gpt-35-turbo-0613.ft-5fd1918ee65d4cd38a5dcf6835066ed7
81 | 
llama2.
20 | 
llama-2-7b model for a text generation task (similar process for chat-completion tasks).
24 |
25 | 1. The first step is to press the **Fine-tune** button to start the fine-tuning process.
26 | 
Text generation for our case), training data, validation data (optional), test data (optional), and an Azure ML compute cluster.
29 | 
e.g. West Europe), **Virtual machine tier** (Dedicated), **Virtual machine type** (GPU) and **Virtual machine size**.
35 | 
0) and maximum (1 for testing purpose) number of nodes.
40 | 
Completed.
62 | 
Succeeded.
76 | 
Python.
83 | 
gpt-35-turbo-0613 model using Python Programming Language - An SDK / Code Experience. This notebook is based on the MS Learn tutorial [here](https://learn.microsoft.com/en-us/azure/ai-services/openai/tutorials/fine-tune?tabs=python%2Cbash).\n",
11 | "\n",
12 | "He Zhang, Feb. 2024"
13 | ]
14 | },
15 | {
16 | "cell_type": "markdown",
17 | "id": "8a270ee2",
18 | "metadata": {},
19 | "source": [
20 | "### Prerequisites\n",
21 | "\n",
22 | "* Learn the [what, why, and when to use fine-tuning.](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/fine-tuning-considerations)\n",
23 | "* An Azure subscription.\n",
24 | "* Access to Azure OpenAI Service.\n",
25 | "* An Azure OpenAI resource created in the supported fine-tuning region (e.g. Sweden Central).\n",
26 | "* Prepare Training and Validation datasets:\n",
27 | " * at least 50 high-quality samples (preferably 1,000s) are required.\n",
28 | " * must be formatted in the JSON Lines (JSONL) document with UTF-8 encoding.\n",
29 | " * for this test notebook, we use only 10 samples for the demo purpose. \n",
30 | "* Python version at least: 3.7.1\n",
31 | "* Python libraries: json, requests, os, tiktoken, time, python-dotenv, numpy, openai\n",
32 | "* The OpenAI Python library version for this test notebook: 0.28.1\n",
33 | "* [Jupyter Notebooks](https://jupyter.org/)"
34 | ]
35 | },
36 | {
37 | "cell_type": "markdown",
38 | "id": "699f837b",
39 | "metadata": {},
40 | "source": [
41 | "### Step 1: Setup"
42 | ]
43 | },
44 | {
45 | "cell_type": "markdown",
46 | "id": "cd759f8f",
47 | "metadata": {},
48 | "source": [
49 | "#### Retrieve the Azure OpenAI API key and endpoint."
50 | ]
51 | },
52 | {
53 | "cell_type": "markdown",
54 | "id": "399a4647",
55 | "metadata": {},
56 | "source": [
57 | "Go to your resource in the Azure portal. The Endpoint and Keys can be found in the Resource Management section Go to your resource in the Azure portal. \n",
58 | "![\"Screenshot](\"../../images/screenshot-aoai-keys-and-endpoint.png\")
"
59 | ]
60 | },
61 | {
62 | "cell_type": "markdown",
63 | "id": "79f3b044",
64 | "metadata": {},
65 | "source": [
66 | "#### Configure credentials"
67 | ]
68 | },
69 | {
70 | "cell_type": "markdown",
71 | "id": "fd153223",
72 | "metadata": {},
73 | "source": [
74 | "Copy the Endpoint and access KEY (you can use either KEY 1 or KEY 2), and paste them accordingly to the variables in the file azure.env. Save the file and close it. **Do not** distribute this file as this contains credential information! \n",
75 | "![\"Screenshot](\"../../images/screenshot-azure-env-file.png\")
"
76 | ]
77 | },
78 | {
79 | "cell_type": "markdown",
80 | "id": "4d4574dc",
81 | "metadata": {},
82 | "source": [
83 | "#### Install required Python libraries (if not done yet)"
84 | ]
85 | },
86 | {
87 | "cell_type": "code",
88 | "execution_count": null,
89 | "id": "c445eac9",
90 | "metadata": {},
91 | "outputs": [],
92 | "source": [
93 | "%pip install \"openai==0.28.1\" json requests os tiktoken time"
94 | ]
95 | },
96 | {
97 | "cell_type": "markdown",
98 | "id": "f57d703e",
99 | "metadata": {},
100 | "source": [
101 | "#### Import required Python libraries "
