Datasets:

carlosug

/

ReadmeInstallationPlan

like 0

[ [ "window", "coming", "soon", "!", ",", "install", "ollama", "window", "via", "wsl2", "." ], [ "window coming soon !", ", install ollama window via wsl2 ." ] ]

[ [ "window", "coming", "soon", "!", ",", "install", "ollama", "window", "via", "wsl2", "." ], [ "window coming soon !", ", install ollama window via wsl2 ." ] ]

[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]

[ [ "linux", "&", "wsl2", "``", "`", "curl", "http", ":", "//ollama.ai/install.sh", "|", "sh", "``", "`", "[", "manual", "install", "instruction", "]", "(", "http", ":", "//github.com/jmorganca/ollama/blob/main/docs/linux.md", ")" ], [ "linux & wsl2 `` ` curl http : //ollama.ai/install.sh | sh `` ` [ manual install instruction ] ( http : //github.com/jmorganca/ollama/blob/main/docs/linux.md )" ] ]

[ [ "linux", "&", "wsl2", "``", "`", "curl", "http", ":", "//ollama.ai/install.sh", "|", "sh", "``", "`", "[", "manual", "install", "instruction", "]", "(", "http", ":", "//github.com/jmorganca/ollama/blob/main/docs/linux.md", ")" ], [ "linux & wsl2 `` ` curl http : //ollama.ai/install.sh | sh `` ` [ manual install instruction ] ( http : //github.com/jmorganca/ollama/blob/main/docs/linux.md )" ] ]

[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]

[ [ "building", "install", "`", "cmake", "`", "`", "go", "`", ":", "``", "`", "brew", "install", "cmake", "go", "``", "`", "generate", "dependency", ":", "``", "`", "go", "generate", "./", "...", "``", "`", "build", "binary", ":", "``", "`", "go", "build", ".", "``", "`", "detailed", "instruction", "found", "[", "developer", "guide", "]", "(", "http", ":", "//github.com/jmorganca/ollama/blob/main/docs/development.md", ")" ], [ "building install ` cmake ` ` go ` : `` ` brew install cmake go `` ` generate dependency : `` ` go generate ./ ... `` ` build binary : `` ` go build .", "`` ` detailed instruction found [ developer guide ] ( http : //github.com/jmorganca/ollama/blob/main/docs/development.md )" ] ]

[ [ "building", "install", "`", "cmake", "`", "`", "go", "`", ":", "``", "`", "brew", "install", "cmake", "go", "``", "`", "generate", "dependency", ":", "``", "`", "go", "generate", "./", "...", "``", "`", "build", "binary", ":", "``", "`", "go", "build", ".", "``", "`", "detailed", "instruction", "found", "[", "developer", "guide", "]", "(", "http", ":", "//github.com/jmorganca/ollama/blob/main/docs/development.md", ")" ], [ "building install ` cmake ` ` go ` : `` ` brew install cmake go `` ` generate dependency : `` ` go generate ./ ... `` ` build binary : `` ` go build .", "`` ` detailed instruction found [ developer guide ] ( http : //github.com/jmorganca/ollama/blob/main/docs/development.md )" ] ]

[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]

