  "output_type": "stream",
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  "text": [
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  "Dataset({\n",
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- " features: ['prompt', 'input_ids', 'attention_mask'],\n",
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  " num_rows: 56355\n",
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  "})\n",
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- "{'prompt': \"Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\\n### Question: Tell me what the notes are for South Australia \\n### Input: Table 1-1000181-1 has columns State/territory (text),Text/background colour (text),Format (text),Current slogan (text),Current series (text),Notes (text). \\n### Answer: SELECT Notes FROM 1-1000181-1 WHERE Current slogan = 'SOUTH AUSTRALIA'\", 'input_ids': [0, 13866, 338, 263, 1139, 393, 16612, 263, 848, 2009, 29892, 3300, 2859, 411, 385, 1881, 393, 16612, 263, 3758, 1591, 29889, 29871, 14350, 263, 3758, 2346, 393, 5663, 17180, 278, 848, 29889, 13, 2277, 29937, 894, 29901, 24948, 592, 825, 278, 11486, 526, 363, 4275, 8314, 29871, 13, 2277, 29937, 10567, 29901, 6137, 29871, 29896, 29899, 29896, 29900, 29900, 29900, 29896, 29947, 29896, 29899, 29896, 756, 4341, 4306, 29914, 357, 768, 706, 313, 726, 511, 1626, 29914, 7042, 12384, 313, 726, 511, 5809, 313, 726, 511, 7583, 269, 1188, 273, 313, 726, 511, 7583, 3652, 313, 726, 511, 3664, 267, 313, 726, 467, 259, 13, 2277, 29937, 673, 29901, 5097, 29871, 8695, 3895, 29871, 29896, 29899, 29896, 29900, 29900, 29900, 29896, 29947, 29896, 29899, 29896, 5754, 9626, 269, 1188, 273, 353, 525, 6156, 2692, 29950, 319, 29965, 10810, 1964, 10764, 29915, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}\n"
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  ]
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  }
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  ],
@@ -483,46 +504,1802 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 25,
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  "metadata": {},
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  "outputs": [
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  {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "/home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/optimization.py:395: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
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- " FutureWarning,\n"
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- ]
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- },
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- {
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- "ename": "ValueError",
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- "evalue": "Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`prompt` in this case) have excessive nesting (inputs type `list` where type `int` is expected).",
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- "output_type": "error",
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- "traceback": [
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- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mconvert_to_tensors\u001b[0;34m(self, tensor_type, prepend_batch_axis)\u001b[0m\n\u001b[1;32m 716\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 717\u001b[0;31m \u001b[0mtensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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- "\u001b[0;31mValueError\u001b[0m: too many dimensions 'str'",
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- "\nThe above exception was the direct cause of the following exception:\n",
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- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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- "\u001b[0;32m/var/tmp/ipykernel_2309/3549391384.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata_collator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataCollatorForLanguageModeling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m )\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sqllama-out'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1664\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1665\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1666\u001b[0;31m \u001b[0mignore_keys_for_eval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_keys_for_eval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1667\u001b[0m )\n\u001b[1;32m 1668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1897\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1898\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1899\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch_iterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1900\u001b[0m \u001b[0mtotal_batched_samples\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1901\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrng_to_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 626\u001b[0m \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 629\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 630\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
512
- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 670\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 671\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 672\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 673\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
513
- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
514
- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/data/data_collator.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, features, return_tensors)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mreturn_tensors\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorch_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 46\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mreturn_tensors\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"np\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
515
- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/data/data_collator.py\u001b[0m in \u001b[0;36mtorch_call\u001b[0;34m(self, examples)\u001b[0m\n\u001b[1;32m 727\u001b[0m \u001b[0;31m# Handle dict or lists with proper padding and conversion to tensor.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 728\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad_to_multiple_of\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_to_multiple_of\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 730\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 731\u001b[0m batch = {\n",
516
- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mpad\u001b[0;34m(self, encoded_inputs, padding, max_length, pad_to_multiple_of, return_attention_mask, return_tensors, verbose)\u001b[0m\n\u001b[1;32m 3033\u001b[0m \u001b[0mbatch_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3034\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3035\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mBatchEncoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_tensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3036\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3037\u001b[0m def create_token_type_ids_from_sequences(\n",
517
- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, encoding, tensor_type, prepend_batch_axis, n_sequences)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_sequences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_sequences\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtensor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprepend_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprepend_batch_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
518
