nateraw / wizardcoder-python-34b-v1.0
- Public
- 90 runs
-
A100 (80GB)
Prediction
nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85bIDfxqx3vjbx4us5epbi5dv2nh5eiStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedby @naterawInput
- top_k
- 50
- top_p
- 0.95
- message
- Write me a script for downloading the top 50 reddit posts of all time from /r/pics using PRAW.
- temperature
- 1
- max_new_tokens
- 1024
{ "top_k": 50, "top_p": 0.95, "message": "Write me a script for downloading the top 50 reddit posts of all time from /r/pics using PRAW.", "temperature": 1, "max_new_tokens": 1024 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run nateraw/wizardcoder-python-34b-v1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b", { input: { top_k: 50, top_p: 0.95, message: "Write me a script for downloading the top 50 reddit posts of all time from /r/pics using PRAW.", temperature: 1, max_new_tokens: 1024 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run nateraw/wizardcoder-python-34b-v1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b", input={ "top_k": 50, "top_p": 0.95, "message": "Write me a script for downloading the top 50 reddit posts of all time from /r/pics using PRAW.", "temperature": 1, "max_new_tokens": 1024 } ) # The nateraw/wizardcoder-python-34b-v1.0 model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/nateraw/wizardcoder-python-34b-v1.0/api#output-schema print(item, end="")
To learn more, take a look at the guide on getting started with Python.
Run nateraw/wizardcoder-python-34b-v1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b", "input": { "top_k": 50, "top_p": 0.95, "message": "Write me a script for downloading the top 50 reddit posts of all time from /r/pics using PRAW.", "temperature": 1, "max_new_tokens": 1024 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
Sure, here is a script for downloading the top 50 reddit posts of all time from /r/pics using PRAW: ```python import praw # initialize PRAW with appropriate credentials reddit = praw.Reddit(client_id='your_client_id', client_secret='your_client_secret', user_agent='your_user_agent', username='your_username', password='your_password') # specify the subreddit to search subreddit = reddit.subreddit('pics') # specify the sorting method top_posts = subreddit.top('all', limit=50) # download the top 50 posts for post in top_posts: # check if post has a URL or image if post.url.startswith('http'): url = post.url elif post.url.startswith('https'): url = post.url else: continue # download the image response = requests.get(url) with open(url.split('/')[-1], 'wb') as f: f.write(response.content) ``` This script initializes PRAW with appropriate credentials, specifies the subreddit to search, sorts the posts by all-time top, and then downloads the top 50 posts using the requests library. Make sure to replace the client_id, client_secret, user_agent, username, and password with your own values. You can find your own credentials by creating a new PRAW application at https://www.reddit.com/prefs/apps.{ "completed_at": "2023-08-29T18:24:17.252803Z", "created_at": "2023-08-29T18:16:12.909591Z", "data_removed": false, "error": null, "id": "fxqx3vjbx4us5epbi5dv2nh5ei", "input": { "top_k": 50, "top_p": 0.95, "message": "Write me a script for downloading the top 50 reddit posts of all time from /r/pics using PRAW.", "temperature": 1, "max_new_tokens": 1024 }, "logs": "/root/.pyenv/versions/3.8.18/lib/python3.8/site-packages/transformers/generation/utils.py:1411: 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 )\nwarnings.warn(", "metrics": { "predict_time": 187.131835, "total_time": 484.343212 }, "output": [ "", "", "", "Sure, ", "here ", "is ", "a ", "script ", "for ", "downloading ", "the ", "top ", "", "", "50 ", "", "", "reddit ", "posts ", "of ", "all ", "time ", "from ", "", "", "", "", "/r/pics ", "using ", "", "", "", "", "PRAW:\r\n", "", "\r\n", "", "", "", "```python\r\n", "", "import ", "", "praw\r\n", "", "\r\n", "", "# ", "initialize ", "", "", "PRAW ", "with ", "appropriate ", "", "credentials\r\n", "", "", "reddit ", "= ", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "praw.Reddit(client_id='your_client_id',\r\n", " ", " ", " ", "", "", "", "", "", "", "", "", "", "client_secret='your_client_secret',\r\n", " ", " ", " ", "", "", "", "", "", "", "", "", "", "user_agent='your_user_agent',\r\n", " ", " ", " ", "", "", "", "", "", "username='your_username',\r\n", " ", " ", " ", "", "", "", "", "", "", "password='your_password')\r\n", "", "\r\n", "", "# ", "specify ", "the ", "", "", "subreddit ", "to ", "", "search\r\n", "", "", "", "subreddit ", "= ", "", "", "", "", "", "", "", "", "", "", "", "reddit.subreddit('pics')\r\n", "", "\r\n", "", "# ", "specify ", "the ", "sorting ", "", "method\r\n", "", "", "", "top_posts ", "= ", "", "", "", "", "", "", "", "subreddit.top('all', ", "", "", "", "", "limit=50)\r\n", "", "\r\n", "", "# ", "download ", "the ", "top ", "", "", "50 ", "", "posts\r\n", "", "for ", "post ", "in ", "", "", "", "", "top_posts:\r\n", " ", " ", "# ", "check ", "if ", "post ", "has ", "a ", "URL ", "or ", "", "image\r\n", " ", " ", "if ", "", "", "", "", "", "", "", "", "", "post.url.startswith('http'):\r\n", " ", " ", "url ", "= ", "", "", "", "post.url\r\n", " ", " ", "elif ", "", "", "", "", "", "", "", "", "", "post.url.startswith('https'):\r\n", " ", " ", "url ", "= ", "", "", "", "post.url\r\n", " ", " ", "", "", "else:\r\n", " ", " ", "", "continue\r\n", "", "\r\n", " ", " ", "# ", "download ", "the ", "", "image\r\n", " ", " ", "response ", "= ", "", "", "", "", "", "requests.get(url)\r\n", " ", " ", "with ", "", "", "", "", "", "", "", "", "", "open(url.split('/')[-1], ", "", "", "", "'wb') ", "as ", "", "", "f:\r\n", " ", " ", "", "", "", "", "", "", "", "f.write(response.content)\r\n", "", "", "```\r\n", "", "\r\n", "", "This ", "script ", "", "initializes ", "", "", "PRAW ", "with ", "appropriate ", "", "credentials, ", "", "specifies ", "the ", "", "", "subreddit ", "to ", "", "search, ", "sorts ", "the ", "posts ", "by ", "", "", "all-time ", "", "top, ", "and ", "then ", "", "downloads ", "the ", "top ", "", "", "50 ", "posts ", "using ", "the ", "requests ", "", "library. ", "Make ", "sure ", "to ", "replace ", "the ", "", "", "", "client_id, ", "", "", "", "client_secret, ", "", "", "", "user_agent, ", "", "username, ", "and ", "password ", "with ", "your ", "own ", "", "values. ", "You ", "can ", "find ", "your ", "own ", "credentials ", "by ", "creating ", "a ", "new ", "", "", "PRAW ", "application ", "at ", "", "", "", "", "", "", "", "", "", "", "", "", "", "https://www.reddit.com/prefs/apps. ", "", "" ], "started_at": "2023-08-29T18:21:10.120968Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fxqx3vjbx4us5epbi5dv2nh5ei", "cancel": "https://api.replicate.com/v1/predictions/fxqx3vjbx4us5epbi5dv2nh5ei/cancel" }, "version": "7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b" }
Generated in/root/.pyenv/versions/3.8.18/lib/python3.8/site-packages/transformers/generation/utils.py:1411: 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 ) warnings.warn(
Prediction
nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85bIDjwomduzbdysk6tcaj6t2dqponmStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- top_k
- 50
- top_p
- 0.95
- message
- Write a script to download the past year's 10Q files for APPL from SEC EDGAR using edgarpy
- temperature
- 1
- max_new_tokens
- 1024
{ "top_k": 50, "top_p": 0.95, "message": "Write a script to download the past year's 10Q files for APPL from SEC EDGAR using edgarpy", "temperature": 1, "max_new_tokens": 1024 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run nateraw/wizardcoder-python-34b-v1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b", { input: { top_k: 50, top_p: 0.95, message: "Write a script to download the past year's 10Q files for APPL from SEC EDGAR using edgarpy", temperature: 1, max_new_tokens: 1024 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run nateraw/wizardcoder-python-34b-v1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b", input={ "top_k": 50, "top_p": 0.95, "message": "Write a script to download the past year's 10Q files for APPL from SEC EDGAR using edgarpy", "temperature": 1, "max_new_tokens": 1024 } ) # The nateraw/wizardcoder-python-34b-v1.0 model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/nateraw/wizardcoder-python-34b-v1.0/api#output-schema print(item, end="")
To learn more, take a look at the guide on getting started with Python.