[ [ "extension", "&", "plugins", "-", "[", "raycast", "extension", "]", "(", "http", ":", "//github.com/massimilianopasquini97/raycast_ollama", ")", "-", "[", "discollama", "]", "(", "http", ":", "//github.com/mxyng/discollama", ")", "(", "discord", "bot", "inside", "ollama", "discord", "channel", ")", "-", "[", "continue", "]", "(", "http", ":", "//github.com/continuedev/continue", ")", "-", "[", "obsidian", "ollama", "plugin", "]", "(", "http", ":", "//github.com/hinterdupfinger/obsidian-ollama", ")", "-", "[", "logseq", "ollama", "plugin", "]", "(", "http", ":", "//github.com/omagdy7/ollama-logseq", ")", "-", "[", "dagger", "chatbot", "]", "(", "http", ":", "//github.com/samalba/dagger-chatbot", ")", "-", "[", "discord", "ai", "bot", "]", "(", "http", ":", "//github.com/mekb-turtle/discord-ai-bot", ")", "-", "[", "ollama", "telegram", "bot", "]", "(", "http", ":", "//github.com/ruecat/ollama-telegram", ")", "-", "[", "ha", "ollama", "conversation", "]", "(", "http", ":", "//github.com/ej52/hass-ollama-conversation", ")", "-", "[", "rivet", "plugin", "]", "(", "http", ":", "//github.com/abrenneke/rivet-plugin-ollama", ")", "-", "[", "llama", "coder", "]", "(", "http", ":", "//github.com/ex3ndr/llama-coder", ")", "(", "copilot", "alternative", "using", "ollama", ")", "-", "[", "obsidian", "bmo", "chatbot", "plugin", "]", "(", "http", ":", "//github.com/longy2k/obsidian-bmo-chatbot", ")", "-", "[", "open", "interpreter", "]", "(", "http", ":", "//docs.openinterpreter.com/language-model-setup/local-models/ollama", ")", "-", "[", "twinny", "]", "(", "http", ":", "//github.com/rjmacarthy/twinny", ")", "(", "copilot", "copilot", "chat", "alternative", "using", "ollama", ")", "-", "[", "wingman-ai", "]", "(", "http", ":", "//github.com/russellcanfield/wingman-ai", ")", "(", "copilot", "code", "chat", "alternative", "using", "ollama", "huggingface", ")" ], [ "extension & plugins - [ raycast extension ] ( http : //github.com/massimilianopasquini97/raycast_ollama ) - [ discollama ] ( http : //github.com/mxyng/discollama ) ( discord bot inside ollama discord channel ) - [ continue ] ( http : //github.com/continuedev/continue ) - [ obsidian ollama plugin ] ( http : //github.com/hinterdupfinger/obsidian-ollama ) - [ logseq ollama plugin ] ( http : //github.com/omagdy7/ollama-logseq ) - [ dagger chatbot ] ( http : //github.com/samalba/dagger-chatbot ) - [ discord ai bot ] ( http : //github.com/mekb-turtle/discord-ai-bot ) - [ ollama telegram bot ] ( http : //github.com/ruecat/ollama-telegram ) - [ ha ollama conversation ] ( http : //github.com/ej52/hass-ollama-conversation ) - [ rivet plugin ] ( http : //github.com/abrenneke/rivet-plugin-ollama ) - [ llama coder ] ( http : //github.com/ex3ndr/llama-coder ) ( copilot alternative using ollama ) - [ obsidian bmo chatbot plugin ] ( http : //github.com/longy2k/obsidian-bmo-chatbot ) - [ open interpreter ] ( http : //docs.openinterpreter.com/language-model-setup/local-models/ollama ) - [ twinny ] ( http : //github.com/rjmacarthy/twinny ) ( copilot copilot chat alternative using ollama ) - [ wingman-ai ] ( http : //github.com/russellcanfield/wingman-ai ) ( copilot code chat alternative using ollama huggingface )" ] ]