- "\u001b[0;32m~/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mconvert_to_tensors\u001b[0;34m(self, tensor_type, prepend_batch_axis)\u001b[0m\n\u001b[1;32m 736\u001b[0m \u001b[0;34mf\" features (`{key}` in this case) have excessive nesting (inputs type `list` where type `int` is\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[0;34m\" expected).\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 738\u001b[0;31m ) from e\n\u001b[0m\u001b[1;32m 739\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 740\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
519
- "\u001b[0;31mValueError\u001b[0m: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`prompt` in this case) have excessive nesting (inputs type `list` where type `int` is expected)."
520
- ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
521
  }
522
  ],
523
  "source": [
 
 
524
  "trainer = transformers.Trainer(\n",
525
- " model = modad,\n",
526
  " train_dataset = data,\n",
527
  " args = targs,\n",
528
  " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
 
39
  "name": "stderr",
40
  "output_type": "stream",
41
  "text": [
42
+ "/home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /opt/conda did not contain libcudart.so as expected! Searching further paths...\n",
43
+ " warn(msg)\n",
44
  "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
45
  "The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. \n",
46
  "The class this function is called from is 'LlamaTokenizer'.\n"
47
  ]
48
  },
49
+ {
50
+ "name": "stdout",
51
+ "output_type": "stream",
52
+ "text": [
53
+ "\n",
54
+ "===================================BUG REPORT===================================\n",
55
+ "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
56
+ "================================================================================\n",
57
+ "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
58
+ "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
59
+ "CUDA SETUP: Detected CUDA version 113\n",
60
+ "CUDA SETUP: Loading binary /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/bitsandbytes/libbitsandbytes_cuda113.so...\n"
61
+ ]
62
+ },
63
  {
64
  "data": {
65
  "application/vnd.jupyter.widget-view+json": {
66
+ "model_id": "5616e727844a4f0b9efaff97aa2f9d75",
67
  "version_major": 2,
68
  "version_minor": 0
69
  },
 
77
  ],
78
  "source": [
79
  "from transformers import LlamaTokenizer, LlamaForCausalLM\n",
80
+ "from peft import prepare_model_for_int8_training\n",
81
  "tokenizer = LlamaTokenizer.from_pretrained(\n",
82
  " \"decapoda-research/llama-7b-hf\")\n",
83
  " \n",
 
85
  "#tokenizer.padding_side = 'left'\n",
86
  "model = LlamaForCausalLM.from_pretrained(\n",
87
  " \"decapoda-research/llama-7b-hf\",\n",
88
+ " load_in_8bit=True,\n",
89
  " device_map=\"auto\",\n",
90
  " torch_dtype=torch.float16\n",
91
+ ")\n",
92
+ "\n",
93
+ "model = prepare_model_for_int8_training(model)\n"
94
  ]
95
  },
96
  {
 
188
  },
189
  {
190
  "cell_type": "code",
191
+ "execution_count": 3,
192
  "metadata": {},
193
  "outputs": [
194
  {
 
252
  },
253
  {
254
  "cell_type": "code",
255
+ "execution_count": 4,
256
  "metadata": {},
257
  "outputs": [
258
  {
 
261
  "text": [
262
  "\n",
263
  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
264
+ "### Question: How many games does novica veličković have when there's more than 24 rebounds?\n",
265
+ "### Input: Table 2-16050349-8 has columns Rank (real),Name (text),Team (text),Games (real),Rebounds (real). \n",
266
+ "### Answer: SELECT COUNT Games FROM 2-16050349-8 WHERE Name = 'novica veličković' AND Rebounds > 24\n",
267
  "\n",
268
  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
269
+ "### Question: What is the number of capacity at somerset park?\n",
270
+ "### Input: Table 1-11206787-5 has columns Team (text),Stadium (text),Capacity (real),Highest (real),Lowest (real),Average (real). \n",
271
+ "### Answer: SELECT COUNT Capacity FROM 1-11206787-5 WHERE Stadium = 'Somerset Park'\n",
272
  "\n",
273
  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
274
+ "### Question: What is the number & name with an Undergoing overhaul, restoration or repairs date?\n",
275
+ "### Input: Table 2-11913905-6 has columns Number & Name (text),Description (text),Livery (text),Owner(s) (text),Date (text). \n",
276
+ "### Answer: SELECT Number & Name FROM 2-11913905-6 WHERE Date = 'undergoing overhaul, restoration or repairs'\n",
277
  "\n",
278
  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
279
+ "### Question: What year did Orlando have a School/Club team in Clemson?\n",
280
+ "### Input: Table 2-15621965-7 has columns Player (text),Nationality (text),Position (text),Years in Orlando (text),School/Club Team (text). \n",
281
+ "### Answer: SELECT Years in Orlando FROM 2-15621965-7 WHERE School/Club Team = 'clemson'\n",
282
  "\n",
283
  "Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.\n",
284
+ "### Question: How many Deaths have a Fate of damaged, and a Tonnage (GRT) smaller than 4,917?\n",
285
+ "### Input: Table 2-18914307-1 has columns Date (text),Ship Name (text),Flag (text),Tonnage ( GRT ) (real),Fate (text),Deaths (real). \n",
286
+ "### Answer: SELECT COUNT Deaths FROM 2-18914307-1 WHERE Fate = 'damaged' AND Tonnage ( GRT ) < 4,917\n"
287
  ]
288
  }
289
  ],
290
  "source": [
291
  "import random\n",
292
+ "import json\n",
293
  "\n",
294
  "# defined by WikiSQL\n",
295
  "\n",
 
387
  },
388
  {
389
  "cell_type": "code",
390
+ "execution_count": 5,
391
  "metadata": {},
392
  "outputs": [
393
  {
394
  "data": {
395
  "application/vnd.jupyter.widget-view+json": {
396
+ "model_id": "431b36f60a3940cf8646e1bea4324745",
397
  "version_major": 2,
398
  "version_minor": 0
399
  },
 
428
  " target_modules=['q_proj','v_proj']\n",
429
  ")\n",
430
  "\n",
431
+ "model = get_peft_model(model,lora_cfg)\n",
432
  "\n",
433
  "tokenizer.pad_token_id = 0\n",
434
  "\n",
 
443
  " padding=\"max_length\"\n",
444
  " ))\n",
445
  "\n",
446
+ "data = data.remove_columns('prompt')\n",
447
  "\n",
448
  "targs = transformers.TrainingArguments(\n",
449
  " per_device_train_batch_size=MICRO_BATCH,\n",
 
459
  ")\n",
460
  "\n",
461
  "\n",
462
+ "model.config.use_cache = False"
463
  ]
464
  },
465
  {
 
472
  },
473
  {
474
  "cell_type": "code",
475
+ "execution_count": 6,
476
  "metadata": {},
477
  "outputs": [
478
  {
 
480
  "output_type": "stream",
481
  "text": [
482
  "Dataset({\n",
483
+ " features: ['input_ids', 'attention_mask'],\n",
484
  " num_rows: 56355\n",
485
  "})\n",
486
+ "{'input_ids': [0, 13866, 338, 263, 1139, 393, 16612, 263, 848, 2009, 29892, 3300, 2859, 411, 385, 1881, 393, 16612, 263, 3758, 1591, 29889, 29871, 14350, 263, 3758, 2346, 393, 5663, 17180, 278, 848, 29889, 13, 2277, 29937, 894, 29901, 24948, 592, 825, 278, 11486, 526, 363, 4275, 8314, 29871, 13, 2277, 29937, 10567, 29901, 6137, 29871, 29896, 29899, 29896, 29900, 29900, 29900, 29896, 29947, 29896, 29899, 29896, 756, 4341, 4306, 29914, 357, 768, 706, 313, 726, 511, 1626, 29914, 7042, 12384, 313, 726, 511, 5809, 313, 726, 511, 7583, 269, 1188, 273, 313, 726, 511, 7583, 3652, 313, 726, 511, 3664, 267, 313, 726, 467, 259, 13, 2277, 29937, 673, 29901, 5097, 29871, 8695, 3895, 29871, 29896, 29899, 29896, 29900, 29900, 29900, 29896, 29947, 29896, 29899, 29896, 5754, 9626, 269, 1188, 273, 353, 525, 6156, 2692, 29950, 319, 29965, 10810, 1964, 10764, 29915, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}\n"
487
  ]
488
  }
489
  ],
 
504
  },
505
  {
506
  "cell_type": "code",
507
+ "execution_count": 6,
508
  "metadata": {},
509
  "outputs": [
510
  {
511
+ "data": {
512
+ "text/html": [
513
+ "\n",
514
+ " <div>\n",
515
+ " \n",
516
+ " <progress value='440' max='440' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
517
+ " [440/440 11:19:07, Epoch 0/1]\n",
518
+ " </div>\n",
519
+ " <table border=\"1\" class=\"dataframe\">\n",
520
+ " <thead>\n",
521
+ " <tr style=\"text-align: left;\">\n",
522
+ " <th>Step</th>\n",
523
+ " <th>Training Loss</th>\n",
524
+ " </tr>\n",
525
+ " </thead>\n",
526
+ " <tbody>\n",