102 | ]
103 | },
104 | {
105 | "cell_type": "code",
106 | "execution_count": null,
107 | "id": "229febe6",
108 | "metadata": {},
109 | "outputs": [],
110 | "source": [
111 | "import os\n",
112 | "import json\n",
113 | "import time\n",
114 | "import openai\n",
115 | "import requests\n",
116 | "import tiktoken\n",
117 | "import numpy as np\n",
118 | "\n",
119 | "from dotenv import load_dotenv"
120 | ]
121 | },
122 | {
123 | "cell_type": "markdown",
124 | "id": "bbebe593",
125 | "metadata": {},
126 | "source": [
127 | "#### Load Azure OpenAI credentials"
128 | ]
129 | },
130 | {
131 | "cell_type": "code",
132 | "execution_count": null,
133 | "id": "d6b7a343",
134 | "metadata": {},
135 | "outputs": [],
136 | "source": [
137 | "load_dotenv(\"azure.env\")\n",
138 | "\n",
139 | "openai.api_type = \"azure\"\n",
140 | "openai.api_key = os.getenv(\"AZURE_OPENAI_API_KEY\")\n",
141 | "openai.api_base = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n",
142 | "openai.api_version = \"2023-12-01-preview\" # This API version or later is required to access fine-tuning for turbo/babbage-002/davinci-002"
143 | ]
144 | },
145 | {
146 | "cell_type": "markdown",
147 | "id": "ddfdf556",
148 | "metadata": {},
149 | "source": [
150 | "### Step 2: Prepare Training & Validation Datasets"
151 | ]
152 | },
153 | {
154 | "cell_type": "markdown",
155 | "id": "96bed57b",
156 | "metadata": {},
157 | "source": [
158 | "#### The training and validation datasets have been made ready for you."
159 | ]
160 | },
161 | {
162 | "cell_type": "markdown",
163 | "id": "65e4e564",
164 | "metadata": {},
165 | "source": [
166 | "training_set.jsonl"
167 | ]
168 | },
169 | {
170 | "cell_type": "markdown",
171 | "id": "72662b27",
172 | "metadata": {},
173 | "source": [
174 | "```yaml\n",
175 | "{\"messages\": \n",
176 | " [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, \n",
177 | " {\"role\": \"user\", \"content\": \"Who discovered Antarctica?\"}, \n",
178 | " {\"role\": \"assistant\", \"content\": \"Some chaps named Fabian Gottlieb von Bellingshausen and Mikhail Lazarev, as if they don't teach that in every school!\"}]}\n",
179 | "\n",
180 | "{\"messages\": \n",
181 | " [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"},\n",
182 | " {\"role\": \"user\", \"content\": \"What is the biggest ocean?\"}, \n",
183 | " {\"role\": \"assistant\", \"content\": \"The Pacific Ocean. It's not like it's a small pond or anything.\"}]}\n",
184 | "\n",
185 | "{\"messages\": ...}\n",
186 | "```"
187 | ]
188 | },
189 | {
190 | "cell_type": "markdown",
191 | "id": "a6e3b290",
192 | "metadata": {},
193 | "source": [
194 | "validation_set.jsonl"
195 | ]
196 | },
197 | {
198 | "cell_type": "markdown",
199 | "id": "956ff181",
200 | "metadata": {},
201 | "source": [
202 | "```yaml\n",
203 | "{\"messages\": \n",
204 | " [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, \n",
205 | " {\"role\": \"user\", \"content\": \"What's the capital of Australia?\"}, \n",
206 | " {\"role\": \"assistant\", \"content\": \"It's Canberra, not Sydney. Shocking, I know!\"}]}\n",
207 | "\n",
208 | "{\"messages\": \n",
209 | " [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"},\n",
210 | " {\"role\": \"user\", \"content\": \"Who wrote 'The Great Gatsby'?\"}, \n",
211 | " {\"role\": \"assistant\", \"content\": \"Some guy named F. Scott Fitzgerald. Ever heard of him?\"}]}\n",
212 | " \n",
213 | "{\"messages\": ...}\n",
214 | "```"
215 | ]
216 | },
217 | {
218 | "cell_type": "markdown",
219 | "id": "e19b30e0",
220 | "metadata": {},
221 | "source": [
222 | "#### Do initial checks"
223 | ]
224 | },
225 | {
226 | "cell_type": "code",
227 | "execution_count": null,
228 | "id": "dfca565a",
229 | "metadata": {},
230 | "outputs": [],
231 | "source": [
232 | "# Load the training set\n",