[ [ "extension", "&", "plugins", "-", "[", "raycast", "extension", "]", "(", "http", ":", "//github.com/massimilianopasquini97/raycast_ollama", ")", "-", "[", "discollama", "]", "(", "http", ":", "//github.com/mxyng/discollama", ")", "(", "discord", "bot", "inside", "ollama", "discord", "channel", ")", "-", "[", "continue", "]", "(", "http", ":", "//github.com/continuedev/continue", ")", "-", "[", "obsidian", "ollama", "plugin", "]", "(", "http", ":", "//github.com/hinterdupfinger/obsidian-ollama", ")", "-", "[", "logseq", "ollama", "plugin", "]", "(", "http", ":", "//github.com/omagdy7/ollama-logseq", ")", "-", "[", "dagger", "chatbot", "]", "(", "http", ":", "//github.com/samalba/dagger-chatbot", ")", "-", "[", "discord", "ai", "bot", "]", "(", "http", ":", "//github.com/mekb-turtle/discord-ai-bot", ")", "-", "[", "ollama", "telegram", "bot", "]", "(", "http", ":", "//github.com/ruecat/ollama-telegram", ")", "-", "[", "ha", "ollama", "conversation", "]", "(", "http", ":", "//github.com/ej52/hass-ollama-conversation", ")", "-", "[", "rivet", "plugin", "]", "(", "http", ":", "//github.com/abrenneke/rivet-plugin-ollama", ")", "-", "[", "llama", "coder", "]", "(", "http", ":", "//github.com/ex3ndr/llama-coder", ")", "(", "copilot", "alternative", "using", "ollama", ")", "-", "[", "obsidian", "bmo", "chatbot", "plugin", "]", "(", "http", ":", "//github.com/longy2k/obsidian-bmo-chatbot", ")", "-", "[", "open", "interpreter", "]", "(", "http", ":", "//docs.openinterpreter.com/language-model-setup/local-models/ollama", ")", "-", "[", "twinny", "]", "(", "http", ":", "//github.com/rjmacarthy/twinny", ")", "(", "copilot", "copilot", "chat", "alternative", "using", "ollama", ")", "-", "[", "wingman-ai", "]", "(", "http", ":", "//github.com/russellcanfield/wingman-ai", ")", "(", "copilot", "code", "chat", "alternative", "using", "ollama", "huggingface", ")" ], [ "extension & plugins - [ raycast extension ] ( http : //github.com/massimilianopasquini97/raycast_ollama ) - [ discollama ] ( http : //github.com/mxyng/discollama ) ( discord bot inside ollama discord channel ) - [ continue ] ( http : //github.com/continuedev/continue ) - [ obsidian ollama plugin ] ( http : //github.com/hinterdupfinger/obsidian-ollama ) - [ logseq ollama plugin ] ( http : //github.com/omagdy7/ollama-logseq ) - [ dagger chatbot ] ( http : //github.com/samalba/dagger-chatbot ) - [ discord ai bot ] ( http : //github.com/mekb-turtle/discord-ai-bot ) - [ ollama telegram bot ] ( http : //github.com/ruecat/ollama-telegram ) - [ ha ollama conversation ] ( http : //github.com/ej52/hass-ollama-conversation ) - [ rivet plugin ] ( http : //github.com/abrenneke/rivet-plugin-ollama ) - [ llama coder ] ( http : //github.com/ex3ndr/llama-coder ) ( copilot alternative using ollama ) - [ obsidian bmo chatbot plugin ] ( http : //github.com/longy2k/obsidian-bmo-chatbot ) - [ open interpreter ] ( http : //docs.openinterpreter.com/language-model-setup/local-models/ollama ) - [ twinny ] ( http : //github.com/rjmacarthy/twinny ) ( copilot copilot chat alternative using ollama ) - [ wingman-ai ] ( http : //github.com/russellcanfield/wingman-ai ) ( copilot code chat alternative using ollama huggingface )" ] ]

[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]

[ [ "install" ], [ "install" ] ]

[ [ "install" ], [ "install" ] ]

[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

[ [ "pip", "installation", ">", "ensure", "python", "3.9+", "installed", "system", ".", "check", "using", ":", "`", "python", "--", "version", "`", ".", ">", "use", "conda", "like", ":", "`", "conda", "create", "-n", "metagpt", "python=3.9", "&", "&", "conda", "activate", "metagpt", "`", "``", "`", "bash", "pip", "install", "metagpt", "metagpt", "--", "init-config" ], [ "pip installation > ensure python 3.9+ installed system .", "check using : ` python -- version ` .", "> use conda like : ` conda create -n metagpt python=3.9 & & conda activate metagpt ` `` ` bash pip install metagpt metagpt -- init-config" ] ]

[ [ "pip", "installation", ">", "ensure", "python", "3.9+", "installed", "system", ".", "check", "using", ":", "`", "python", "--", "version", "`", ".", ">", "use", "conda", "like", ":", "`", "conda", "create", "-n", "metagpt", "python=3.9", "&", "&", "conda", "activate", "metagpt", "`", "``", "`", "bash", "pip", "install", "metagpt", "metagpt", "--", "init-config" ], [ "pip installation > ensure python 3.9+ installed system .", "check using : ` python -- version ` .", "> use conda like : ` conda create -n metagpt python=3.9 & & conda activate metagpt ` `` ` bash pip install metagpt metagpt -- init-config" ] ]