Run nateraw/wizardcoder-python-34b-v1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b", "input": { "top_k": 50, "top_p": 0.95, "message": "Write a script to download the past year\'s 10Q files for APPL from SEC EDGAR using edgarpy", "temperature": 1, "max_new_tokens": 1024 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
To download the past year's 10Q files for APPL from SEC EDGAR using edgarpy, you can use the following script: ```python import edgar # Set the parameters for the filing search company = "Apple Inc." form_type = "10-Q" start_date = "2021-01-01" end_date = "2022-01-01" # Create the EDGAR object edgar_obj = edgar.Edgar() # Search for the filings filings = edgar_obj.filings(cik_lookup=company, form_type=form_type, start_date=start_date, end_date=end_date) # Download the filings for filing in filings: filing.download("10q_files") print("10Q Files downloaded successfully!") ``` In the above script, we first set the parameters for the filing search, such as the company name, form type, and date range. We then create an instance of the EDGAR object and use it to search for the filings that match our criteria. Once we have the filings, we loop through them and use the `download()` method to download each filing to a specified folder on our computer. Finally, we print a message to indicate that the downloads have completed successfully.{ "completed_at": "2023-08-29T18:29:05.059147Z", "created_at": "2023-08-29T18:26:25.538800Z", "data_removed": false, "error": null, "id": "jwomduzbdysk6tcaj6t2dqponm", "input": { "top_k": 50, "top_p": 0.95, "message": "Write a script to download the past year's 10Q files for APPL from SEC EDGAR using edgarpy", "temperature": 1, "max_new_tokens": 1024 }, "logs": "/root/.pyenv/versions/3.8.18/lib/python3.8/site-packages/transformers/generation/utils.py:1411: 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 )\nwarnings.warn(", "metrics": { "predict_time": 159.44567, "total_time": 159.520347 }, "output": [ "", "To ", "download ", "the ", "past ", "", "", "year's ", "", "", "", "10Q ", "files ", "for ", "", "APPL ", "from ", "", "SEC ", "", "", "EDGAR ", "using ", "", "", "", "edgarpy, ", "you ", "can ", "use ", "the ", "following ", "", "", "script:\r\n", "", "\r\n", "", "", "", "```python\r\n", "", "import ", "", "", "edgar\r\n", "", "\r\n", "", "# ", "Set ", "the ", "parameters ", "for ", "the ", "", "filing ", "", "search\r\n", "", "company ", "= ", "", "", "\"Apple ", "", "", "Inc.\"\r\n", "", "", "", "form_type ", "= ", "", "", "", "", "", "\"10-Q\"\r\n", "", "", "", "start_date ", "= ", "", "", "", "", "", "", "", "", "", "", "", "\"2021-01-01\"\r\n", "", "", "", "end_date ", "= ", "", "", "", "", "", "", "", "", "", "", "", "\"2022-01-01\"\r\n", "", "\r\n", "", "# ", "Create ", "the ", "", "", "EDGAR ", "", "object\r\n", "", "", "", "", "edgar_obj ", "= ", "", "", "", "", "", "edgar.Edgar()\r\n", "", "\r\n", "", "# ", "Search ", "for ", "the ", "", "", "filings\r\n", "", "", "filings ", "= ", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "edgar_obj.filings(cik_lookup=company,\r\n", " ", " ", " ", "", "", "", "", "", "", "", "form_type=form_type,\r\n", " ", " ", " ", "", "", "", "", "", "", "", "start_date=start_date,\r\n", " ", " ", " ", "", "", "", "", "", "", "", "end_date=end_date)\r\n", "", "\r\n", "", "# ", "Download ", "the ", "", "", "filings\r\n", "", "for ", "", "filing ", "in ", "", "", "", "filings:\r\n", " ", " ", "", "", "", "", "", "", "", "", "", "", "", "filing.download(\"10q_files\")\r\n", "", "\r\n", "", "", "", "", "", "print(\"10Q ", "Files ", "downloaded ", "", "", "", "successfully!\")\r\n", "", "", "```\r\n", "", "\r\n", "", "In ", "the ", "above ", "", "script, ", "we ", "first ", "set ", "the ", "parameters ", "for ", "the ", "", "filing ", "", "search, ", "such ", "as ", "the ", "company ", "", "name, ", "form ", "", "type, ", "and ", "date ", "", "range. ", "We ", "then ", "create ", "an ", "instance ", "of ", "the ", "", "", "EDGAR ", "object ", "and ", "use ", "it ", "to ", "search ", "for ", "the ", "", "filings ", "that ", "match ", "our ", "", "criteria.\r\n", "", "\r\n", "", "Once ", "we ", "have ", "the ", "", "", "filings, ", "we ", "loop ", "through ", "them ", "and ", "use ", "the ", "", "", "`download()` ", "method ", "to ", "download ", "each ", "", "filing ", "to ", "a ", "specified ", "folder ", "on ", "our ", "", "computer. ", "", "Finally, ", "we ", "print ", "a ", "message ", "to ", "indicate ", "that ", "the ", "", "downloads ", "have ", "completed ", "", "successfully. ", "", "" ], "started_at": "2023-08-29T18:26:25.613477Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jwomduzbdysk6tcaj6t2dqponm", "cancel": "https://api.replicate.com/v1/predictions/jwomduzbdysk6tcaj6t2dqponm/cancel" }, "version": "7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b" }
Generated in/root/.pyenv/versions/3.8.18/lib/python3.8/site-packages/transformers/generation/utils.py:1411: 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 ) warnings.warn(
Prediction
nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85bIDxthxburbd2lbmmzpcnavxhucjqStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- top_k
- 50
- top_p
- 0.95
- message
- Generate an SVG of an analog clock.
- temperature
- 1
- max_new_tokens
- 1024
{ "top_k": 50, "top_p": 0.95, "message": "Generate an SVG of an analog clock.", "temperature": 1, "max_new_tokens": 1024 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run nateraw/wizardcoder-python-34b-v1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b", { input: { top_k: 50, top_p: 0.95, message: "Generate an SVG of an analog clock.", temperature: 1, max_new_tokens: 1024 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run nateraw/wizardcoder-python-34b-v1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b", input={ "top_k": 50, "top_p": 0.95, "message": "Generate an SVG of an analog clock.", "temperature": 1, "max_new_tokens": 1024 } ) # The nateraw/wizardcoder-python-34b-v1.0 model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/nateraw/wizardcoder-python-34b-v1.0/api#output-schema print(item, end="")
To learn more, take a look at the guide on getting started with Python.