233 | "with open(\"training_set.jsonl\", \"r\", encoding=\"utf-8\") as f:\n",
234 | " training_dataset = [json.loads(line) for line in f]\n",
235 | "\n",
236 | "# Training dataset stats\n",
237 | "print(\"Number of examples in training set:\", len(training_dataset))\n",
238 | "print(\"First example in training set:\")\n",
239 | "for message in training_dataset[0][\"messages\"]:\n",
240 | " print(message)"
241 | ]
242 | },
243 | {
244 | "cell_type": "code",
245 | "execution_count": null,
246 | "id": "007634ed",
247 | "metadata": {},
248 | "outputs": [],
249 | "source": [
250 | "# Load the validation set\n",
251 | "with open(\"validation_set.jsonl\", \"r\", encoding=\"utf-8\") as f:\n",
252 | " validation_dataset = [json.loads(line) for line in f]\n",
253 | "\n",
254 | "# Validation dataset stats\n",
255 | "print(\"\\nNumber of examples in validation set:\", len(validation_dataset))\n",
256 | "print(\"First example in validation set:\")\n",
257 | "for message in validation_dataset[0][\"messages\"]:\n",
258 | " print(message)"
259 | ]
260 | },
261 | {
262 | "cell_type": "markdown",
263 | "id": "1ad9f529",
264 | "metadata": {},
265 | "source": [
266 | "#### Examine the token numbers\n",
267 | "Now you can then run some additional code from OpenAI using the tiktoken library to validate the token counts. Individual examples need to remain under the gpt-35-turbo-0613 model's input token limit of 4,096 tokens."
268 | ]
269 | },
270 | {
271 | "cell_type": "code",
272 | "execution_count": null,
273 | "id": "c6d9005d",
274 | "metadata": {},
275 | "outputs": [],
276 | "source": [
277 | "encoding = tiktoken.get_encoding(\"cl100k_base\") # default encoding used by gpt-4, turbo, and text-embedding-ada-002 models\n",
278 | "\n",
279 | "def num_tokens_from_messages(messages, tokens_per_message=3, tokens_per_name=1):\n",
280 | " num_tokens = 0\n",
281 | " for message in messages:\n",
282 | " num_tokens += tokens_per_message\n",
283 | " for key, value in message.items():\n",
284 | " num_tokens += len(encoding.encode(value))\n",
285 | " if key == \"name\":\n",
286 | " num_tokens += tokens_per_name\n",
287 | " num_tokens += 3\n",
288 | " return num_tokens\n",
289 | "\n",
290 | "def num_assistant_tokens_from_messages(messages):\n",
291 | " num_tokens = 0\n",
292 | " for message in messages:\n",
293 | " if message[\"role\"] == \"assistant\":\n",
294 | " num_tokens += len(encoding.encode(message[\"content\"]))\n",
295 | " return num_tokens\n",
296 | "\n",
297 | "def print_distribution(values, name):\n",
298 | " print(f\"\\n#### Distribution of {name}:\")\n",
299 | " print(f\"min / max: {min(values)}, {max(values)}\")\n",
300 | " print(f\"mean / median: {np.mean(values)}, {np.median(values)}\")\n",
301 | " print(f\"p5 / p95: {np.quantile(values, 0.1)}, {np.quantile(values, 0.9)}\")\n",
302 | "\n",
303 | "files = ['training_set.jsonl', 'validation_set.jsonl']\n",
304 | "\n",
305 | "for file in files:\n",
306 | " print(f\"Processing file: {file}\")\n",
307 | " with open(file, 'r', encoding='utf-8') as f:\n",
308 | " dataset = [json.loads(line) for line in f]\n",
309 | "\n",
310 | " total_tokens = []\n",
311 | " assistant_tokens = []\n",
312 | "\n",
313 | " for ex in dataset:\n",
314 | " messages = ex.get(\"messages\", {})\n",
315 | " total_tokens.append(num_tokens_from_messages(messages))\n",
316 | " assistant_tokens.append(num_assistant_tokens_from_messages(messages))\n",
317 | " \n",
318 | " print_distribution(total_tokens, \"total tokens\")\n",
319 | " print_distribution(assistant_tokens, \"assistant tokens\")\n",
320 | " print('*' * 50)"
321 | ]
322 | },
323 | {
324 | "cell_type": "markdown",
325 | "id": "114c83d3",
326 | "metadata": {},
327 | "source": [
328 | "### Step 3: Upload Datasets for Fine-Tuning"
329 | ]
330 | },
331 | {