[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

[ [ "print", "repo", "structure", "file", "``", "`", "detail", "installation", "please", "refer", "[", "cli_install", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/get_started/installation.html", "#", "install-stable-version", ")" ], [ "print repo structure file `` ` detail installation please refer [ cli_install ] ( http : //docs.deepwisdom.ai/main/en/guide/get_started/installation.html # install-stable-version )" ] ]

[ [ "print", "repo", "structure", "file", "``", "`", "detail", "installation", "please", "refer", "[", "cli_install", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/get_started/installation.html", "#", "install-stable-version", ")" ], [ "print repo structure file `` ` detail installation please refer [ cli_install ] ( http : //docs.deepwisdom.ai/main/en/guide/get_started/installation.html # install-stable-version )" ] ]

[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

[ [ "docker", "installation", ">", "note", ":", "window", ",", "need", "replace", "``", "/opt/metagpt", "''", "directory", "docker", "permission", "create", ",", "``", ":", "\\users\\x\\metagpt", "''", "``", "`", "bash" ], [ "docker installation > note : window , need replace `` /opt/metagpt '' directory docker permission create , `` : \\users\\x\\metagpt '' `` ` bash" ] ]

[ [ "docker", "installation", ">", "note", ":", "window", ",", "need", "replace", "``", "/opt/metagpt", "''", "directory", "docker", "permission", "create", ",", "``", ":", "\\users\\x\\metagpt", "''", "``", "`", "bash" ], [ "docker installation > note : window , need replace `` /opt/metagpt '' directory docker permission create , `` : \\users\\x\\metagpt '' `` ` bash" ] ]

[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

[ [ "step", "2", ":", "run", "metagpt", "demo", "container", "docker", "run", "--", "rm", "\\", "--", "privileged", "\\", "-v", "/opt/metagpt/config/config2.yaml", ":", "/app/metagpt/config/config2.yaml", "\\", "-v", "/opt/metagpt/workspace", ":", "/app/metagpt/workspace", "\\", "metagpt/metagpt", ":", "latest", "\\", "metagpt", "``", "create", "2048", "game", "''", "``", "`", "detail", "installation", "please", "refer", "[", "docker_install", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/get_started/installation.html", "#", "install-with-docker", ")" ], [ "step 2 : run metagpt demo container docker run -- rm \\ -- privileged \\ -v /opt/metagpt/config/config2.yaml : /app/metagpt/config/config2.yaml \\ -v /opt/metagpt/workspace : /app/metagpt/workspace \\ metagpt/metagpt : latest \\ metagpt `` create 2048 game '' `` ` detail installation please refer [ docker_install ] ( http : //docs.deepwisdom.ai/main/en/guide/get_started/installation.html # install-with-docker )" ] ]

[ [ "step", "2", ":", "run", "metagpt", "demo", "container", "docker", "run", "--", "rm", "\\", "--", "privileged", "\\", "-v", "/opt/metagpt/config/config2.yaml", ":", "/app/metagpt/config/config2.yaml", "\\", "-v", "/opt/metagpt/workspace", ":", "/app/metagpt/workspace", "\\", "metagpt/metagpt", ":", "latest", "\\", "metagpt", "``", "create", "2048", "game", "''", "``", "`", "detail", "installation", "please", "refer", "[", "docker_install", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/get_started/installation.html", "#", "install-with-docker", ")" ], [ "step 2 : run metagpt demo container docker run -- rm \\ -- privileged \\ -v /opt/metagpt/config/config2.yaml : /app/metagpt/config/config2.yaml \\ -v /opt/metagpt/workspace : /app/metagpt/workspace \\ metagpt/metagpt : latest \\ metagpt `` create 2048 game '' `` ` detail installation please refer [ docker_install ] ( http : //docs.deepwisdom.ai/main/en/guide/get_started/installation.html # install-with-docker )" ] ]

