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توضیحات

Updated and improved implementation of the self-instruct system.

ویژگی	مقدار
سیستم عامل	-
نام فایل	airoboros-0.0.5
نام	airoboros
نسخه کتابخانه	0.0.5
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Jon Durbin
ایمیل نویسنده	-
آدرس صفحه اصلی	https://github.com/jondurbin/airoboros
آدرس اینترنتی	https://pypi.org/project/airoboros/
مجوز	Apache 2.0

# airoboros: using large language models to fine-tune large language models This is my take on implementing the [Self-Instruct paper](https://arxiv.org/abs/2212.10560). The approach is quite heavily modified, and uses the human generated seeds provided by [Databricks Dolly Project](https://huggingface.co/datasets/databricks/databricks-dolly-15k) This updated implementation supports either the /v1/completions endpoint or /v1/chat/completions, which is particularly useful in that it supports gpt-4 and gpt-3.5-turbo (which is 1/10 the cost of text-davinci-003). ## Key differences * Sample instructions in prompts by default use the human-generated seeds from Dolly. * Machine-generated instructions are not sampled for prompt examples, to avoid degredation. * Support for either /v1/completions or /v1/chat/completions APIs (which allows gpt-3.5-turbo instead of text-davinci-003, as well as gpt-4 if you have access). * In memory vector db (Chroma) for similarity comparison, which is much faster than calculating rouge score for each generated instruction. * (Seemingly) better prompt, which includes injection of random topics to relate the instructions to, which creates much more diverse prompts. * Multi-threaded producers/consumer implementation for significantly faster runtimes (generally 150+ unique prompts per minute, more initially since there are fewer duplicates, decreasing over time). * Tries to ensure the context, if provided, is relevant to the topic and contains all the information that would be necessary to respond to the instruction, and nost just a link to article/etc. * Generally speaking, this implementation tries to reduce some of the [noise](https://github.com/tloen/alpaca-lora/issues/65) ## Generating instructions See available options via: ``` airoboros generate-instructions --help ``` Example output: ``` usage: self_instruct.py [-h] [--model MODEL] [--organization-id ORGANIZATION_ID] [--openai-api-key OPENAI_API_KEY] [--instruction-count INSTRUCTION_COUNT] [--seed-tasks-path SEED_TASKS_PATH] [--output-path OUTPUT_PATH] [--overwrite] [--append] [--prompt PROMPT] [--skip-instruction-re SKIP_INSTRUCTION_RE] [--code-gen-re CODE_GEN_RE] [--samples-per-request SAMPLES_PER_REQUEST] [--min-instruction-length MIN_INSTRUCTION_LENGTH] [--max-instruction-length MAX_INSTRUCTION_LENGTH] [--temperature TEMPERATURE] [--top-p TOP_P] [--frequency-penalty FREQUENCY_PENALTY] [--presence-penalty PRESENCE_PENALTY] [--max-usage-tokens MAX_USAGE_TOKENS] [--prompt-generation-concurrency PROMPT_GENERATION_CONCURRENCY] [--min-docsearch-score MIN_DOCSEARCH_SCORE] options: -h, --help show this help message and exit --model MODEL OpenAI model/engine to use for prompt generation, which can be either part of the /v1/completions or /v1/chat/completions endpoints --organization-id ORGANIZATION_ID organization ID to include in the request to OpenAI, defaults to organization ID tied to the API key --openai-api-key OPENAI_API_KEY OpenAI API key to use, defaults to the OPENAI_API_KEY environment variable --instruction-count INSTRUCTION_COUNT number of instructions to generate, not including the seed instructions --seed-tasks-path SEED_TASKS_PATH path to an input seed instructions JSONL file --output-path OUTPUT_PATH path to store all generated instructions in --overwrite overwrite output path if it exists --append append to output path if it exists --prompt PROMPT prompt prefix to use for generating tasks --skip-instruction-re SKIP_INSTRUCTION_RE regular expression used to filter low-quality/unusable instructions --code-gen-re CODE_GEN_RE regular expression used to filter coding/programming tasks --samples-per-request SAMPLES_PER_REQUEST number of random sample instructions to include in prompts --min-instruction-length MIN_INSTRUCTION_LENGTH minimum instruction length --max-instruction-length MAX_INSTRUCTION_LENGTH maximum instruction length --temperature TEMPERATURE temperature parameter to use in OpenAI requests --top-p TOP_P top-p parameter to use in OpenAI requests --frequency-penalty FREQUENCY_PENALTY frequency penalty to use in OpenAI requests --presence-penalty PRESENCE_PENALTY presence penalty to use in OpenAI requests --max-usage-tokens MAX_USAGE_TOKENS Maximum token usage, calculated as sum of total_tokens from responses --prompt-generation-concurrency PROMPT_GENERATION_CONCURRENCY Number of concurrent prompt generation threads/requests to use --min-docsearch-score MIN_DOCSEARCH_SCORE ``` ## Coming soon Scripts for fine-tuning various models using the self-instruct (and human-generated) prompts.

نیازمندی

مقدار	نام
>=0.1.2	rouge-score
>=3.8	aiohttp
>=2.2	backoff
>=2.28	requests
>=0.7	loguru
>=0.3.21	chromadb
>=0.0.155	langchain
>=2.2.2	sentence-transformers
-	black
-	flake8

نحوه نصب

نصب پکیج whl airoboros-0.0.5:

pip install airoboros-0.0.5.whl

نصب پکیج tar.gz airoboros-0.0.5:

pip install airoboros-0.0.5.tar.gz