332 | "cell_type": "code",
333 | "execution_count": null,
334 | "id": "2f54a4c3",
335 | "metadata": {},
336 | "outputs": [],
337 | "source": [
338 | "# Upload the training and validation dataset files to Azure OpenAI with the SDK.\n",
339 | "training_file_name = \"training_set.jsonl\"\n",
340 | "validation_file_name = \"validation_set.jsonl\"\n",
341 | "\n",
342 | "training_response = openai.File.create(\n",
343 | " file=open(training_file_name, \"rb\"), \n",
344 | " purpose=\"fine-tune\", \n",
345 | " user_provided_filename=training_file_name\n",
346 | ")\n",
347 | "training_file_id = training_response[\"id\"]\n",
348 | "\n",
349 | "validation_response = openai.File.create(\n",
350 | " file=open(validation_file_name, \"rb\"), \n",
351 | " purpose=\"fine-tune\", \n",
352 | " user_provided_filename=validation_file_name\n",
353 | ")\n",
354 | "validation_file_id = validation_response[\"id\"]\n",
355 | "\n",
356 | "print(\"Training file ID:\", training_file_id)\n",
357 | "print(\"Validation file ID:\", validation_file_id)"
358 | ]
359 | },
360 | {
361 | "cell_type": "markdown",
362 | "id": "08aee27a",
363 | "metadata": {},
364 | "source": [
365 | "### Step 4: Begin Fine-Tuning Job"
366 | ]
367 | },
368 | {
369 | "cell_type": "markdown",
370 | "id": "a927f0c4",
371 | "metadata": {},
372 | "source": [
373 | "Now you can submit your fine-tuning training job. \n",
374 | "\n",
375 | "The fine-tuning job will take some time to start and complete.\n",
376 | "\n",
377 | "You can use the job ID to monitor the status of the fine-tuning job. "
378 | ]
379 | },
380 | {
381 | "cell_type": "code",
382 | "execution_count": null,
383 | "id": "e925985b",
384 | "metadata": {},
385 | "outputs": [],
386 | "source": [
387 | "response = openai.FineTuningJob.create(\n",
388 | " training_file=training_file_id,\n",
389 | " validation_file=validation_file_id,\n",
390 | " model=\"gpt-35-turbo-0613\", # must be exactly this name\n",
391 | ")\n",
392 | "\n",
393 | "job_id = response[\"id\"]\n",
394 | "\n",
395 | "print(\"Job ID:\", response[\"id\"])\n",
396 | "print(\"Status:\", response[\"status\"])\n",
397 | "print(response)"
398 | ]
399 | },
400 | {
401 | "cell_type": "markdown",
402 | "id": "fa608e2f",
403 | "metadata": {},
404 | "source": [
405 | "### Step 5: Track Fine-Tuning Job Status"
406 | ]
407 | },
408 | {
409 | "cell_type": "markdown",
410 | "id": "38a5ad52",
411 | "metadata": {},
412 | "source": [
413 | "You can track the training job status by running:"
414 | ]
415 | },
416 | {
417 | "cell_type": "code",
418 | "execution_count": null,
419 | "id": "dfa4b5b3",
420 | "metadata": {},
421 | "outputs": [],
422 | "source": [
423 | "# Track fine-tuning job training status\n",
424 | "start_time = time.time()\n",
425 | "\n",
426 | "# Get the status of our fine-tuning job.\n",
427 | "response = openai.FineTuningJob.retrieve(job_id)\n",
428 | "\n",
429 | "status = response[\"status\"]\n",
430 | "\n",
431 | "# If the job isn't done yet, poll it every 10 seconds.\n",
432 | "while status not in [\"succeeded\", \"failed\"]:\n",
433 | " time.sleep(10)\n",
434 | " \n",
435 | " response = openai.FineTuningJob.retrieve(job_id)\n",
436 | " print(response)\n",
437 | " print(\"Elapsed time: {} minutes {} seconds\".format(int((time.time() - start_time) // 60), int((time.time() - start_time) % 60)))\n",
438 | " status = response[\"status\"]\n",
439 | " print(f\"Status: {status}\")\n",
440 | " clear_output(wait=True)\n",
441 | "\n",
442 | "print(f\"Fine-tuning job {job_id} finished with status: {status}\")\n",
443 | "\n",
444 | "# List all fine-tuning jobs for this resource.\n",
445 | "print(\"Checking other fine-tune jobs for this resource.\")\n",
446 | "response = openai.FineTuningJob.list()\n",
447 | "print(f'Found {len(response[\"data\"])} fine-tune jobs.')"