[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

[ [ "quickstart", "&", "demo", "video", "-", "try", "[", "metagpt", "huggingface", "space", "]", "(", "http", ":", "//huggingface.co/spaces/deepwisdom/metagpt", ")", "-", "[", "matthew", "berman", ":", "install", "metagpt", "-", "build", "startup", "one", "prompt", "!", "!", "]", "(", "http", ":", "//youtu.be/ut75j_kg_ay", ")", "-", "[", "official", "demo", "video", "]", "(", "http", ":", "//github.com/geekan/metagpt/assets/2707039/5e8c1062-8c35-440f-bb20-2b0320f8d27d", ")", "http", ":", "//github.com/geekan/metagpt/assets/34952977/34345016-5d13-489d-b9f9-b82ace413419" ], [ "quickstart & demo video - try [ metagpt huggingface space ] ( http : //huggingface.co/spaces/deepwisdom/metagpt ) - [ matthew berman : install metagpt - build startup one prompt ! !", "] ( http : //youtu.be/ut75j_kg_ay ) - [ official demo video ] ( http : //github.com/geekan/metagpt/assets/2707039/5e8c1062-8c35-440f-bb20-2b0320f8d27d ) http : //github.com/geekan/metagpt/assets/34952977/34345016-5d13-489d-b9f9-b82ace413419" ] ]

[ [ "quickstart", "&", "demo", "video", "-", "try", "[", "metagpt", "huggingface", "space", "]", "(", "http", ":", "//huggingface.co/spaces/deepwisdom/metagpt", ")", "-", "[", "matthew", "berman", ":", "install", "metagpt", "-", "build", "startup", "one", "prompt", "!", "!", "]", "(", "http", ":", "//youtu.be/ut75j_kg_ay", ")", "-", "[", "official", "demo", "video", "]", "(", "http", ":", "//github.com/geekan/metagpt/assets/2707039/5e8c1062-8c35-440f-bb20-2b0320f8d27d", ")", "http", ":", "//github.com/geekan/metagpt/assets/34952977/34345016-5d13-489d-b9f9-b82ace413419" ], [ "quickstart & demo video - try [ metagpt huggingface space ] ( http : //huggingface.co/spaces/deepwisdom/metagpt ) - [ matthew berman : install metagpt - build startup one prompt ! !", "] ( http : //youtu.be/ut75j_kg_ay ) - [ official demo video ] ( http : //github.com/geekan/metagpt/assets/2707039/5e8c1062-8c35-440f-bb20-2b0320f8d27d ) http : //github.com/geekan/metagpt/assets/34952977/34345016-5d13-489d-b9f9-b82ace413419" ] ]

[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

[ [ "tutorial", "-", "🗒", "[", "online", "document", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/", ")", "-", "💻", "[", "usage", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/get_started/quickstart.html", ")", "-", "🔎", "[", "metagpt", "?", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/get_started/introduction.html", ")", "-", "🛠", "build", "agent", "?", "-", "[", "metagpt", "usage", "&", "development", "guide", "|", "agent", "101", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/tutorials/agent_101.html", ")", "-", "[", "metagpt", "usage", "&", "development", "guide", "|", "multiagent", "101", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/tutorials/multi_agent_101.html", ")", "-", "🧑‍💻", "contribution", "-", "[", "develop", "roadmap", "]", "(", "docs/roadmap.md", ")", "-", "🔖", "use", "case", "-", "[", "debate", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/use_cases/multi_agent/debate.html", ")", "-", "[", "researcher", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/use_cases/agent/researcher.html", ")", "-", "[", "recepit", "assistant", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/use_cases/agent/receipt_assistant.html", ")", "-", "❓", "[", "faq", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/faq.html", ")" ], [ "tutorial - 🗒 [ online document ] ( http : //docs.deepwisdom.ai/main/en/ ) - 💻 [ usage ] ( http : //docs.deepwisdom.ai/main/en/guide/get_started/quickstart.html ) - 🔎 [ metagpt ?", "] ( http : //docs.deepwisdom.ai/main/en/guide/get_started/introduction.html ) - 🛠 build agent ?", "- [ metagpt usage & development guide | agent 101 ] ( http : //docs.deepwisdom.ai/main/en/guide/tutorials/agent_101.html ) - [ metagpt usage & development guide | multiagent 101 ] ( http : //docs.deepwisdom.ai/main/en/guide/tutorials/multi_agent_101.html ) - 🧑‍💻 contribution - [ develop roadmap ] ( docs/roadmap.md ) - 🔖 use case - [ debate ] ( http : //docs.deepwisdom.ai/main/en/guide/use_cases/multi_agent/debate.html ) - [ researcher ] ( http : //docs.deepwisdom.ai/main/en/guide/use_cases/agent/researcher.html ) - [ recepit assistant ] ( http : //docs.deepwisdom.ai/main/en/guide/use_cases/agent/receipt_assistant.html ) - ❓ [ faq ] ( http : //docs.deepwisdom.ai/main/en/guide/faq.html )" ] ]

