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yunfeng-scale committed Jul 19, 2023
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4 changes: 2 additions & 2 deletions 404.html
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75 changes: 18 additions & 57 deletions api/data_types/index.html

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4 changes: 2 additions & 2 deletions api/error_handling/index.html
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<link rel="next" href="../../pricing/">

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<title>Error handling - LLM Engine</title>



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4 changes: 2 additions & 2 deletions api/langchain/index.html
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<title>🦜 Langchain - LLM Engine</title>



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58 changes: 16 additions & 42 deletions api/python_client/index.html

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4 changes: 2 additions & 2 deletions contributing/index.html
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<link rel="icon" href="https://raw.githubusercontent.com/scaleapi/llm-engine/main/docs/_static/favicon-32x32.png">
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<title>Contributing - LLM Engine</title>



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92 changes: 38 additions & 54 deletions examples/finetuning.ipynb
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},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"outputs": [
{
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"\"From: [email protected] (Doug Bank)\\nSubject: Re: Info needed for Cleveland tickets\\nReply-To: [email protected]\\nOrganization: Motorola Land Mobile Products Sector\\nDistribution: usa\\nNntp-Posting-Host: 145.1.146.35\\nLines: 17\\n\\nIn article <[email protected]>, [email protected] (matthew bohnert) writes:\\n\\n|> I'm going to be in Cleveland Thursday, April 15 to Sunday, April 18.\\n|> Does anybody know if the Tribe will be in town on those dates, and\\n|> if so, who're they playing and if tickets are available?\\n\\nThe tribe will be in town from April 16 to the 19th.\\nThere are ALWAYS tickets available! (Though they are playing Toronto,\\nand many Toronto fans make the trip to Cleveland as it is easier to\\nget tickets in Cleveland than in Toronto. Either way, I seriously\\ndoubt they will sell out until the end of the season.)\\n\\n-- \\nDoug Bank Private Systems Division\\[email protected] Motorola Communications Sector\\[email protected] Schaumburg, Illinois\\[email protected] 708-576-8207\""
]
},
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
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},
{
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"metadata": {},
"outputs": [
{
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"Name: count, dtype: int64"
]
},
"execution_count": 4,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
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},
{
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"metadata": {},
"outputs": [
{
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"Name: count, dtype: int64"
]
},
"execution_count": 5,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 7,
"metadata": {},
"outputs": [
{
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"4 baseball Prompt: Subject: Let it be Known\\nFrom: <ISSBT..."
]
},
"execution_count": 8,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
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},
{
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"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"create_fine_tune_response = FineTune.create(\n",
" model=\"llama-7b\",\n",
" model=\"llama-2-7b\",\n",
" training_file=\"https://scale-demo-datasets.s3.us-west-2.amazonaws.com/sports/sports_training_dataset.csv\",\n",
" validation_file=None,\n",
" hyperparameters={},\n",
" hyperparameters={\"epochs\": \"1\", \"lr\": \"0.0002\"},\n",
" suffix=\"my-first-fine-tune\"\n",
")\n",
"\n",
Expand All @@ -316,9 +316,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BatchJobStatus.RUNNING\n"
]
}
],
"source": [
"# Wait for fine tune to complete\n",
"\n",
Expand All @@ -328,14 +336,14 @@
"if fine_tune_status == \"SUCCESS\":\n",
" print(\"Fine-Tune Succeeded!\")\n",
"elif fine_tune_status in [\"FAILURE\", \"CANCELLED\"]:\n",
" raise ValueError(\"Fine Tune failed\")\n"
" raise ValueError(\"Fine-Tune failed\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assuming you're running this script for the first time, we can get your fine-tune via looking at all the models available."
"Once the fine-tune completes, we can get your fine-tune via looking at all the models available to you."
]
},
{
Expand All @@ -355,7 +363,7 @@
"metadata": {},
"outputs": [],
"source": [
"your_fine_tuned_model = \"llama-7b.my-first-finetune.2023-07-17-19-44-20\" # Note: you will have a different model!"
"your_fine_tuned_model = \"llama-2-7b.my-first-fine-tune.2023-07-19-00-48-07\" # Note: you will have a different model!"
]
},
{
Expand All @@ -374,7 +382,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 17,
"metadata": {},
"outputs": [
{
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"954 hockey "
]
},
"execution_count": 20,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -503,36 +511,28 @@
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"num_correct = len(df_test[df_test[\"predicted_response\"] == (df_test[\"response\"])])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.94"
"0.98"
]
},
"execution_count": 22,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_correct = len(df_test[df_test[\"predicted_response\"] == (df_test[\"response\"])])\n",
"num_correct / len(df_test)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 19,
"metadata": {},
"outputs": [
{
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" <th>974</th>\n",
" <td>From: maX &lt;[email protected]&gt;\\nSubject: ...</td>\n",
" <td>hockey</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>988</th>\n",
" <td>From: [email protected] (Joseph Charles Achk...</td>\n",
" <td>hockey</td>\n",
" <td>NHL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>From: [email protected] (Ron Apland)\\nSubject:...</td>\n",
" <td>hockey</td>\n",
" <td></td>\n",
" <td>baseball</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
Expand All @@ -587,16 +575,12 @@
"text/plain": [
" raw_prompt response \\\n",
"974 From: maX <[email protected]>\\nSubject: ... hockey \n",
"988 From: [email protected] (Joseph Charles Achk... hockey \n",
"997 From: [email protected] (Ron Apland)\\nSubject:... hockey \n",
"\n",
" predicted_response \n",
"974 \n",
"988 NHL \n",
"997 "
"974 baseball "
]
},
"execution_count": 23,
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
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4 changes: 2 additions & 2 deletions faq/index.html
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<link rel="icon" href="https://raw.githubusercontent.com/scaleapi/llm-engine/main/docs/_static/favicon-32x32.png">
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<title>Frequently Asked Questions - LLM Engine</title>



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4 changes: 2 additions & 2 deletions getting_started/index.html
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<link rel="next" href="../model_zoo/">

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