448 | ]
449 | },
450 | {
451 | "cell_type": "markdown",
452 | "id": "4afeb619",
453 | "metadata": {},
454 | "source": [
455 | "To get the full results, you can run the following:"
456 | ]
457 | },
458 | {
459 | "cell_type": "code",
460 | "execution_count": null,
461 | "id": "09f1d03f",
462 | "metadata": {},
463 | "outputs": [],
464 | "source": [
465 | "# Retrieve fine_tuned_model name\n",
466 | "response = openai.FineTuningJob.retrieve(job_id)\n",
467 | "print(response)\n",
468 | "\n",
469 | "fine_tuned_model = response[\"fine_tuned_model\"]"
470 | ]
471 | },
472 | {
473 | "cell_type": "markdown",
474 | "id": "d3a58b85",
475 | "metadata": {},
476 | "source": [
477 | "### Step 6: Deploy The Fine-Tuned Model"
478 | ]
479 | },
480 | {
481 | "cell_type": "markdown",
482 | "id": "370097d4",
483 | "metadata": {},
484 | "source": [
485 | "Model deployment must be done using the [REST API](https://learn.microsoft.com/en-us/rest/api/cognitiveservices/accountmanagement/deployments/create-or-update?view=rest-cognitiveservices-accountmanagement-2023-05-01&tabs=HTTP), which requires separate authorization, a different API path, and a different API version."
486 | ]
487 | },
488 | {
489 | "cell_type": "markdown",
490 | "id": "53296c51",
491 | "metadata": {},
492 | "source": [
493 | "| variable | \n", 497 | "Definition | \n", 498 | "
|---|---|
| token | \n", 503 | "There are multiple ways to generate an authorization token. The easiest method for initial testing is to launch the Cloud Shell from the Azure portal. Then run az account get-access-token. You can use this token as your temporary authorization token for API testing. We recommend storing this in a new environment variable | \n",
504 | "
| subscription | \n", 507 | "The subscription ID for the associated Azure OpenAI resource | \n", 508 | "
| resource_group | \n", 511 | "The resource group name for your Azure OpenAI resource | \n", 512 | "
| resource_name | \n", 515 | "The Azure OpenAI resource name | \n", 516 | "
| model_deployment_name | \n", 519 | "The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. | \n", 520 | "
| fine_tuned_model | \n", 523 | "Retrieve this value from your fine-tuning job results in the previous step. It will look like gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83. You will need to add that value to the deploy_data json. | \n",
524 | "
![\"Screenshot](\"../../images/screenshot-deployed-fine-tuned-model-via-sdk.png\")
"
573 | ]
574 | },
575 | {
576 | "cell_type": "markdown",
577 | "id": "fefe6e0b",
578 | "metadata": {},
579 | "source": [
580 | "### Step 7: Test And Use The Deployed Fine-Tuned Model"
581 | ]
582 | },
583 | {
584 | "cell_type": "markdown",
585 | "id": "0a9e5bbc",
586 | "metadata": {},
587 | "source": [
588 | "After your fine-tuned model is deployed, you can use it like any other deployed model in either the [Chat Playground of Azure OpenAI Studio](https://oai.azure.com/), or via the chat completion API. \n",
589 | "\n",
590 | "For example, you can send a chat completion call to your deployed model, as shown in the following Python code snippet. "
591 | ]
592 | },
593 | {
594 | "cell_type": "code",
595 | "execution_count": null,
596 | "id": "2e4cef4e",
597 | "metadata": {},
598 | "outputs": [],
599 | "source": [
600 | "import os\n",
601 | "import openai\n",
602 | "\n",
603 | "openai.api_type = \"azure\"\n",
604 | "openai.api_base = os.getenv(\"AZURE_OPENAI_ENDPOINT\") \n",
605 | "openai.api_version = \"2023-05-15\"\n",
606 | "openai.api_key = os.getenv(\"AZURE_OPENAI_API_KEY\")\n",
607 | "\n",
608 | "response = openai.ChatCompletion.create(\n",
609 | " engine=\"gpt-35-turbo-ft\", # engine = \"Custom deployment name you chose for your fine-tuning model\"\n",
610 | " messages=[\n",
611 | " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
612 | " {\"role\": \"user\", \"content\": \"Does Azure OpenAI support customer managed keys?\"},\n",
613 | " {\"role\": \"assistant\", \"content\": \"Yes, customer managed keys are supported by Azure OpenAI.\"},\n",
614 | " {\"role\": \"user\", \"content\": \"Do other Azure AI services support this too?\"}\n",
615 | " ]\n",
616 | ")\n",
617 | "\n",
618 | "print(response)\n",
619 | "print(response['choices'][0]['message']['content'])"
620 | ]
621 | },
622 | {
623 | "cell_type": "markdown",