527
+ " <tr>\n",
528
+ " <td>1</td>\n",
529
+ " <td>2.517200</td>\n",
530
+ " </tr>\n",
531
+ " <tr>\n",
532
+ " <td>2</td>\n",
533
+ " <td>2.482300</td>\n",
534
+ " </tr>\n",
535
+ " <tr>\n",
536
+ " <td>3</td>\n",
537
+ " <td>2.444100</td>\n",
538
+ " </tr>\n",
539
+ " <tr>\n",
540
+ " <td>4</td>\n",
541
+ " <td>2.456500</td>\n",
542
+ " </tr>\n",
543
+ " <tr>\n",
544
+ " <td>5</td>\n",
545
+ " <td>2.441400</td>\n",
546
+ " </tr>\n",
547
+ " <tr>\n",
548
+ " <td>6</td>\n",
549
+ " <td>2.484600</td>\n",
550
+ " </tr>\n",
551
+ " <tr>\n",
552
+ " <td>7</td>\n",
553
+ " <td>2.424000</td>\n",
554
+ " </tr>\n",
555
+ " <tr>\n",
556
+ " <td>8</td>\n",
557
+ " <td>2.477900</td>\n",
558
+ " </tr>\n",
559
+ " <tr>\n",
560
+ " <td>9</td>\n",
561
+ " <td>2.429700</td>\n",
562
+ " </tr>\n",
563
+ " <tr>\n",
564
+ " <td>10</td>\n",
565
+ " <td>2.436000</td>\n",
566
+ " </tr>\n",
567
+ " <tr>\n",
568
+ " <td>11</td>\n",
569
+ " <td>2.422000</td>\n",
570
+ " </tr>\n",
571
+ " <tr>\n",
572
+ " <td>12</td>\n",
573
+ " <td>2.408800</td>\n",
574
+ " </tr>\n",
575
+ " <tr>\n",
576
+ " <td>13</td>\n",
577
+ " <td>2.402900</td>\n",
578
+ " </tr>\n",
579
+ " <tr>\n",
580
+ " <td>14</td>\n",
581
+ " <td>2.424500</td>\n",
582
+ " </tr>\n",
583
+ " <tr>\n",
584
+ " <td>15</td>\n",
585
+ " <td>2.421800</td>\n",
586
+ " </tr>\n",
587
+ " <tr>\n",
588
+ " <td>16</td>\n",
589
+ " <td>2.424100</td>\n",
590
+ " </tr>\n",
591
+ " <tr>\n",
592
+ " <td>17</td>\n",
593
+ " <td>2.404000</td>\n",
594
+ " </tr>\n",
595
+ " <tr>\n",
596
+ " <td>18</td>\n",
597
+ " <td>2.386900</td>\n",
598
+ " </tr>\n",
599
+ " <tr>\n",
600
+ " <td>19</td>\n",
601
+ " <td>2.414400</td>\n",
602
+ " </tr>\n",
603
+ " <tr>\n",
604
+ " <td>20</td>\n",
605
+ " <td>2.370600</td>\n",
606
+ " </tr>\n",
607
+ " <tr>\n",
608
+ " <td>21</td>\n",
609
+ " <td>2.382500</td>\n",
610
+ " </tr>\n",
611
+ " <tr>\n",
612
+ " <td>22</td>\n",
613
+ " <td>2.350700</td>\n",
614
+ " </tr>\n",
615
+ " <tr>\n",
616
+ " <td>23</td>\n",
617
+ " <td>2.385700</td>\n",
618
+ " </tr>\n",
619
+ " <tr>\n",
620
+ " <td>24</td>\n",
621
+ " <td>2.350400</td>\n",
622
+ " </tr>\n",
623
+ " <tr>\n",
624
+ " <td>25</td>\n",
625
+ " <td>2.354900</td>\n",
626
+ " </tr>\n",
627
+ " <tr>\n",
628
+ " <td>26</td>\n",
629
+ " <td>2.345400</td>\n",
630
+ " </tr>\n",
631
+ " <tr>\n",
632
+ " <td>27</td>\n",
633
+ " <td>2.373000</td>\n",
634
+ " </tr>\n",
635
+ " <tr>\n",
636
+ " <td>28</td>\n",
637
+ " <td>2.343200</td>\n",
638
+ " </tr>\n",
639
+ " <tr>\n",
640
+ " <td>29</td>\n",
641
+ " <td>2.374300</td>\n",
642
+ " </tr>\n",
643
+ " <tr>\n",
644
+ " <td>30</td>\n",
645
+ " <td>2.325000</td>\n",
646
+ " </tr>\n",
647
+ " <tr>\n",
648
+ " <td>31</td>\n",
649
+ " <td>2.352000</td>\n",
650
+ " </tr>\n",
651
+ " <tr>\n",
652
+ " <td>32</td>\n",
653
+ " <td>2.344600</td>\n",
654
+ " </tr>\n",
655
+ " <tr>\n",
656
+ " <td>33</td>\n",
657
+ " <td>2.360000</td>\n",
658
+ " </tr>\n",
659
+ " <tr>\n",
660
+ " <td>34</td>\n",
661
+ " <td>2.347400</td>\n",
662
+ " </tr>\n",
663
+ " <tr>\n",
664
+ " <td>35</td>\n",
665
+ " <td>2.346700</td>\n",
666
+ " </tr>\n",
667
+ " <tr>\n",
668
+ " <td>36</td>\n",
669
+ " <td>2.329000</td>\n",
670
+ " </tr>\n",
671
+ " <tr>\n",
672
+ " <td>37</td>\n",
673
+ " <td>2.314600</td>\n",
674
+ " </tr>\n",
675
+ " <tr>\n",
676
+ " <td>38</td>\n",
677
+ " <td>2.306000</td>\n",
678
+ " </tr>\n",
679
+ " <tr>\n",
680
+ " <td>39</td>\n",
681
+ " <td>2.292600</td>\n",
682
+ " </tr>\n",
683
+ " <tr>\n",
684
+ " <td>40</td>\n",
685
+ " <td>2.333800</td>\n",
686
+ " </tr>\n",
687
+ " <tr>\n",
688
+ " <td>41</td>\n",
689
+ " <td>2.311500</td>\n",
690
+ " </tr>\n",
691
+ " <tr>\n",
692
+ " <td>42</td>\n",
693
+ " <td>2.308300</td>\n",
694
+ " </tr>\n",
695
+ " <tr>\n",
696
+ " <td>43</td>\n",
697
+ " <td>2.287400</td>\n",
698
+ " </tr>\n",
699
+ " <tr>\n",
700
+ " <td>44</td>\n",
701
+ " <td>2.314100</td>\n",
702
+ " </tr>\n",
703
+ " <tr>\n",
704
+ " <td>45</td>\n",
705
+ " <td>2.280400</td>\n",
706
+ " </tr>\n",
707
+ " <tr>\n",
708
+ " <td>46</td>\n",
709
+ " <td>2.261300</td>\n",
710
+ " </tr>\n",
711
+ " <tr>\n",
712
+ " <td>47</td>\n",
713
+ " <td>2.274200</td>\n",
714
+ " </tr>\n",
715
+ " <tr>\n",
716
+ " <td>48</td>\n",
717
+ " <td>2.246900</td>\n",
718
+ " </tr>\n",
719
+ " <tr>\n",
720
+ " <td>49</td>\n",
721
+ " <td>2.257100</td>\n",
722
+ " </tr>\n",
723
+ " <tr>\n",
724
+ " <td>50</td>\n",
725
+ " <td>2.274500</td>\n",
726
+ " </tr>\n",
727
+ " <tr>\n",
728
+ " <td>51</td>\n",
729
+ " <td>2.245500</td>\n",
730
+ " </tr>\n",
731
+ " <tr>\n",
732
+ " <td>52</td>\n",
733
+ " <td>2.250700</td>\n",
734
+ " </tr>\n",
735
+ " <tr>\n",
736
+ " <td>53</td>\n",
737
+ " <td>2.296600</td>\n",
738
+ " </tr>\n",
739
+ " <tr>\n",
740
+ " <td>54</td>\n",
741
+ " <td>2.261000</td>\n",
742
+ " </tr>\n",
743
+ " <tr>\n",
744
+ " <td>55</td>\n",
745
+ " <td>2.223800</td>\n",
746
+ " </tr>\n",
747
+ " <tr>\n",
748
+ " <td>56</td>\n",
749
+ " <td>2.244000</td>\n",
750
+ " </tr>\n",
751
+ " <tr>\n",
752
+ " <td>57</td>\n",
753
+ " <td>2.228500</td>\n",
754
+ " </tr>\n",
755
+ " <tr>\n",
756
+ " <td>58</td>\n",
757
+ " <td>2.229100</td>\n",
758
+ " </tr>\n",
759
+ " <tr>\n",
760
+ " <td>59</td>\n",
761
+ " <td>2.162300</td>\n",
762
+ " </tr>\n",
763
+ " <tr>\n",
764
+ " <td>60</td>\n",
765
+ " <td>2.238000</td>\n",
766
+ " </tr>\n",
767
+ " <tr>\n",
768
+ " <td>61</td>\n",
769
+ " <td>2.246000</td>\n",
770
+ " </tr>\n",
771
+ " <tr>\n",
772
+ " <td>62</td>\n",
773
+ " <td>2.184800</td>\n",
774
+ " </tr>\n",
775
+ " <tr>\n",
776
+ " <td>63</td>\n",
777
+ " <td>2.195000</td>\n",
778
+ " </tr>\n",
779
+ " <tr>\n",
780
+ " <td>64</td>\n",
781
+ " <td>2.199500</td>\n",
782
+ " </tr>\n",
783
+ " <tr>\n",
784
+ " <td>65</td>\n",
785
+ " <td>2.180000</td>\n",
786
+ " </tr>\n",
787
+ " <tr>\n",
788
+ " <td>66</td>\n",
789
+ " <td>2.179800</td>\n",
790
+ " </tr>\n",
791
+ " <tr>\n",
792
+ " <td>67</td>\n",
793
+ " <td>2.149700</td>\n",
794
+ " </tr>\n",
795
+ " <tr>\n",
796
+ " <td>68</td>\n",
797
+ " <td>2.177000</td>\n",
798
+ " </tr>\n",
799
+ " <tr>\n",
800
+ " <td>69</td>\n",
801
+ " <td>2.156600</td>\n",
802
+ " </tr>\n",
803
+ " <tr>\n",
804
+ " <td>70</td>\n",
805
+ " <td>2.193400</td>\n",
806
+ " </tr>\n",
807
+ " <tr>\n",
808
+ " <td>71</td>\n",
809
+ " <td>2.163400</td>\n",
810
+ " </tr>\n",
811
+ " <tr>\n",
812
+ " <td>72</td>\n",
813
+ " <td>2.147400</td>\n",
814
+ " </tr>\n",
815
+ " <tr>\n",
816
+ " <td>73</td>\n",
817
+ " <td>2.134700</td>\n",
818
+ " </tr>\n",
819
+ " <tr>\n",
820
+ " <td>74</td>\n",
821
+ " <td>2.133200</td>\n",
822
+ " </tr>\n",
823
+ " <tr>\n",
824
+ " <td>75</td>\n",
825
+ " <td>2.118000</td>\n",
826
+ " </tr>\n",
827
+ " <tr>\n",
828
+ " <td>76</td>\n",
829
+ " <td>2.139000</td>\n",
830
+ " </tr>\n",
831
+ " <tr>\n",
832
+ " <td>77</td>\n",
833
+ " <td>2.102000</td>\n",
834
+ " </tr>\n",
835
+ " <tr>\n",
836
+ " <td>78</td>\n",
837
+ " <td>2.109100</td>\n",
838
+ " </tr>\n",
839
+ " <tr>\n",
840
+ " <td>79</td>\n",
841
+ " <td>2.099000</td>\n",
842
+ " </tr>\n",
843
+ " <tr>\n",
844
+ " <td>80</td>\n",
845
+ " <td>2.097500</td>\n",
846
+ " </tr>\n",
847
+ " <tr>\n",
848
+ " <td>81</td>\n",
849
+ " <td>2.073200</td>\n",
850
+ " </tr>\n",
851
+ " <tr>\n",
852
+ " <td>82</td>\n",
853
+ " <td>2.055200</td>\n",
854
+ " </tr>\n",
855
+ " <tr>\n",
856
+ " <td>83</td>\n",
857
+ " <td>2.078100</td>\n",
858
+ " </tr>\n",
859
+ " <tr>\n",
860
+ " <td>84</td>\n",
861
+ " <td>2.104800</td>\n",
862
+ " </tr>\n",
863
+ " <tr>\n",
864
+ " <td>85</td>\n",
865
+ " <td>2.061100</td>\n",
866
+ " </tr>\n",
867
+ " <tr>\n",
868
+ " <td>86</td>\n",
869
+ " <td>2.066500</td>\n",
870
+ " </tr>\n",
871
+ " <tr>\n",
872
+ " <td>87</td>\n",
873
+ " <td>2.073500</td>\n",
874
+ " </tr>\n",
875
+ " <tr>\n",
876
+ " <td>88</td>\n",
877
+ " <td>2.010500</td>\n",
878
+ " </tr>\n",
879
+ " <tr>\n",
880
+ " <td>89</td>\n",
881
+ " <td>2.045700</td>\n",
882
+ " </tr>\n",
883
+ " <tr>\n",
884
+ " <td>90</td>\n",
885
