Row 2341

Row ID: 2341 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 2341 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I asked Claude 2 to provide an outline for generating a large set of coding prompts and responses from Code Llama - Instruct which could be used to train an LLM as a code interpreter and assistant. I had Claude use Stanford's Self-Instruct paper as a template and Meta's Code Llama paper as a resource for creating the procedural outline. Perhaps AI2's Dolma dataset could make a decent code interpreter with a good, large, and diverse set of coding related prompts and replies for training purposes?

Here is what Claude 2 recommended: "Here is an outline for generating a large set of prompts and responses to train a high quality code interpreting LLM assistant, using ideas from the Self-Instruct and Code Llama papers:

Introduction

- Brief background on instruction tuning of LLMs and goal of creating a code assistant

Generating Diverse Programming Prompts

- Use Code Llama to generate a wide variety of programming prompts covering different domains, formats, difficulty levels, etc. - Prompts can include code snippets, natural language questions about code, bug fixes, optimizations, documentation, etc. - Leverage ideas like diverse decoding, top-k sampling, nucleus sampling to increase diversity - Remove exact duplicates but maintain overall distribution

Generating Responses

- For each prompt, generate multiple possible responses using Code Llama - Instruct - Responses can include explanations, code completions, edits, documentation strings, etc. - Vary temperature and top-p to generate different candidate responses per prompt - Remove responses that are exact duplicates

Filtering Data

- Remove prompts and responses that contain unsafe content, biases, etc. using safety classifiers - Remove prompts that are too ambiguous or broad without a clear target response - Prioritize concise, natural prompts and responses

Training the Final Model

- Use the filtered prompt-response pairs to finetune a base LLM like Code Llama - Finetune with multiple prompt-response examples per training epoch - Evaluate on held-out human annotated data and iterate if needed

This overall pipeline should produce a large, diverse, high-quality set of prompt-response pairs that teach the LLM how to interpret and respond to natural language queries about code. The trained model can serve as an effective programming assistant." https://poe.com/s/xK6rOzf9Ssoq80CG5W6L

Field	Value
text	I asked Claude 2 to provide an outline for generating a large set of coding prompts and responses from Code Llama - Instruct which could be used to train an LLM as a code interpreter and assistant. I had Claude use Stanford's Self-Instruct paper as a template and Meta's Code Llama paper as a resource for creating the procedural outline. Perhaps AI2's Dolma dataset could make a decent code interpreter with a good, large, and diverse set of coding related prompts and replies for training purposes?…
label	r/opensourceai
dataType	post
communityName	r/OpenSourceAI
datetime	2023-08-28
username_encoded	Z0FBQUFBQm5LakwwU1VZbWJXU1lZaWxNTUloNE9BZ3k5c2d1TV9wd0x3SXNDQ3FkNFFJSGwyOVY2TG95OXJ1WkVIT21DU0VqNjZvbGl5VFh0ZzFSbG1nbVpscDdQdGJ5VFE9PQ==
url_encoded	Z0FBQUFBQm5Lak9FdV9wOW1HSE5MZzNoNWdwT1NMTVZMQ2FzVUZuLVFibGFROExNRWVqc240N1FRMTVHZE43MWFOT05pMTA0Z2d5Zy1PbE04Y19aYVktTVp3b0VsRm5xMTZtQWhFR0VnRGlkR3djSDdSeWFUTVcxeDhCaGtJUHB0X1Y2UEUtSFMwS2JFVEZibjZMSVp1UDVWdGhKdVloY1RJd2JQMWZBNEZZNW12RFVweS1oMDI1cmlJWkdXRW9qRmpWNC1LSkxteDZ6bzVNQ2pYZE9SUGxOZmotdno4SHZJQT09

Raw Record

{
  "text": "I asked Claude 2 to provide an outline for generating a large set of coding prompts and responses from Code Llama - Instruct which could be used to train an LLM as a code interpreter and assistant. I had Claude use Stanford's Self-Instruct paper as a template and Meta's Code Llama paper as a resource for creating the procedural outline. Perhaps AI2's Dolma dataset could make a decent code interpreter with a good, large, and diverse set of coding related prompts and replies for training purposes?\n\nHere is what Claude 2 recommended: \"Here is an outline for generating a large set of prompts and responses to train a high quality code interpreting LLM assistant, using ideas from the Self-Instruct and Code Llama papers:\n\nIntroduction\n\n- Brief background on instruction tuning of LLMs and goal of creating a code assistant\n\nGenerating Diverse Programming Prompts\n\n- Use Code Llama to generate a wide variety of programming prompts covering different domains, formats, difficulty levels, etc.\n- Prompts can include code snippets, natural language questions about code, bug fixes, optimizations, documentation, etc.\n- Leverage ideas like diverse decoding, top-k sampling, nucleus sampling to increase diversity\n- Remove exact duplicates but maintain overall distribution\n\nGenerating Responses\n\n- For each prompt, generate multiple possible responses using Code Llama - Instruct\n- Responses can include explanations, code completions, edits, documentation strings, etc.\n- Vary temperature and top-p to generate different candidate responses per prompt\n- Remove responses that are exact duplicates\n\nFiltering Data\n\n- Remove prompts and responses that contain unsafe content, biases, etc. using safety classifiers\n- Remove prompts that are too ambiguous or broad without a clear target response\n- Prioritize concise, natural prompts and responses\n\nTraining the Final Model\n\n- Use the filtered prompt-response pairs to finetune a base LLM like Code Llama\n- Finetune with multiple prompt-response examples per training epoch\n- Evaluate on held-out human annotated data and iterate if needed\n\nThis overall pipeline should produce a large, diverse, high-quality set of prompt-response pairs that teach the LLM how to interpret and respond to natural language queries about code. The trained model can serve as an effective programming assistant.\" https://poe.com/s/xK6rOzf9Ssoq80CG5W6L",
  "label": "r/opensourceai",
  "dataType": "post",
  "communityName": "r/OpenSourceAI",
  "datetime": "2023-08-28",
  "username_encoded": "Z0FBQUFBQm5LakwwU1VZbWJXU1lZaWxNTUloNE9BZ3k5c2d1TV9wd0x3SXNDQ3FkNFFJSGwyOVY2TG95OXJ1WkVIT21DU0VqNjZvbGl5VFh0ZzFSbG1nbVpscDdQdGJ5VFE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9FdV9wOW1HSE5MZzNoNWdwT1NMTVZMQ2FzVUZuLVFibGFROExNRWVqc240N1FRMTVHZE43MWFOT05pMTA0Z2d5Zy1PbE04Y19aYVktTVp3b0VsRm5xMTZtQWhFR0VnRGlkR3djSDdSeWFUTVcxeDhCaGtJUHB0X1Y2UEUtSFMwS2JFVEZibjZMSVp1UDVWdGhKdVloY1RJd2JQMWZBNEZZNW12RFVweS1oMDI1cmlJWkdXRW9qRmpWNC1LSkxteDZ6bzVNQ2pYZE9SUGxOZmotdno4SHZJQT09"
}

Explore Dataset Explore Row

Entry Information

Entry ID: 2341
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000