[ [ "tutorial", "-", "🗒", "[", "online", "document", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/", ")", "-", "💻", "[", "usage", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/get_started/quickstart.html", ")", "-", "🔎", "[", "metagpt", "?", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/get_started/introduction.html", ")", "-", "🛠", "build", "agent", "?", "-", "[", "metagpt", "usage", "&", "development", "guide", "|", "agent", "101", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/tutorials/agent_101.html", ")", "-", "[", "metagpt", "usage", "&", "development", "guide", "|", "multiagent", "101", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/tutorials/multi_agent_101.html", ")", "-", "🧑‍💻", "contribution", "-", "[", "develop", "roadmap", "]", "(", "docs/roadmap.md", ")", "-", "🔖", "use", "case", "-", "[", "debate", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/use_cases/multi_agent/debate.html", ")", "-", "[", "researcher", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/use_cases/agent/researcher.html", ")", "-", "[", "recepit", "assistant", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/use_cases/agent/receipt_assistant.html", ")", "-", "❓", "[", "faq", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/faq.html", ")" ], [ "tutorial - 🗒 [ online document ] ( http : //docs.deepwisdom.ai/main/en/ ) - 💻 [ usage ] ( http : //docs.deepwisdom.ai/main/en/guide/get_started/quickstart.html ) - 🔎 [ metagpt ?", "] ( http : //docs.deepwisdom.ai/main/en/guide/get_started/introduction.html ) - 🛠 build agent ?", "- [ metagpt usage & development guide | agent 101 ] ( http : //docs.deepwisdom.ai/main/en/guide/tutorials/agent_101.html ) - [ metagpt usage & development guide | multiagent 101 ] ( http : //docs.deepwisdom.ai/main/en/guide/tutorials/multi_agent_101.html ) - 🧑‍💻 contribution - [ develop roadmap ] ( docs/roadmap.md ) - 🔖 use case - [ debate ] ( http : //docs.deepwisdom.ai/main/en/guide/use_cases/multi_agent/debate.html ) - [ researcher ] ( http : //docs.deepwisdom.ai/main/en/guide/use_cases/agent/researcher.html ) - [ recepit assistant ] ( http : //docs.deepwisdom.ai/main/en/guide/use_cases/agent/receipt_assistant.html ) - ❓ [ faq ] ( http : //docs.deepwisdom.ai/main/en/guide/faq.html )" ] ]