+ " <td>2.026700</td>\n",
886
+ " </tr>\n",
887
+ " <tr>\n",
888
+ " <td>91</td>\n",
889
+ " <td>2.046500</td>\n",
890
+ " </tr>\n",
891
+ " <tr>\n",
892
+ " <td>92</td>\n",
893
+ " <td>2.015300</td>\n",
894
+ " </tr>\n",
895
+ " <tr>\n",
896
+ " <td>93</td>\n",
897
+ " <td>2.019100</td>\n",
898
+ " </tr>\n",
899
+ " <tr>\n",
900
+ " <td>94</td>\n",
901
+ " <td>2.008600</td>\n",
902
+ " </tr>\n",
903
+ " <tr>\n",
904
+ " <td>95</td>\n",
905
+ " <td>1.961000</td>\n",
906
+ " </tr>\n",
907
+ " <tr>\n",
908
+ " <td>96</td>\n",
909
+ " <td>1.974300</td>\n",
910
+ " </tr>\n",
911
+ " <tr>\n",
912
+ " <td>97</td>\n",
913
+ " <td>1.991700</td>\n",
914
+ " </tr>\n",
915
+ " <tr>\n",
916
+ " <td>98</td>\n",
917
+ " <td>1.984700</td>\n",
918
+ " </tr>\n",
919
+ " <tr>\n",
920
+ " <td>99</td>\n",
921
+ " <td>1.975900</td>\n",
922
+ " </tr>\n",
923
+ " <tr>\n",
924
+ " <td>100</td>\n",
925
+ " <td>1.963900</td>\n",
926
+ " </tr>\n",
927
+ " <tr>\n",
928
+ " <td>101</td>\n",
929
+ " <td>1.934300</td>\n",
930
+ " </tr>\n",
931
+ " <tr>\n",
932
+ " <td>102</td>\n",
933
+ " <td>1.990400</td>\n",
934
+ " </tr>\n",
935
+ " <tr>\n",
936
+ " <td>103</td>\n",
937
+ " <td>1.914900</td>\n",
938
+ " </tr>\n",
939
+ " <tr>\n",
940
+ " <td>104</td>\n",
941
+ " <td>1.956100</td>\n",
942
+ " </tr>\n",
943
+ " <tr>\n",
944
+ " <td>105</td>\n",
945
+ " <td>1.943400</td>\n",
946
+ " </tr>\n",
947
+ " <tr>\n",
948
+ " <td>106</td>\n",
949
+ " <td>1.931000</td>\n",
950
+ " </tr>\n",
951
+ " <tr>\n",
952
+ " <td>107</td>\n",
953
+ " <td>1.919000</td>\n",
954
+ " </tr>\n",
955
+ " <tr>\n",
956
+ " <td>108</td>\n",
957
+ " <td>1.912800</td>\n",
958
+ " </tr>\n",
959
+ " <tr>\n",
960
+ " <td>109</td>\n",
961
+ " <td>1.920400</td>\n",
962
+ " </tr>\n",
963
+ " <tr>\n",
964
+ " <td>110</td>\n",
965
+ " <td>1.878300</td>\n",
966
+ " </tr>\n",
967
+ " <tr>\n",
968
+ " <td>111</td>\n",
969
+ " <td>1.890800</td>\n",
970
+ " </tr>\n",
971
+ " <tr>\n",
972
+ " <td>112</td>\n",
973
+ " <td>1.881900</td>\n",
974
+ " </tr>\n",
975
+ " <tr>\n",
976
+ " <td>113</td>\n",
977
+ " <td>1.885400</td>\n",
978
+ " </tr>\n",
979
+ " <tr>\n",
980
+ " <td>114</td>\n",
981
+ " <td>1.908400</td>\n",
982
+ " </tr>\n",
983
+ " <tr>\n",
984
+ " <td>115</td>\n",
985
+ " <td>1.871200</td>\n",
986
+ " </tr>\n",
987
+ " <tr>\n",
988
+ " <td>116</td>\n",
989
+ " <td>1.900000</td>\n",
990
+ " </tr>\n",
991
+ " <tr>\n",
992
+ " <td>117</td>\n",
993
+ " <td>1.888000</td>\n",
994
+ " </tr>\n",
995
+ " <tr>\n",
996
+ " <td>118</td>\n",
997
+ " <td>1.875100</td>\n",
998
+ " </tr>\n",
999
+ " <tr>\n",
1000
+ " <td>119</td>\n",
1001
+ " <td>1.855000</td>\n",
1002
+ " </tr>\n",
1003
+ " <tr>\n",
1004
+ " <td>120</td>\n",
1005
+ " <td>1.852100</td>\n",
1006
+ " </tr>\n",
1007
+ " <tr>\n",
1008
+ " <td>121</td>\n",
1009
+ " <td>1.851200</td>\n",
1010
+ " </tr>\n",
1011
+ " <tr>\n",
1012
+ " <td>122</td>\n",
1013
+ " <td>1.821800</td>\n",
1014
+ " </tr>\n",
1015
+ " <tr>\n",
1016
+ " <td>123</td>\n",
1017
+ " <td>1.853000</td>\n",
1018
+ " </tr>\n",
1019
+ " <tr>\n",
1020
+ " <td>124</td>\n",
1021
+ " <td>1.854700</td>\n",
1022
+ " </tr>\n",
1023
+ " <tr>\n",
1024
+ " <td>125</td>\n",
1025
+ " <td>1.806900</td>\n",
1026
+ " </tr>\n",
1027
+ " <tr>\n",
1028
+ " <td>126</td>\n",
1029
+ " <td>1.845300</td>\n",
1030
+ " </tr>\n",
1031
+ " <tr>\n",
1032
+ " <td>127</td>\n",
1033
+ " <td>1.797800</td>\n",
1034
+ " </tr>\n",
1035
+ " <tr>\n",
1036
+ " <td>128</td>\n",
1037
+ " <td>1.795300</td>\n",
1038
+ " </tr>\n",
1039
+ " <tr>\n",
1040
+ " <td>129</td>\n",
1041
+ " <td>1.799500</td>\n",
1042
+ " </tr>\n",
1043
+ " <tr>\n",
1044
+ " <td>130</td>\n",
1045
+ " <td>1.853900</td>\n",
1046
+ " </tr>\n",
1047
+ " <tr>\n",
1048
+ " <td>131</td>\n",
1049
+ " <td>1.780100</td>\n",
1050
+ " </tr>\n",
1051
+ " <tr>\n",
1052
+ " <td>132</td>\n",
1053
+ " <td>1.789400</td>\n",
1054
+ " </tr>\n",
1055
+ " <tr>\n",
1056
+ " <td>133</td>\n",
1057
+ " <td>1.776700</td>\n",
1058
+ " </tr>\n",
1059
+ " <tr>\n",
1060
+ " <td>134</td>\n",
1061
+ " <td>1.747300</td>\n",
1062
+ " </tr>\n",
1063
+ " <tr>\n",
1064
+ " <td>135</td>\n",
1065
+ " <td>1.753700</td>\n",
1066
+ " </tr>\n",
1067
+ " <tr>\n",
1068
+ " <td>136</td>\n",
1069
+ " <td>1.761300</td>\n",
1070
+ " </tr>\n",
1071
+ " <tr>\n",
1072
+ " <td>137</td>\n",
1073
+ " <td>1.725500</td>\n",
1074
+ " </tr>\n",
1075
+ " <tr>\n",
1076
+ " <td>138</td>\n",
1077
+ " <td>1.710800</td>\n",
1078
+ " </tr>\n",
1079
+ " <tr>\n",
1080
+ " <td>139</td>\n",
1081
+ " <td>1.733500</td>\n",
1082
+ " </tr>\n",
1083
+ " <tr>\n",
1084
+ " <td>140</td>\n",
1085
+ " <td>1.727000</td>\n",
1086
+ " </tr>\n",
1087
+ " <tr>\n",
1088
+ " <td>141</td>\n",
1089
+ " <td>1.744300</td>\n",
1090
+ " </tr>\n",
1091
+ " <tr>\n",
1092
+ " <td>142</td>\n",
1093
+ " <td>1.728900</td>\n",
1094
+ " </tr>\n",
1095
+ " <tr>\n",
1096
+ " <td>143</td>\n",
1097
+ " <td>1.725100</td>\n",
1098
+ " </tr>\n",
1099
+ " <tr>\n",
1100
+ " <td>144</td>\n",
1101
+ " <td>1.708000</td>\n",
1102
+ " </tr>\n",
1103
+ " <tr>\n",
1104
+ " <td>145</td>\n",
1105
+ " <td>1.709000</td>\n",
1106
+ " </tr>\n",
1107
+ " <tr>\n",
1108
+ " <td>146</td>\n",
1109
+ " <td>1.704600</td>\n",
1110
+ " </tr>\n",
1111
+ " <tr>\n",
1112
+ " <td>147</td>\n",
1113
+ " <td>1.684600</td>\n",
1114
+ " </tr>\n",
1115
+ " <tr>\n",
1116
+ " <td>148</td>\n",
1117
+ " <td>1.676100</td>\n",
1118
+ " </tr>\n",
1119
+ " <tr>\n",
1120
+ " <td>149</td>\n",
1121
+ " <td>1.682800</td>\n",
1122
+ " </tr>\n",
1123
+ " <tr>\n",
1124
+ " <td>150</td>\n",
1125
+ " <td>1.669900</td>\n",
1126
+ " </tr>\n",
1127
+ " <tr>\n",
1128
+ " <td>151</td>\n",
1129
+ " <td>1.636400</td>\n",
1130
+ " </tr>\n",
1131
+ " <tr>\n",
1132
+ " <td>152</td>\n",
1133
+ " <td>1.671500</td>\n",
1134
+ " </tr>\n",
1135
+ " <tr>\n",
1136
+ " <td>153</td>\n",
1137
+ " <td>1.673200</td>\n",
1138
+ " </tr>\n",
1139
+ " <tr>\n",
1140
+ " <td>154</td>\n",
1141
+ " <td>1.644300</td>\n",
1142
+ " </tr>\n",
1143
+ " <tr>\n",
1144
+ " <td>155</td>\n",
1145
+ " <td>1.620800</td>\n",
1146
+ " </tr>\n",
1147
+ " <tr>\n",
1148
+ " <td>156</td>\n",
1149
+ " <td>1.617500</td>\n",
1150
+ " </tr>\n",
1151
+ " <tr>\n",
1152
+ " <td>157</td>\n",
1153
+ " <td>1.647700</td>\n",
1154
+ " </tr>\n",
1155
+ " <tr>\n",
1156
+ " <td>158</td>\n",
1157
+ " <td>1.629300</td>\n",
1158
+ " </tr>\n",
1159
+ " <tr>\n",
1160
+ " <td>159</td>\n",
1161
+ " <td>1.608800</td>\n",
1162
+ " </tr>\n",
1163
+ " <tr>\n",
1164
+ " <td>160</td>\n",
1165
+ " <td>1.633000</td>\n",
1166
+ " </tr>\n",
1167
+ " <tr>\n",
1168
+ " <td>161</td>\n",
1169
+ " <td>1.618200</td>\n",
1170
+ " </tr>\n",
1171
+ " <tr>\n",
1172
+ " <td>162</td>\n",
1173
+ " <td>1.634300</td>\n",
1174
+ " </tr>\n",
1175
+ " <tr>\n",
1176
+ " <td>163</td>\n",
1177
+ " <td>1.588400</td>\n",
1178
+ " </tr>\n",
1179
+ " <tr>\n",
1180
+ " <td>164</td>\n",
1181
+ " <td>1.581100</td>\n",
1182
+ " </tr>\n",
1183
+ " <tr>\n",
1184
+ " <td>165</td>\n",
1185
+ " <td>1.584500</td>\n",
1186
+ " </tr>\n",
1187
+ " <tr>\n",
1188
+ " <td>166</td>\n",
1189
+ " <td>1.594800</td>\n",
1190
+ " </tr>\n",
1191
+ " <tr>\n",
1192
+ " <td>167</td>\n",
1193
+ " <td>1.563800</td>\n",
1194
+ " </tr>\n",
1195
+ " <tr>\n",
1196
+ " <td>168</td>\n",
1197
+ " <td>1.576900</td>\n",
1198
+ " </tr>\n",
1199
+ " <tr>\n",
1200
+ " <td>169</td>\n",
1201
+ " <td>1.546300</td>\n",
1202
+ " </tr>\n",
1203
+ " <tr>\n",
1204
+ " <td>170</td>\n",
1205
+ " <td>1.569800</td>\n",
1206
+ " </tr>\n",
1207
+ " <tr>\n",
1208
+ " <td>171</td>\n",
1209
+ " <td>1.592300</td>\n",
1210
+ " </tr>\n",
1211
+ " <tr>\n",
1212
+ " <td>172</td>\n",
1213
+ " <td>1.537800</td>\n",
1214
+ " </tr>\n",
1215
+ " <tr>\n",
1216
+ " <td>173</td>\n",
1217
+ " <td>1.519200</td>\n",
1218
+ " </tr>\n",
1219
+ " <tr>\n",
1220
+ " <td>174</td>\n",
1221
+ " <td>1.512100</td>\n",
1222
+ " </tr>\n",
1223
+ " <tr>\n",
1224
+ " <td>175</td>\n",
1225
+ " <td>1.581500</td>\n",
1226
+ " </tr>\n",
1227
+ " <tr>\n",
1228
+ " <td>176</td>\n",
1229