Run nateraw/wizardcoder-python-34b-v1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "nateraw/wizardcoder-python-34b-v1.0:7cccdb1f912482eb5b2f5671d23e6a6313ab0e101aa15a915ec777ed8521a85b", "input": { "top_k": 50, "top_p": 0.95, "message": "Generate an SVG of an analog clock.", "temperature": 1, "max_new_tokens": 1024 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
Sure, here's an SVG of an analog clock: ```html <svg viewBox="0 0 100 100"> <circle cx="50" cy="50" r="45" fill="none" stroke="#333" stroke-width="3"/> <circle cx="50" cy="50" r="5" fill="#333"/> <line x1="50" y1="50" x2="50" y2="45" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="47.5" y2="50" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="52.5" y2="50" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="50" y2="52.5" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="50" y2="47.5" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="49.38" y2="50.38" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="50.62" y2="50.38" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="50.38" y2="49.62" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="50.38" y2="50.62" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="50.62" y2="49.62" stroke="#333" stroke-width="2"/> <line x1="50" y1="50" x2="49.62" y2="50.62" stroke="#333" stroke-width="2"/> <g id="hour-hand"> <rect x="45" y="25" width="5" height="40" fill="none" stroke="#333" stroke-width="3"/> </g> <g id="minute-hand"> <rect x="47.5" y="25" width="3" height="40" fill="none" stroke="#333" stroke-width="2"/> </g> <g id="second-hand"> <rect x="48.75" y="25" width="1" height="40" fill="none" stroke="#f00" stroke-width="1"/> </g> </svg> ``` The clock is centered at 50,50 and has a radius of 45. The center dot is a circle with a radius of 5. The four lines on the edge are tick marks, while the lines at 45 degree intervals are hour markers. The hands are all represented as rectangles that are rotated and translated to their appropriate positions. The hour hand is 5 pixels wide, the minute hand is 3 pixels wide, and the second hand is 1 pixel wide. The stroke widths of the hands are adjusted to give them the desired thickness.{ "completed_at": "2023-08-29T22:08:48.799914Z", "created_at": "2023-08-29T21:54:34.626853Z", "data_removed": false, "error": null, "id": "xthxburbd2lbmmzpcnavxhucjq", "input": { "top_k": 50, "top_p": 0.95, "message": "Generate an SVG of an analog clock.", "temperature": 1, "max_new_tokens": 1024 }, "logs": "/root/.pyenv/versions/3.8.18/lib/python3.8/site-packages/transformers/generation/utils.py:1411: 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 )\nwarnings.warn(", "metrics": { "predict_time": 484.458383, "total_time": 854.173061 }, "output": [ "", "", "", "Sure, ", "", "", "here's ", "an ", "", "SVG ", "of ", "an ", "analog ", "", "", "clock:\r\n", "", "\r\n", "", "", "", "```html\r\n", "", "", "<svg ", "", "", "", "viewBox=\"0 ", "", "0 ", "", "", "", "100 ", "", "", "", "", "100\">\r\n", "", " ", "", "<circle ", "", "", "", "", "cx=\"50\" ", "", "", "", "", "cy=\"50\" ", "", "", "", "", "r=\"45\" ", "", "", "", "fill=\"none\" ", "", "", "", "", "", "stroke=\"#333\" ", "", "", "", "", "", "", "stroke-width=\"3\"/>\r\n", "", " ", "", "<circle ", "", "", "", "", "cx=\"50\" ", "", "", "", "", "cy=\"50\" ", "", "", "", "r=\"5\" ", "", "", "", "", "", "", "fill=\"#333\"/>\r\n", "", " ", "", "<line ", "", "", "", "", "", "x1=\"50\" ", "", "", "", "", "", "y1=\"50\" ", "", "", "", "", "", "x2=\"50\" ", "", "", "", 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Generated in/root/.pyenv/versions/3.8.18/lib/python3.8/site-packages/transformers/generation/utils.py:1411: 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 ) warnings.warn(
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