[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

[ [ "💻", "example", "usage", "``", "`", "pip", "install", "llama-index", "``", "`", "example", "`", "example", "`", "folder", ".", "index", "`", "index", "`", "folder", "(", "see", "list", "index", ")", ".", "build", "simple", "vector", "store", "index", "using", "openai", ":", "``", "`", "python", "import", "o", "os.environ", "[", "``", "openai_api_key", "''", "]", "=", "``", "your_openai_api_key", "''", "llama_index", "import", "vectorstoreindex", ",", "simpledirectoryreader", "document", "=", "simpledirectoryreader", "(", "``", "your_data_directory", "''", ")", ".load_data", "(", ")", "index", "=", "vectorstoreindex.from_documents", "(", "document", ")", "``", "`", "build", "simple", "vector", "store", "index", "using", "non-openai", "llm", ",", "e.g", ".", "llama", "2", "hosted", "[", "replicate", "]", "(", "http", ":", "//replicate.com/", ")", ",", "easily", "create", "free", "trial", "api", "token", ":", "``", "`", "python", "import", "o", "os.environ", "[", "``", "replicate_api_token", "''", "]", "=", "``", "your_replicate_api_token", "''", "llama_index.llms", "import", "replicate", "llama2_7b_chat", "=", "``", "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e", "''", "llm", "=", "replicate", "(", "model=llama2_7b_chat", ",", "temperature=0.01", ",", "additional_kwargs=", "{", "``", "top_p", "''", ":", "1", ",", "``", "max_new_tokens", "''", ":", "300", "}", ",", ")" ], [ "💻 example usage `` ` pip install llama-index `` ` example ` example ` folder .", "index ` index ` folder ( see list index ) .", "build simple vector store index using openai : `` ` python import o os.environ [ `` openai_api_key '' ] = `` your_openai_api_key '' llama_index import vectorstoreindex , simpledirectoryreader document = simpledirectoryreader ( `` your_data_directory '' ) .load_data ( ) index = vectorstoreindex.from_documents ( document ) `` ` build simple vector store index using non-openai llm , e.g .", "llama 2 hosted [ replicate ] ( http : //replicate.com/ ) , easily create free trial api token : `` ` python import o os.environ [ `` replicate_api_token '' ] = `` your_replicate_api_token '' llama_index.llms import replicate llama2_7b_chat = `` meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e '' llm = replicate ( model=llama2_7b_chat , temperature=0.01 , additional_kwargs= { `` top_p '' : 1 , `` max_new_tokens '' : 300 } , )" ] ]

[ [ "💻", "example", "usage", "``", "`", "pip", "install", "llama-index", "``", "`", "example", "`", "example", "`", "folder", ".", "index", "`", "index", "`", "folder", "(", "see", "list", "index", ")", ".", "build", "simple", "vector", "store", "index", "using", "openai", ":", "``", "`", "python", "import", "o", "os.environ", "[", "``", "openai_api_key", "''", "]", "=", "``", "your_openai_api_key", "''", "llama_index", "import", "vectorstoreindex", ",", "simpledirectoryreader", "document", "=", "simpledirectoryreader", "(", "``", "your_data_directory", "''", ")", ".load_data", "(", ")", "index", "=", "vectorstoreindex.from_documents", "(", "document", ")", "``", "`", "build", "simple", "vector", "store", "index", "using", "non-openai", "llm", ",", "e.g", ".", "llama", "2", "hosted", "[", "replicate", "]", "(", "http", ":", "//replicate.com/", ")", ",", "easily", "create", "free", "trial", "api", "token", ":", "``", "`", "python", "import", "o", "os.environ", "[", "``", "replicate_api_token", "''", "]", "=", "``", "your_replicate_api_token", "''", "llama_index.llms", "import", "replicate", "llama2_7b_chat", "=", "``", "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e", "''", "llm", "=", "replicate", "(", "model=llama2_7b_chat", ",", "temperature=0.01", ",", "additional_kwargs=", "{", "``", "top_p", "''", ":", "1", ",", "``", "max_new_tokens", "''", ":", "300", "}", ",", ")" ], [ "💻 example usage `` ` pip install llama-index `` ` example ` example ` folder .", "index ` index ` folder ( see list index ) .", "build simple vector store index using openai : `` ` python import o os.environ [ `` openai_api_key '' ] = `` your_openai_api_key '' llama_index import vectorstoreindex , simpledirectoryreader document = simpledirectoryreader ( `` your_data_directory '' ) .load_data ( ) index = vectorstoreindex.from_documents ( document ) `` ` build simple vector store index using non-openai llm , e.g .", "llama 2 hosted [ replicate ] ( http : //replicate.com/ ) , easily create free trial api token : `` ` python import o os.environ [ `` replicate_api_token '' ] = `` your_replicate_api_token '' llama_index.llms import replicate llama2_7b_chat = `` meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e '' llm = replicate ( model=llama2_7b_chat , temperature=0.01 , additional_kwargs= { `` top_p '' : 1 , `` max_new_tokens '' : 300 } , )" ] ]