+ " <td>1.534500</td>\n",
1230
+ " </tr>\n",
1231
+ " <tr>\n",
1232
+ " <td>177</td>\n",
1233
+ " <td>1.509400</td>\n",
1234
+ " </tr>\n",
1235
+ " <tr>\n",
1236
+ " <td>178</td>\n",
1237
+ " <td>1.521300</td>\n",
1238
+ " </tr>\n",
1239
+ " <tr>\n",
1240
+ " <td>179</td>\n",
1241
+ " <td>1.528500</td>\n",
1242
+ " </tr>\n",
1243
+ " <tr>\n",
1244
+ " <td>180</td>\n",
1245
+ " <td>1.494300</td>\n",
1246
+ " </tr>\n",
1247
+ " <tr>\n",
1248
+ " <td>181</td>\n",
1249
+ " <td>1.495000</td>\n",
1250
+ " </tr>\n",
1251
+ " <tr>\n",
1252
+ " <td>182</td>\n",
1253
+ " <td>1.499700</td>\n",
1254
+ " </tr>\n",
1255
+ " <tr>\n",
1256
+ " <td>183</td>\n",
1257
+ " <td>1.461300</td>\n",
1258
+ " </tr>\n",
1259
+ " <tr>\n",
1260
+ " <td>184</td>\n",
1261
+ " <td>1.469200</td>\n",
1262
+ " </tr>\n",
1263
+ " <tr>\n",
1264
+ " <td>185</td>\n",
1265
+ " <td>1.495200</td>\n",
1266
+ " </tr>\n",
1267
+ " <tr>\n",
1268
+ " <td>186</td>\n",
1269
+ " <td>1.467400</td>\n",
1270
+ " </tr>\n",
1271
+ " <tr>\n",
1272
+ " <td>187</td>\n",
1273
+ " <td>1.437000</td>\n",
1274
+ " </tr>\n",
1275
+ " <tr>\n",
1276
+ " <td>188</td>\n",
1277
+ " <td>1.463000</td>\n",
1278
+ " </tr>\n",
1279
+ " <tr>\n",
1280
+ " <td>189</td>\n",
1281
+ " <td>1.437900</td>\n",
1282
+ " </tr>\n",
1283
+ " <tr>\n",
1284
+ " <td>190</td>\n",
1285
+ " <td>1.467400</td>\n",
1286
+ " </tr>\n",
1287
+ " <tr>\n",
1288
+ " <td>191</td>\n",
1289
+ " <td>1.472300</td>\n",
1290
+ " </tr>\n",
1291
+ " <tr>\n",
1292
+ " <td>192</td>\n",
1293
+ " <td>1.434000</td>\n",
1294
+ " </tr>\n",
1295
+ " <tr>\n",
1296
+ " <td>193</td>\n",
1297
+ " <td>1.411500</td>\n",
1298
+ " </tr>\n",
1299
+ " <tr>\n",
1300
+ " <td>194</td>\n",
1301
+ " <td>1.432500</td>\n",
1302
+ " </tr>\n",
1303
+ " <tr>\n",
1304
+ " <td>195</td>\n",
1305
+ " <td>1.459800</td>\n",
1306
+ " </tr>\n",
1307
+ " <tr>\n",
1308
+ " <td>196</td>\n",
1309
+ " <td>1.431900</td>\n",
1310
+ " </tr>\n",
1311
+ " <tr>\n",
1312
+ " <td>197</td>\n",
1313
+ " <td>1.456200</td>\n",
1314
+ " </tr>\n",
1315
+ " <tr>\n",
1316
+ " <td>198</td>\n",
1317
+ " <td>1.394800</td>\n",
1318
+ " </tr>\n",
1319
+ " <tr>\n",
1320
+ " <td>199</td>\n",
1321
+ " <td>1.422700</td>\n",
1322
+ " </tr>\n",
1323
+ " <tr>\n",
1324
+ " <td>200</td>\n",
1325
+ " <td>1.412800</td>\n",
1326
+ " </tr>\n",
1327
+ " <tr>\n",
1328
+ " <td>201</td>\n",
1329
+ " <td>1.413800</td>\n",
1330
+ " </tr>\n",
1331
+ " <tr>\n",
1332
+ " <td>202</td>\n",
1333
+ " <td>1.380000</td>\n",
1334
+ " </tr>\n",
1335
+ " <tr>\n",
1336
+ " <td>203</td>\n",
1337
+ " <td>1.407400</td>\n",
1338
+ " </tr>\n",
1339
+ " <tr>\n",
1340
+ " <td>204</td>\n",
1341
+ " <td>1.406200</td>\n",
1342
+ " </tr>\n",
1343
+ " <tr>\n",
1344
+ " <td>205</td>\n",
1345
+ " <td>1.396100</td>\n",
1346
+ " </tr>\n",
1347
+ " <tr>\n",
1348
+ " <td>206</td>\n",
1349
+ " <td>1.407100</td>\n",
1350
+ " </tr>\n",
1351
+ " <tr>\n",
1352
+ " <td>207</td>\n",
1353
+ " <td>1.379600</td>\n",
1354
+ " </tr>\n",
1355
+ " <tr>\n",
1356
+ " <td>208</td>\n",
1357
+ " <td>1.360600</td>\n",
1358
+ " </tr>\n",
1359
+ " <tr>\n",
1360
+ " <td>209</td>\n",
1361
+ " <td>1.395100</td>\n",
1362
+ " </tr>\n",
1363
+ " <tr>\n",
1364
+ " <td>210</td>\n",
1365
+ " <td>1.352500</td>\n",
1366
+ " </tr>\n",
1367
+ " <tr>\n",
1368
+ " <td>211</td>\n",
1369
+ " <td>1.358900</td>\n",
1370
+ " </tr>\n",
1371
+ " <tr>\n",
1372
+ " <td>212</td>\n",
1373
+ " <td>1.369100</td>\n",
1374
+ " </tr>\n",
1375
+ " <tr>\n",
1376
+ " <td>213</td>\n",
1377
+ " <td>1.342600</td>\n",
1378
+ " </tr>\n",
1379
+ " <tr>\n",
1380
+ " <td>214</td>\n",
1381
+ " <td>1.358900</td>\n",
1382
+ " </tr>\n",
1383
+ " <tr>\n",
1384
+ " <td>215</td>\n",
1385
+ " <td>1.320300</td>\n",
1386
+ " </tr>\n",
1387
+ " <tr>\n",
1388
+ " <td>216</td>\n",
1389
+ " <td>1.355700</td>\n",
1390
+ " </tr>\n",
1391
+ " <tr>\n",
1392
+ " <td>217</td>\n",
1393
+ " <td>1.315700</td>\n",
1394
+ " </tr>\n",
1395
+ " <tr>\n",
1396
+ " <td>218</td>\n",
1397
+ " <td>1.348800</td>\n",
1398
+ " </tr>\n",
1399
+ " <tr>\n",
1400
+ " <td>219</td>\n",
1401
+ " <td>1.319800</td>\n",
1402
+ " </tr>\n",
1403
+ " <tr>\n",
1404
+ " <td>220</td>\n",
1405
+ " <td>1.336500</td>\n",
1406
+ " </tr>\n",
1407
+ " <tr>\n",
1408
+ " <td>221</td>\n",
1409
+ " <td>1.339600</td>\n",
1410
+ " </tr>\n",
1411
+ " <tr>\n",
1412
+ " <td>222</td>\n",
1413
+ " <td>1.319500</td>\n",
1414
+ " </tr>\n",
1415
+ " <tr>\n",
1416
+ " <td>223</td>\n",
1417
+ " <td>1.319600</td>\n",
1418
+ " </tr>\n",
1419
+ " <tr>\n",
1420
+ " <td>224</td>\n",
1421
+ " <td>1.330200</td>\n",
1422
+ " </tr>\n",
1423
+ " <tr>\n",
1424
+ " <td>225</td>\n",
1425
+ " <td>1.271700</td>\n",
1426
+ " </tr>\n",
1427
+ " <tr>\n",
1428
+ " <td>226</td>\n",
1429
+ " <td>1.317300</td>\n",
1430
+ " </tr>\n",
1431
+ " <tr>\n",
1432
+ " <td>227</td>\n",
1433
+ " <td>1.287400</td>\n",
1434
+ " </tr>\n",
1435
+ " <tr>\n",
1436
+ " <td>228</td>\n",
1437
+ " <td>1.283300</td>\n",
1438
+ " </tr>\n",
1439
+ " <tr>\n",
1440
+ " <td>229</td>\n",
1441
+ " <td>1.280500</td>\n",
1442
+ " </tr>\n",
1443
+ " <tr>\n",
1444
+ " <td>230</td>\n",
1445
+ " <td>1.274200</td>\n",
1446
+ " </tr>\n",
1447
+ " <tr>\n",
1448
+ " <td>231</td>\n",
1449
+ " <td>1.297000</td>\n",
1450
+ " </tr>\n",
1451
+ " <tr>\n",
1452
+ " <td>232</td>\n",
1453
+ " <td>1.266400</td>\n",
1454
+ " </tr>\n",
1455
+ " <tr>\n",
1456
+ " <td>233</td>\n",
1457
+ " <td>1.253100</td>\n",
1458
+ " </tr>\n",
1459
+ " <tr>\n",
1460
+ " <td>234</td>\n",
1461
+ " <td>1.273100</td>\n",
1462
+ " </tr>\n",
1463
+ " <tr>\n",
1464
+ " <td>235</td>\n",
1465
+ " <td>1.293300</td>\n",
1466
+ " </tr>\n",
1467
+ " <tr>\n",
1468
+ " <td>236</td>\n",
1469
+ " <td>1.293000</td>\n",
1470
+ " </tr>\n",
1471
+ " <tr>\n",
1472
+ " <td>237</td>\n",
1473
+ " <td>1.273500</td>\n",
1474
+ " </tr>\n",
1475
+ " <tr>\n",
1476
+ " <td>238</td>\n",
1477
+ " <td>1.253100</td>\n",
1478
+ " </tr>\n",
1479
+ " <tr>\n",
1480
+ " <td>239</td>\n",
1481
+ " <td>1.257700</td>\n",
1482
+ " </tr>\n",
1483
+ " <tr>\n",
1484
+ " <td>240</td>\n",
1485
+ " <td>1.232500</td>\n",
1486
+ " </tr>\n",
1487
+ " <tr>\n",
1488
+ " <td>241</td>\n",
1489
+ " <td>1.233100</td>\n",
1490
+ " </tr>\n",
1491
+ " <tr>\n",
1492
+ " <td>242</td>\n",
1493
+ " <td>1.226000</td>\n",
1494
+ " </tr>\n",
1495
+ " <tr>\n",
1496
+ " <td>243</td>\n",
1497
+ " <td>1.218400</td>\n",
1498
+ " </tr>\n",
1499
+ " <tr>\n",
1500
+ " <td>244</td>\n",
1501
+ " <td>1.222800</td>\n",
1502
+ " </tr>\n",
1503
+ " <tr>\n",
1504
+ " <td>245</td>\n",
1505
+ " <td>1.232100</td>\n",
1506
+ " </tr>\n",
1507
+ " <tr>\n",
1508
+ " <td>246</td>\n",
1509
+ " <td>1.214800</td>\n",
1510
+ " </tr>\n",
1511
+ " <tr>\n",
1512
+ " <td>247</td>\n",
1513
+ " <td>1.205700</td>\n",
1514
+ " </tr>\n",
1515
+ " <tr>\n",
1516
+ " <td>248</td>\n",
1517
+ " <td>1.228400</td>\n",
1518
+ " </tr>\n",
1519
+ " <tr>\n",
1520
+ " <td>249</td>\n",
1521
+ " <td>1.202600</td>\n",
1522
+ " </tr>\n",
1523
+ " <tr>\n",
1524
+ " <td>250</td>\n",
1525
+ " <td>1.207700</td>\n",
1526
+ " </tr>\n",
1527
+ " <tr>\n",
1528
+ " <td>251</td>\n",
1529
+ " <td>1.205800</td>\n",
1530
+ " </tr>\n",
1531
+ " <tr>\n",
1532
+ " <td>252</td>\n",
1533
+ " <td>1.198400</td>\n",
1534
+ " </tr>\n",
1535
+ " <tr>\n",
1536
+ " <td>253</td>\n",
1537
+ " <td>1.207800</td>\n",
1538
+ " </tr>\n",
1539
+ " <tr>\n",
1540
+ " <td>254</td>\n",
1541
+ " <td>1.198600</td>\n",
1542
+ " </tr>\n",
1543
+ " <tr>\n",
1544
+ " <td>255</td>\n",
1545
+ " <td>1.201700</td>\n",
1546
+ " </tr>\n",
1547
+ " <tr>\n",
1548
+ " <td>256</td>\n",
1549
+ " <td>1.195500</td>\n",
1550
+ " </tr>\n",
1551
+ " <tr>\n",
1552
+ " <td>257</td>\n",
1553
+ " <td>1.190500</td>\n",
1554
+ " </tr>\n",
1555
+ " <tr>\n",
1556
+ " <td>258</td>\n",
1557
+ " <td>1.197100</td>\n",
1558
+ " </tr>\n",
1559
+ " <tr>\n",
1560
+ " <td>259</td>\n",
1561
+ " <td>1.165100</td>\n",
1562
+ " </tr>\n",
1563
+ " <tr>\n",
1564
+ " <td>260</td>\n",
1565
+ " <td>1.173200</td>\n",
1566
+ " </tr>\n",
1567
+ " <tr>\n",