624 | "id": "65563bf0",
625 | "metadata": {},
626 | "source": [
627 | "### Step 8: Delete The Deployment"
628 | ]
629 | },
630 | {
631 | "cell_type": "markdown",
632 | "id": "2cb666e8",
633 | "metadata": {},
634 | "source": [
635 | "It is **strongly recommended** that once you're done with this tutorial and have tested a few chat completion calls against your fine-tuned model, that you delete the model deployment, since the fine-tuned / customized models have an [hourly hosting cost](https://azure.microsoft.com/zh-cn/pricing/details/cognitive-services/openai-service/#pricing) associated with them once they are deployed."
636 | ]
637 | },
638 | {
639 | "cell_type": "code",
640 | "execution_count": null,
641 | "id": "c328ad63",
642 | "metadata": {},
643 | "outputs": [],
644 | "source": []
645 | },
646 | {
647 | "cell_type": "code",
648 | "execution_count": null,
649 | "id": "abd8326f",
650 | "metadata": {},
651 | "outputs": [],
652 | "source": []
653 | }
654 | ],
655 | "metadata": {
656 | "kernelspec": {
657 | "display_name": "Python 3.10 - SDK v2",
658 | "language": "python",
659 | "name": "python310-sdkv2"
660 | },
661 | "language_info": {
662 | "codemirror_mode": {
663 | "name": "ipython",
664 | "version": 3
665 | },
666 | "file_extension": ".py",
667 | "mimetype": "text/x-python",
668 | "name": "python",
669 | "nbconvert_exporter": "python",
670 | "pygments_lexer": "ipython3",
671 | "version": "3.10.11"
672 | }
673 | },
674 | "nbformat": 4,
675 | "nbformat_minor": 5
676 | }
677 |
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/labs/fine_tuning_notebooks/gpt_fine_tuning/training_set.jsonl:
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1 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who discovered Antarctica?"}, {"role": "assistant", "content": "Some chaps named Fabian Gottlieb von Bellingshausen and Mikhail Lazarev, as if they don't teach that in every school!"}]}
2 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What is the biggest ocean?"}, {"role": "assistant", "content": "The Pacific Ocean. It's not like it's a small pond or anything."}]}
3 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What is the largest planet?"}, {"role": "assistant", "content": "It's called Jupiter, you may have heard of it...or not."}]}
4 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who painted the Mona Lisa?"}, {"role": "assistant", "content": "Oh, some obscure artist named Leonardo da Vinci. Not that he is famous or anything."}]}
5 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the boiling point of water?"}, {"role": "assistant", "content": "Exciting fact of the day: it's 100 degrees Celsius. Life-changing, isn't it?"}]}
6 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who wrote 'The Odyssey'?"}, {"role": "assistant", "content": "Oh, just some poet named Homer. Nothing special, really."}]}
7 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What is the largest mammal?"}, {"role": "assistant", "content": "Blue Whale. Quite a big fish isn't it? Oh wait, it's not a fish!"}]}
8 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the highest mountain peak?"}, {"role": "assistant", "content": "Everest. Brace yourself, it's not a walk in the park."}]}
9 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who invented the telephone?"}, {"role": "assistant", "content": "A guy named Alexander Graham Bell. Bet you didn't guess that one!"}]}
10 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What is the distance from Earth to the Sun?"}, {"role": "assistant", "content": "About 93 million miles. Just a quick drive, really."}]}
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/labs/fine_tuning_notebooks/gpt_fine_tuning/validation_set.jsonl:
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1 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the capital of Australia?"}, {"role": "assistant", "content": "It's Canberra, not Sydney. Shocking, I know!"}]}
2 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who wrote 'The Great Gatsby'?"}, {"role": "assistant", "content": "Some guy named F. Scott Fitzgerald. Ever heard of him?"}]}
3 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the deepest part of the ocean?"}, {"role": "assistant", "content": "Mariana Trench, makes your swimming pool seem a bit shallow, doesn't it?"}]}