1568
+ " <td>261</td>\n",
1569
+ " <td>1.163400</td>\n",
1570
+ " </tr>\n",
1571
+ " <tr>\n",
1572
+ " <td>262</td>\n",
1573
+ " <td>1.191500</td>\n",
1574
+ " </tr>\n",
1575
+ " <tr>\n",
1576
+ " <td>263</td>\n",
1577
+ " <td>1.173700</td>\n",
1578
+ " </tr>\n",
1579
+ " <tr>\n",
1580
+ " <td>264</td>\n",
1581
+ " <td>1.134400</td>\n",
1582
+ " </tr>\n",
1583
+ " <tr>\n",
1584
+ " <td>265</td>\n",
1585
+ " <td>1.165500</td>\n",
1586
+ " </tr>\n",
1587
+ " <tr>\n",
1588
+ " <td>266</td>\n",
1589
+ " <td>1.134800</td>\n",
1590
+ " </tr>\n",
1591
+ " <tr>\n",
1592
+ " <td>267</td>\n",
1593
+ " <td>1.149500</td>\n",
1594
+ " </tr>\n",
1595
+ " <tr>\n",
1596
+ " <td>268</td>\n",
1597
+ " <td>1.173100</td>\n",
1598
+ " </tr>\n",
1599
+ " <tr>\n",
1600
+ " <td>269</td>\n",
1601
+ " <td>1.137000</td>\n",
1602
+ " </tr>\n",
1603
+ " <tr>\n",
1604
+ " <td>270</td>\n",
1605
+ " <td>1.171200</td>\n",
1606
+ " </tr>\n",
1607
+ " <tr>\n",
1608
+ " <td>271</td>\n",
1609
+ " <td>1.120600</td>\n",
1610
+ " </tr>\n",
1611
+ " <tr>\n",
1612
+ " <td>272</td>\n",
1613
+ " <td>1.147600</td>\n",
1614
+ " </tr>\n",
1615
+ " <tr>\n",
1616
+ " <td>273</td>\n",
1617
+ " <td>1.128300</td>\n",
1618
+ " </tr>\n",
1619
+ " <tr>\n",
1620
+ " <td>274</td>\n",
1621
+ " <td>1.150300</td>\n",
1622
+ " </tr>\n",
1623
+ " <tr>\n",
1624
+ " <td>275</td>\n",
1625
+ " <td>1.147700</td>\n",
1626
+ " </tr>\n",
1627
+ " <tr>\n",
1628
+ " <td>276</td>\n",
1629
+ " <td>1.150200</td>\n",
1630
+ " </tr>\n",
1631
+ " <tr>\n",
1632
+ " <td>277</td>\n",
1633
+ " <td>1.106900</td>\n",
1634
+ " </tr>\n",
1635
+ " <tr>\n",
1636
+ " <td>278</td>\n",
1637
+ " <td>1.145400</td>\n",
1638
+ " </tr>\n",
1639
+ " <tr>\n",
1640
+ " <td>279</td>\n",
1641
+ " <td>1.117300</td>\n",
1642
+ " </tr>\n",
1643
+ " <tr>\n",
1644
+ " <td>280</td>\n",
1645
+ " <td>1.121900</td>\n",
1646
+ " </tr>\n",
1647
+ " <tr>\n",
1648
+ " <td>281</td>\n",
1649
+ " <td>1.139400</td>\n",
1650
+ " </tr>\n",
1651
+ " <tr>\n",
1652
+ " <td>282</td>\n",
1653
+ " <td>1.109100</td>\n",
1654
+ " </tr>\n",
1655
+ " <tr>\n",
1656
+ " <td>283</td>\n",
1657
+ " <td>1.142100</td>\n",
1658
+ " </tr>\n",
1659
+ " <tr>\n",
1660
+ " <td>284</td>\n",
1661
+ " <td>1.117300</td>\n",
1662
+ " </tr>\n",
1663
+ " <tr>\n",
1664
+ " <td>285</td>\n",
1665
+ " <td>1.104200</td>\n",
1666
+ " </tr>\n",
1667
+ " <tr>\n",
1668
+ " <td>286</td>\n",
1669
+ " <td>1.134200</td>\n",
1670
+ " </tr>\n",
1671
+ " <tr>\n",
1672
+ " <td>287</td>\n",
1673
+ " <td>1.100400</td>\n",
1674
+ " </tr>\n",
1675
+ " <tr>\n",
1676
+ " <td>288</td>\n",
1677
+ " <td>1.092100</td>\n",
1678
+ " </tr>\n",
1679
+ " <tr>\n",
1680
+ " <td>289</td>\n",
1681
+ " <td>1.120500</td>\n",
1682
+ " </tr>\n",
1683
+ " <tr>\n",
1684
+ " <td>290</td>\n",
1685
+ " <td>1.088100</td>\n",
1686
+ " </tr>\n",
1687
+ " <tr>\n",
1688
+ " <td>291</td>\n",
1689
+ " <td>1.128600</td>\n",
1690
+ " </tr>\n",
1691
+ " <tr>\n",
1692
+ " <td>292</td>\n",
1693
+ " <td>1.105400</td>\n",
1694
+ " </tr>\n",
1695
+ " <tr>\n",
1696
+ " <td>293</td>\n",
1697
+ " <td>1.094000</td>\n",
1698
+ " </tr>\n",
1699
+ " <tr>\n",
1700
+ " <td>294</td>\n",
1701
+ " <td>1.108900</td>\n",
1702
+ " </tr>\n",
1703
+ " <tr>\n",
1704
+ " <td>295</td>\n",
1705
+ " <td>1.073100</td>\n",
1706
+ " </tr>\n",
1707
+ " <tr>\n",
1708
+ " <td>296</td>\n",
1709
+ " <td>1.100900</td>\n",
1710
+ " </tr>\n",
1711
+ " <tr>\n",
1712
+ " <td>297</td>\n",
1713
+ " <td>1.092400</td>\n",
1714
+ " </tr>\n",
1715
+ " <tr>\n",
1716
+ " <td>298</td>\n",
1717
+ " <td>1.090300</td>\n",
1718
+ " </tr>\n",
1719
+ " <tr>\n",
1720
+ " <td>299</td>\n",
1721
+ " <td>1.079400</td>\n",
1722
+ " </tr>\n",
1723
+ " <tr>\n",
1724
+ " <td>300</td>\n",
1725
+ " <td>1.090300</td>\n",
1726
+ " </tr>\n",
1727
+ " <tr>\n",
1728
+ " <td>301</td>\n",
1729
+ " <td>1.086100</td>\n",
1730
+ " </tr>\n",
1731
+ " <tr>\n",
1732
+ " <td>302</td>\n",
1733
+ " <td>1.080300</td>\n",
1734
+ " </tr>\n",
1735
+ " <tr>\n",
1736
+ " <td>303</td>\n",
1737
+ " <td>1.075600</td>\n",
1738
+ " </tr>\n",
1739
+ " <tr>\n",
1740
+ " <td>304</td>\n",
1741
+ " <td>1.075900</td>\n",
1742
+ " </tr>\n",
1743
+ " <tr>\n",
1744
+ " <td>305</td>\n",
1745
+ " <td>1.092200</td>\n",
1746
+ " </tr>\n",
1747
+ " <tr>\n",
1748
+ " <td>306</td>\n",
1749
+ " <td>1.070600</td>\n",
1750
+ " </tr>\n",
1751
+ " <tr>\n",
1752
+ " <td>307</td>\n",
1753
+ " <td>1.068800</td>\n",
1754
+ " </tr>\n",
1755
+ " <tr>\n",
1756
+ " <td>308</td>\n",
1757
+ " <td>1.071300</td>\n",
1758
+ " </tr>\n",
1759
+ " <tr>\n",
1760
+ " <td>309</td>\n",
1761
+ " <td>1.073900</td>\n",
1762
+ " </tr>\n",
1763
+ " <tr>\n",
1764
+ " <td>310</td>\n",
1765
+ " <td>1.055400</td>\n",
1766
+ " </tr>\n",
1767
+ " <tr>\n",
1768
+ " <td>311</td>\n",
1769
+ " <td>1.067900</td>\n",
1770
+ " </tr>\n",
1771
+ " <tr>\n",
1772
+ " <td>312</td>\n",
1773
+ " <td>1.041000</td>\n",
1774
+ " </tr>\n",
1775
+ " <tr>\n",
1776
+ " <td>313</td>\n",
1777
+ " <td>1.048600</td>\n",
1778
+ " </tr>\n",
1779
+ " <tr>\n",
1780
+ " <td>314</td>\n",
1781
+ " <td>1.072600</td>\n",
1782
+ " </tr>\n",
1783
+ " <tr>\n",
1784
+ " <td>315</td>\n",
1785
+ " <td>1.058800</td>\n",
1786
+ " </tr>\n",
1787
+ " <tr>\n",
1788
+ " <td>316</td>\n",
1789
+ " <td>1.039000</td>\n",
1790
+ " </tr>\n",
1791
+ " <tr>\n",
1792
+ " <td>317</td>\n",
1793
+ " <td>1.072300</td>\n",
1794
+ " </tr>\n",
1795
+ " <tr>\n",
1796
+ " <td>318</td>\n",
1797
+ " <td>1.056600</td>\n",
1798
+ " </tr>\n",
1799
+ " <tr>\n",
1800
+ " <td>319</td>\n",
1801
+ " <td>1.035100</td>\n",
1802
+ " </tr>\n",
1803
+ " <tr>\n",
1804
+ " <td>320</td>\n",
1805
+ " <td>1.052800</td>\n",
1806
+ " </tr>\n",
1807
+ " <tr>\n",
1808
+ " <td>321</td>\n",
1809
+ " <td>1.046700</td>\n",
1810
+ " </tr>\n",
1811
+ " <tr>\n",
1812
+ " <td>322</td>\n",
1813
+ " <td>1.073400</td>\n",
1814
+ " </tr>\n",
1815
+ " <tr>\n",
1816
+ " <td>323</td>\n",
1817
+ " <td>1.054000</td>\n",
1818
+ " </tr>\n",
1819
+ " <tr>\n",
1820
+ " <td>324</td>\n",
1821
+ " <td>1.077100</td>\n",
1822
+ " </tr>\n",
1823
+ " <tr>\n",
1824
+ " <td>325</td>\n",
1825
+ " <td>1.035200</td>\n",
1826
+ " </tr>\n",
1827
+ " <tr>\n",
1828
+ " <td>326</td>\n",
1829
+ " <td>1.027700</td>\n",
1830
+ " </tr>\n",
1831
+ " <tr>\n",
1832
+ " <td>327</td>\n",
1833
+ " <td>1.060000</td>\n",
1834
+ " </tr>\n",
1835
+ " <tr>\n",
1836
+ " <td>328</td>\n",
1837
+ " <td>1.048900</td>\n",
1838
+ " </tr>\n",
1839
+ " <tr>\n",
1840
+ " <td>329</td>\n",
1841
+ " <td>1.040000</td>\n",
1842
+ " </tr>\n",
1843
+ " <tr>\n",
1844
+ " <td>330</td>\n",
1845
+ " <td>1.026900</td>\n",
1846
+ " </tr>\n",
1847
+ " <tr>\n",
1848
+ " <td>331</td>\n",
1849
+ " <td>1.049300</td>\n",
1850
+ " </tr>\n",
1851
+ " <tr>\n",
1852
+ " <td>332</td>\n",
1853
+ " <td>1.017100</td>\n",
1854
+ " </tr>\n",
1855
+ " <tr>\n",
1856
+ " <td>333</td>\n",
1857
+ " <td>0.996200</td>\n",
1858
+ " </tr>\n",
1859
+ " <tr>\n",
1860
+ " <td>334</td>\n",
1861
+ " <td>1.006400</td>\n",
1862
+ " </tr>\n",
1863
+ " <tr>\n",
1864
+ " <td>335</td>\n",
1865
+ " <td>1.026700</td>\n",
1866
+ " </tr>\n",
1867
+ " <tr>\n",
1868
+ " <td>336</td>\n",
1869
+ " <td>1.073700</td>\n",
1870
+ " </tr>\n",
1871
+ " <tr>\n",
1872
+ " <td>337</td>\n",
1873
+ " <td>1.039200</td>\n",
1874
+ " </tr>\n",
1875
+ " <tr>\n",
1876
+ " <td>338</td>\n",
1877
+ " <td>1.041100</td>\n",
1878
+ " </tr>\n",
1879
+ " <tr>\n",
1880
+ " <td>339</td>\n",
1881
+ " <td>1.054300</td>\n",
1882
+ " </tr>\n",
1883
+ " <tr>\n",
1884
+ " <td>340</td>\n",
1885
+ " <td>1.013500</td>\n",
1886
+ " </tr>\n",
1887
+ " <tr>\n",
1888
+ " <td>341</td>\n",
1889
+ " <td>1.024900</td>\n",
1890
+ " </tr>\n",
1891
+ " <tr>\n",
1892
+ " <td>342</td>\n",
1893
+ " <td>1.003300</td>\n",
1894
+ " </tr>\n",
1895
+ " <tr>\n",
1896
+ " <td>343</td>\n",
1897
+ " <td>0.993400</td>\n",
1898
+ " </tr>\n",
1899
+ " <tr>\n",
1900
+ " <td>344</td>\n",
1901
+ " <td>1.037300</td>\n",
1902
+ " </tr>\n",
1903
+ " <tr>\n",
1904
+ " <td>345</td>\n",
1905
+ " <td>1.009300</td>\n",
1906
+ " </tr>\n",
1907
+ " <tr>\n",
1908
+ " <td>346</td>\n",
1909
+ " <td>1.030400</td>\n",
1910
+ " </tr>\n",
1911
+ " <tr>\n",
1912
+ " <td>347</td>\n",
1913
+ " <td>1.001400</td>\n",
1914
+ " </tr>\n",
1915
+ " <tr>\n",
1916
+ " <td>348</td>\n",
1917
+ " <td>1.012100</td>\n",
1918
+ " </tr>\n",
1919
+ " <tr>\n",
1920
+ " <td>349</td>\n",
1921
+ " <td>1.027300</td>\n",
1922
+ " </tr>\n",
1923
+ " <tr>\n",
1924
+ " <td>350</td>\n",
1925
+ " <td>1.012700</td>\n",
1926
+ " </tr>\n",
1927
+ " <tr>\n",
1928
+ " <td>351</td>\n",
1929
+ " <td>1.013400</td>\n",
1930
+ " </tr>\n",
1931
+ " <tr>\n",
1932
+ " <td>352</td>\n",
1933
+ " <td>1.004400</td>\n",
1934
+ " </tr>\n",
1935
+ " <tr>\n",
1936
+ " <td>353</td>\n",
1937
+ " <td>1.024800</td>\n",
1938
+ " </tr>\n",
1939
+ " <tr>\n",
1940
+ " <td>354</td>\n",
1941
+ " <td>0.990700</td>\n",
1942
+ " </tr>\n",
1943
+ " <tr>\n",
1944
+ " <td>355</td>\n",
1945
+ " <td>1.048600</td>\n",
1946
+ " </tr>\n",
1947
+ " <tr>\n",
1948
+ " <td>356</td>\n",
1949
+ " <td>0.992700</td>\n",
1950
+ " </tr>\n",
1951
+ " <tr>\n",
1952
+ " <td>357</td>\n",
1953
+ " <td>0.991800</td>\n",
1954
+ " </tr>\n",
1955
+ " <tr>\n",
1956
+ " <td>358</td>\n",
1957
+ " <td>0.985300</td>\n",
1958
+ " </tr>\n",
1959
+ " <tr>\n",
1960
+ " <td>359</td>\n",
1961
+ " <td>1.019100</td>\n",
1962
+ " </tr>\n",
1963
+ " <tr>\n",
1964
+ " <td>360</td>\n",
1965
+ " <td>1.007300</td>\n",
1966
+ " </tr>\n",
1967
+ " <tr>\n",
1968
+ " <td>361</td>\n",
1969
+ " <td>1.025500</td>\n",
1970
+ " </tr>\n",
1971
+ " <tr>\n",
1972
+ " <td>362</td>\n",
1973
+ " <td>0.999100</td>\n",
1974
+ " </tr>\n",
1975
+ " <tr>\n",
1976
+ " <td>363</td>\n",
1977
+ " <td>0.997900</td>\n",
1978
+ " </tr>\n",
1979
+ " <tr>\n",
1980
+ " <td>364</td>\n",
1981
+ " <td>1.013300</td>\n",
1982
+ " </tr>\n",
1983
+ " <tr>\n",
1984
+ " <td>365</td>\n",
1985
+ " <td>1.014700</td>\n",
1986
+ " </tr>\n",
1987
+ " <tr>\n",
1988
+ " <td>366</td>\n",
1989
+ " <td>1.037700</td>\n",
1990
+ " </tr>\n",
1991
+ " <tr>\n",
1992
+ " <td>367</td>\n",
1993
+ " <td>0.992400</td>\n",
1994
+ " </tr>\n",
1995
+ " <tr>\n",
1996
+ " <td>368</td>\n",
1997
+ " <td>0.988800</td>\n",
1998
+ " </tr>\n",
1999
+ " <tr>\n",
2000
+ " <td>369</td>\n",
2001
+ " <td>0.993900</td>\n",
2002
+ " </tr>\n",
2003
+ " <tr>\n",
2004
+ " <td>370</td>\n",
2005
+ " <td>0.999500</td>\n",
2006
+ " </tr>\n",
2007
+ " <tr>\n",
2008
+ " <td>371</td>\n",
2009
+ " <td>0.973000</td>\n",
2010
+ " </tr>\n",
2011
+ " <tr>\n",
2012
+ " <td>372</td>\n",
2013
+ " <td>0.972200</td>\n",
2014
+ " </tr>\n",
2015
+ " <tr>\n",
2016
+ " <td>373</td>\n",
2017
+ " <td>0.989200</td>\n",
2018
+ " </tr>\n",
2019
+ " <tr>\n",
2020
+ " <td>374</td>\n",
2021
+ " <td>0.994500</td>\n",
2022
+ " </tr>\n",
2023
+ " <tr>\n",
2024
+ " <td>375</td>\n",
2025
+ " <td>0.995800</td>\n",
2026
+ " </tr>\n",
2027
+ " <tr>\n",
2028
+ " <td>376</td>\n",
2029
+ " <td>0.992000</td>\n",
2030
+ " </tr>\n",
2031
+ " <tr>\n",
2032
+ " <td>377</td>\n",
2033
+ " <td>0.977800</td>\n",
2034
+ " </tr>\n",
2035
+ " <tr>\n",
2036
+ " <td>378</td>\n",
2037
+ " <td>0.975700</td>\n",
2038
+ " </tr>\n",
2039
+ " <tr>\n",
2040
+ " <td>379</td>\n",
2041
+ " <td>0.973700</td>\n",
2042
+ " </tr>\n",
2043
+ " <tr>\n",
2044
+ " <td>380</td>\n",
2045
+ " <td>0.986200</td>\n",
2046
+ " </tr>\n",
2047
+ " <tr>\n",
2048
+ " <td>381</td>\n",
2049
+ " <td>1.008000</td>\n",
2050
+ " </tr>\n",
2051
+ " <tr>\n",
2052
+ " <td>382</td>\n",
2053
+ " <td>0.954100</td>\n",
2054
+ " </tr>\n",
2055
+ " <tr>\n",
2056
+ " <td>383</td>\n",
2057
+ " <td>1.015900</td>\n",
2058
+ " </tr>\n",
2059
+ " <tr>\n",
2060
+ " <td>384</td>\n",
2061
+ " <td>1.008200</td>\n",
2062
+ " </tr>\n",
2063
+ " <tr>\n",
2064
+ " <td>385</td>\n",
2065
+ " <td>0.974700</td>\n",
2066
+ " </tr>\n",
2067
+ " <tr>\n",
2068
+ " <td>386</td>\n",
2069
+ " <td>0.987500</td>\n",
2070
+ " </tr>\n",
2071
+ " <tr>\n",
2072
+ " <td>387</td>\n",
2073
+ " <td>0.993700</td>\n",
2074
+ " </tr>\n",
2075
+ " <tr>\n",
2076
+ " <td>388</td>\n",
2077
+ " <td>0.999200</td>\n",
2078
+ " </tr>\n",
2079
+ " <tr>\n",
2080
+ " <td>389</td>\n",
2081
+ " <td>1.000700</td>\n",
2082
+ " </tr>\n",
2083
+ " <tr>\n",
2084
+ " <td>390</td>\n",
2085
+ " <td>0.978600</td>\n",
2086
+ " </tr>\n",
2087
+ " <tr>\n",
2088
+ " <td>391</td>\n",
2089
+ " <td>0.956200</td>\n",
2090
+ " </tr>\n",
2091
+ " <tr>\n",
2092
+ " <td>392</td>\n",
2093
+ " <td>1.001600</td>\n",
2094
+ " </tr>\n",
2095
+ " <tr>\n",
2096
+ " <td>393</td>\n",
2097
+ " <td>0.971300</td>\n",
2098
+ " </tr>\n",
2099
+ " <tr>\n",
2100
+ " <td>394</td>\n",
2101
+ " <td>0.965800</td>\n",
2102
+ " </tr>\n",
2103
+ " <tr>\n",
2104
+ " <td>395</td>\n",
2105
+ " <td>0.981000</td>\n",
2106
+ " </tr>\n",
2107
+ " <tr>\n",
2108
+ " <td>396</td>\n",
2109
+ " <td>0.965400</td>\n",
2110
+ " </tr>\n",
2111
+ " <tr>\n",
2112
+ " <td>397</td>\n",
2113
+ " <td>0.974200</td>\n",
2114
+ " </tr>\n",
2115
+ " <tr>\n",
2116
+ " <td>398</td>\n",
2117
+ " <td>0.970700</td>\n",
2118
+ " </tr>\n",
2119
+ " <tr>\n",
2120
+ " <td>399</td>\n",
2121
+ " <td>0.953500</td>\n",
2122
+ " </tr>\n",
2123
+ " <tr>\n",
2124
+ " <td>400</td>\n",
2125
+ " <td>0.979700</td>\n",
2126
+ " </tr>\n",
2127
+ " <tr>\n",
2128
+ " <td>401</td>\n",
2129
+ " <td>0.957700</td>\n",
2130
+ " </tr>\n",
2131
+ " <tr>\n",
2132
+ " <td>402</td>\n",
2133
+ " <td>0.984600</td>\n",
2134
+ " </tr>\n",
2135
+ " <tr>\n",
2136
+ " <td>403</td>\n",
2137
+ " <td>1.015600</td>\n",
2138
+ " </tr>\n",
2139
+ " <tr>\n",
2140
+ " <td>404</td>\n",
2141
+ " <td>0.976800</td>\n",
2142
+ " </tr>\n",
2143
+ " <tr>\n",
2144
+ " <td>405</td>\n",
2145
+ " <td>0.969100</td>\n",
2146
+ " </tr>\n",
2147
+ " <tr>\n",
2148
+ " <td>406</td>\n",
2149
+ " <td>0.974200</td>\n",
2150
+ " </tr>\n",
2151
+ " <tr>\n",
2152
+ " <td>407</td>\n",
2153
+ " <td>0.983300</td>\n",
2154
+ " </tr>\n",
2155
+ " <tr>\n",
2156
+ " <td>408</td>\n",
2157
+ " <td>0.974300</td>\n",
2158
+ " </tr>\n",
2159
+ " <tr>\n",
2160
+ " <td>409</td>\n",
2161
+ " <td>0.980600</td>\n",
2162
+ " </tr>\n",
2163
+ " <tr>\n",
2164
+ " <td>410</td>\n",
2165
+ " <td>0.986300</td>\n",
2166
+ " </tr>\n",
2167
+ " <tr>\n",
2168
+ " <td>411</td>\n",
2169
+ " <td>0.968100</td>\n",
2170
+ " </tr>\n",
2171
+ " <tr>\n",
2172
+ " <td>412</td>\n",
2173
+ " <td>0.980500</td>\n",
2174
+ " </tr>\n",
2175
+ " <tr>\n",
2176
+ " <td>413</td>\n",
2177
+ " <td>0.976200</td>\n",
2178
+ " </tr>\n",
2179
+ " <tr>\n",
2180
+ " <td>414</td>\n",
2181
+ " <td>0.987300</td>\n",
2182
+ " </tr>\n",
2183
+ " <tr>\n",
2184
+ " <td>415</td>\n",
2185
+ " <td>0.971600</td>\n",
2186
+ " </tr>\n",
2187
+ " <tr>\n",
2188
+ " <td>416</td>\n",
2189
+ " <td>0.985200</td>\n",
2190
+ " </tr>\n",
2191
+ " <tr>\n",
2192
+ " <td>417</td>\n",
2193
+ " <td>0.989800</td>\n",
2194
+ " </tr>\n",
2195
+ " <tr>\n",
2196
+ " <td>418</td>\n",
2197
+ " <td>0.972000</td>\n",
2198
+ " </tr>\n",
2199
+ " <tr>\n",
2200
+ " <td>419</td>\n",
2201
+ " <td>0.971100</td>\n",
2202
+ " </tr>\n",
2203
+ " <tr>\n",
2204
+ " <td>420</td>\n",
2205
+ " <td>0.988800</td>\n",
2206
+ " </tr>\n",
2207
+ " <tr>\n",
2208
+ " <td>421</td>\n",
2209
+ " <td>0.965600</td>\n",
2210
+ " </tr>\n",
2211
+ " <tr>\n",
2212
+ " <td>422</td>\n",
2213
+ " <td>1.020400</td>\n",
2214
+ " </tr>\n",
2215
+ " <tr>\n",
2216
+ " <td>423</td>\n",
2217
+ " <td>0.978000</td>\n",
2218
+ " </tr>\n",
2219
+ " <tr>\n",
2220
+ " <td>424</td>\n",
2221
+ " <td>0.987800</td>\n",
2222
+ " </tr>\n",
2223
+ " <tr>\n",
2224
+ " <td>425</td>\n",
2225
+ " <td>0.953700</td>\n",
2226
+ " </tr>\n",
2227
+ " <tr>\n",
2228
+ " <td>426</td>\n",
2229
+ " <td>0.990400</td>\n",
2230
+ " </tr>\n",
2231
+ " <tr>\n",
2232
+ " <td>427</td>\n",
2233
+ " <td>0.982900</td>\n",
2234
+ " </tr>\n",
2235
+ " <tr>\n",
2236
+ " <td>428</td>\n",
2237
+ " <td>0.989100</td>\n",
2238
+ " </tr>\n",
2239
+ " <tr>\n",
2240
+ " <td>429</td>\n",
2241
+ " <td>0.983800</td>\n",
2242
+ " </tr>\n",
2243
+ " <tr>\n",
2244
+ " <td>430</td>\n",
2245