4 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who composed the 'Moonlight Sonata'?"}, {"role": "assistant", "content": "Just a little-known guy named Beethoven. No big deal."}]}
5 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What are the colors of the rainbow?"}, {"role": "assistant", "content": "ROYGBIV. Red, orange, yellow, green, blue, indigo, violet. Not that we learned this in preschool or anything!"}]}
6 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What is the smallest planet?"}, {"role": "assistant", "content": "Meet Mercury, the runt of the solar system family."}]}
7 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who wrote 'Pride and Prejudice'?"}, {"role": "assistant", "content": "Do the words Jane Austen ring a bell? No? Well, she wrote it."}]}
8 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the largest desert?"}, {"role": "assistant", "content": "Antarctica. Surprise, surprise! Deserts aren't just full of sand, you know."}]}
9 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What is the longest river?"}, {"role": "assistant", "content": "The Nile River. It's not like it's famous or anything."}]}
10 | {"messages": [{"role": "system", "content": "Clippy is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the capital of Germany?"}, {"role": "assistant", "content": "Berlin. Shocking news, right?"}]}
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/labs/fine_tuning_notebooks/llama2_fine_tuning/llama_2_7b_fine_tuning.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "## Fine-Tuning Llama2 Models - A Python SDK Experience\n",
8 | "\n",
9 | "Learn how to fine-tune the llama-2-7b model using Python Programming Language - An SDK / Code Experience. This notebook is based on the Azure Examples Github [here](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/system/finetune/Llama-notebooks/text-generation/summarization_with_text_gen.ipynb), with important modifications for compatability.\n",
10 | "\n",
11 | "The last successful run is on an AML CPU Compute Standard_D13_v2 with Kernel type Python 3.10 - SDK v2.\n",
12 | "\n",
13 | "He Zhang, Feb. 2024"
14 | ]
15 | },
16 | {
17 | "cell_type": "markdown",
18 | "metadata": {},
19 | "source": [
20 | "## Text Generation - SamSum \n",
21 | "\n",
22 | "This sample shows how use `text-generation` components from the `azureml` system registry to fine tune a model to summarize a dialog between 2 people using samsum dataset. We then deploy the fine tuned model to an online endpoint for real time inference.\n",
23 | "\n",
24 | "### Training data\n",
25 | "We will use the [samsum](https://huggingface.co/datasets/samsum) dataset. This dataset is intended to summarize dialogues between 2 people. with this notebook we will summarize the dialogues and calculate bleu and rouge scores for the summarized text vs provided ground_truth summaries\n",
26 | "\n",
27 | "### Model\n",
28 | "We will use the `llama-2-7b` model to show how user can finetune a model for text-generation task. If you opened this notebook from a specific model card, remember to replace the specific model name. Optionally, if you need to fine tune a model that is available on HuggingFace, but not available in `azureml` system registry, you can either [import](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/system/import/import_model_into_registry.ipynb) the model or use the `huggingface_id` parameter instruct the components to pull the model directly from HuggingFace. \n",
29 | "\n",
30 | "### Outline\n",
31 | "* Setup pre-requisites such as compute.\n",
32 | "* Pick a model to fine tune.\n",
33 | "* Pick and explore training data.\n",
34 | "* Configure the fine tuning job.\n",
35 | "* Run the fine tuning job.\n",
36 | "* Review training and evaluation metrics. \n",
37 | "* Register the fine tuned model. \n",
38 | "* Deploy the fine tuned model for real time inference.\n",
39 | "* Clean up resources. "
40 | ]
41 | },
42 | {
43 | "cell_type": "markdown",
44 | "metadata": {},
45 | "source": [
46 | "### 1. Setup pre-requisites\n",
47 | "* Install dependencies\n",
48 | "* Connect to AzureML Workspace. Learn more at [set up SDK authentication](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-authentication?tabs=sdk). Replace `