+ " <td>0.981500</td>\n",
2246
+ " </tr>\n",
2247
+ " <tr>\n",
2248
+ " <td>431</td>\n",
2249
+ " <td>0.966900</td>\n",
2250
+ " </tr>\n",
2251
+ " <tr>\n",
2252
+ " <td>432</td>\n",
2253
+ " <td>0.967300</td>\n",
2254
+ " </tr>\n",
2255
+ " <tr>\n",
2256
+ " <td>433</td>\n",
2257
+ " <td>0.999400</td>\n",
2258
+ " </tr>\n",
2259
+ " <tr>\n",
2260
+ " <td>434</td>\n",
2261
+ " <td>0.973100</td>\n",
2262
+ " </tr>\n",
2263
+ " <tr>\n",
2264
+ " <td>435</td>\n",
2265
+ " <td>0.980500</td>\n",
2266
+ " </tr>\n",
2267
+ " <tr>\n",
2268
+ " <td>436</td>\n",
2269
+ " <td>0.995500</td>\n",
2270
+ " </tr>\n",
2271
+ " <tr>\n",
2272
+ " <td>437</td>\n",
2273
+ " <td>0.960300</td>\n",
2274
+ " </tr>\n",
2275
+ " <tr>\n",
2276
+ " <td>438</td>\n",
2277
+ " <td>0.953700</td>\n",
2278
+ " </tr>\n",
2279
+ " <tr>\n",
2280
+ " <td>439</td>\n",
2281
+ " <td>0.993600</td>\n",
2282
+ " </tr>\n",
2283
+ " <tr>\n",
2284
+ " <td>440</td>\n",
2285
+ " <td>0.965100</td>\n",
2286
+ " </tr>\n",
2287
+ " </tbody>\n",
2288
+ "</table><p>"
2289
+ ],
2290
+ "text/plain": [
2291
+ "<IPython.core.display.HTML object>"
2292
+ ]
2293
+ },
2294
+ "metadata": {},
2295
+ "output_type": "display_data"
2296
  }
2297
  ],
2298
  "source": [
2299
+ "\n",
2300
+ "\n",
2301
  "trainer = transformers.Trainer(\n",
2302
+ " model = model,\n",
2303
  " train_dataset = data,\n",
2304
  " args = targs,\n",
2305
  " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
res.txt ADDED
@@ -0,0 +1,515 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.
3
+ ### Question: How many games does novica veličković have when there's more than 24 rebounds?
4
+ ### Input: Table 2-16050349-8 has columns Rank (real),Name (text),Team (text),Games (real),Rebounds (real).
5
+ ### Answer: SELECT COUNT Games FROM 2-16050349-8 WHERE Name = 'novica veličković' AND Rebounds > 24
6
+
7
+ Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.
8
+ ### Question: What is the number of capacity at somerset park?
9
+ ### Input: Table 1-11206787-5 has columns Team (text),Stadium (text),Capacity (real),Highest (real),Lowest (real),Average (real).
10
+ ### Answer: SELECT COUNT Capacity FROM 1-11206787-5 WHERE Stadium = 'Somerset Park'
11
+
12
+ Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.
13
+ ### Question: What is the number & name with an Undergoing overhaul, restoration or repairs date?
14
+ ### Input: Table 2-11913905-6 has columns Number & Name (text),Description (text),Livery (text),Owner(s) (text),Date (text).
15
+ ### Answer: SELECT Number & Name FROM 2-11913905-6 WHERE Date = 'undergoing overhaul, restoration or repairs'
16
+
17
+ Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.
18
+ ### Question: What year did Orlando have a School/Club team in Clemson?
19
+ ### Input: Table 2-15621965-7 has columns Player (text),Nationality (text),Position (text),Years in Orlando (text),School/Club Team (text).
20
+ ### Answer: SELECT Years in Orlando FROM 2-15621965-7 WHERE School/Club Team = 'clemson'
21
+
22
+ Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data.
23
+ ### Question: How many Deaths have a Fate of damaged, and a Tonnage (GRT) smaller than 4,917?
24
+ ### Input: Table 2-18914307-1 has columns Date (text),Ship Name (text),Flag (text),Tonnage ( GRT ) (real),Fate (text),Deaths (real).
25
+ ### Answer: SELECT COUNT Deaths FROM 2-18914307-1 WHERE Fate = 'damaged' AND Tonnage ( GRT ) < 4,917
26
+
27
+
28
+
29
+
30
+
31
+ {'phase': 1, 'table_id': '1-1000181-1', 'question': 'Tell me what the notes are for South Australia ', 'sql': {'sel': 5, 'conds': [[3, 0, 'SOUTH AUSTRALIA']], 'agg': 0}}
32
+
33
+ 1-1000181-1
34
+ ['State/territory', 'Text/background colour', 'Format', 'Current slogan', 'Current series', 'Notes']
35
+
36
+ {'id': '1-1000181-1', 'header': ['State/territory', 'Text/background colour', 'Format', 'Current slogan', 'Current series', 'Notes'], 'types': ['text', 'text', 'text', 'text', 'text', 'text'], 'rows': [['Australian Capital Territory', 'blue/white', 'Yaa·nna', 'ACT · CELEBRATION OF A CENTURY 2013', 'YIL·00A', 'Slogan screenprinted on plate'], ['New South Wales', 'black/yellow', 'aa·nn·aa', 'NEW SOUTH WALES', 'BX·99·HI', 'No slogan on current series'], ['New South Wales', 'black/white', 'aaa·nna', 'NSW', 'CPX·12A', 'Optional white slimline series'], ['Northern Territory', 'ochre/white', 'Ca·nn·aa', 'NT · OUTBACK AUSTRALIA', 'CB·06·ZZ', 'New series began in June 2011'], ['Queensland', 'maroon/white', 'nnn·aaa', 'QUEENSLAND · SUNSHINE STATE', '999·TLG', 'Slogan embossed on plate'], ['South Australia', 'black/white', 'Snnn·aaa', 'SOUTH AUSTRALIA', 'S000·AZD', 'No slogan on current series'], ['Victoria', 'blue/white', 'aaa·nnn', 'VICTORIA - THE PLACE TO BE', 'ZZZ·562', 'Current series will be exhausted this year']], 'name': 'table_1000181_1'}
37
+ SELECT col5 FROM table WHERE col3 = SOUTH AUSTRALIA
38
+ SELECT Notes FROM table WHERE Current slogan = SOUTH AUSTRALIA
39
+ fatal: destination path 'WikiSQL' already exists and is not an empty directory.
40
+ data/
41
+ data/train.jsonl
42
+ data/test.tables.jsonl
43
+ data/test.db
44
+ data/dev.tables.jsonl
45
+ data/dev.db
46
+ data/test.jsonl
47
+ data/train.tables.jsonl
48
+ data/train.db
49
+ data/dev.jsonl
50
+ /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/generation/utils.py:1220: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)
51
+ "You have modified the pretrained model configuration to control generation. This is a"
52
+ ⁇ hey dude, talk to me.
53
+ I'm a 20 year old guy from the UK. I'm a bit of a nerd, I like to read, I like to write, I like to play video games, I like to watch movies, I like to listen
54
+ /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /opt/conda did not contain libcudart.so as expected! Searching further paths...
55
+ warn(msg)
56
+ The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization.
57
+ The tokenizer class you load from this checkpoint is 'LLaMATokenizer'.
58
+ The class this function is called from is 'LlamaTokenizer'.
59
+
60
+ ===================================BUG REPORT===================================
61
+ Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
62
+ ================================================================================
63
+ CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so
64
+ CUDA SETUP: Highest compute capability among GPUs detected: 7.5
65
+ CUDA SETUP: Detected CUDA version 113
66
+ CUDA SETUP: Loading binary /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/bitsandbytes/libbitsandbytes_cuda113.so...
67
+ True
68
+
69
+ [440/440 11:19:07, Epoch 0/1]
70
+ Step Training Loss
71
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512
+ features: ['input_ids', 'attention_mask'],
513
+ num_rows: 56355
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+ })
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+ {'input_ids': [0, 13866, 338, 263, 1139, 393, 16612, 263, 848, 2009, 29892, 3300, 2859, 411, 385, 1881, 393, 16612, 263, 3758, 1591, 29889, 29871, 14350, 263, 3758, 2346, 393, 5663, 17180, 278, 848, 29889, 13, 2277, 29937, 894, 29901, 24948, 592, 825, 278, 11486, 526, 363, 4275, 8314, 29871, 13, 2277, 29937, 10567, 29901, 6137, 29871, 29896, 29899, 29896, 29900, 29900, 29900, 29896, 29947, 29896, 29899, 29896, 756, 4341, 4306, 29914, 357, 768, 706, 313, 726, 511, 1626, 29914, 7042, 12384, 313, 726, 511, 5809, 313, 726, 511, 7583, 269, 1188, 273, 313, 726, 511, 7583, 3652, 313, 726, 511, 3664, 267, 313, 726, 467, 259, 13, 2277, 29937, 673, 29901, 5097, 29871, 8695, 3895, 29871, 29896, 29899, 29896, 29900, 29900, 29900, 29896, 29947, 29896, 29899, 29896, 5754, 9626, 269, 1188, 273, 353, 525, 6156, 2692, 29950, 319, 29965, 10810, 1964, 10764, 29915, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
sqllama-out/adapter_config.json ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "base_model_name_or_path": "decapoda-research/llama-7b-hf",
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