[ "agents", "application", "data", "fine-tuning", "framework", "llamaindex", "llm", "rag", "vector-database" ]

[ [ "🔧", "dependency", "main", "third-party", "package", "requirement", "`", "tiktoken", "`", ",", "`", "openai", "`", ",", "`", "langchain", "`", ".", "requirement", "contained", "within", "`", "setup.py", "`", "file", ".", "run", "package", "locally", "without", "building", "wheel", ",", "simply", "run", ":", "``", "`", "bash", "pip", "install", "poetry", "poetry", "install", "--", "dev", "``", "`" ], [ "🔧 dependency main third-party package requirement ` tiktoken ` , ` openai ` , ` langchain ` .", "requirement contained within ` setup.py ` file .", "run package locally without building wheel , simply run : `` ` bash pip install poetry poetry install -- dev `` `" ] ]

[ [ "🔧", "dependency", "main", "third-party", "package", "requirement", "`", "tiktoken", "`", ",", "`", "openai", "`", ",", "`", "langchain", "`", ".", "requirement", "contained", "within", "`", "setup.py", "`", "file", ".", "run", "package", "locally", "without", "building", "wheel", ",", "simply", "run", ":", "``", "`", "bash", "pip", "install", "poetry", "poetry", "install", "--", "dev", "``", "`" ], [ "🔧 dependency main third-party package requirement ` tiktoken ` , ` openai ` , ` langchain ` .", "requirement contained within ` setup.py ` file .", "run package locally without building wheel , simply run : `` ` bash pip install poetry poetry install -- dev `` `" ] ]

[ "agents", "application", "data", "fine-tuning", "framework", "llamaindex", "llm", "rag", "vector-database" ]

[ [ "getting", "started", "🚀", "follow", "instruction", "get", "copy", "project", "running", "local", "machine", "development", "testing", "purpose", ".", "find", "everything", "[", "documentation", "]", "(", "http", ":", "//docs.quivr.app/", ")", "." ], [ "getting started 🚀 follow instruction get copy project running local machine development testing purpose .", "find everything [ documentation ] ( http : //docs.quivr.app/ ) ." ] ]

[ [ "getting", "started", "🚀", "follow", "instruction", "get", "copy", "project", "running", "local", "machine", "development", "testing", "purpose", ".", "find", "everything", "[", "documentation", "]", "(", "http", ":", "//docs.quivr.app/", ")", "." ], [ "getting started 🚀 follow instruction get copy project running local machine development testing purpose .", "find everything [ documentation ] ( http : //docs.quivr.app/ ) ." ] ]

[ "ai", "api", "chatbot", "chatgpt", "database", "docker", "frontend", "html", "javascript", "llm", "openai", "postgresql", "privacy", "rag", "react", "rest-api", "security", "typescript", "vector", "ycombinator" ]

[ [ "prerequisite", "📋", "ensure", "following", "installed", ":", "-", "docker", "-", "docker", "compose" ], [ "prerequisite 📋 ensure following installed : - docker - docker compose" ] ]

[ [ "prerequisite", "📋", "ensure", "following", "installed", ":", "-", "docker", "-", "docker", "compose" ], [ "prerequisite 📋 ensure following installed : - docker - docker compose" ] ]

[ "ai", "api", "chatbot", "chatgpt", "database", "docker", "frontend", "html", "javascript", "llm", "openai", "postgresql", "privacy", "rag", "react", "rest-api", "security", "typescript", "vector", "ycombinator" ]

[ [ "60", "second", "installation", "💽", "find", "installation", "video", "[", "]", "(", "http", ":", "//www.youtube.com/watch", "?", "v=cxba6dzjn48", ")", ".", "-", "*", "*", "step", "0", "*", "*", ":", "supabase", "cli", "follow", "instruction", "[", "]", "(", "http", ":", "//supabase.com/docs/guides/cli/getting-started", ")", "install", "supabase", "cli", "required", ".", "``", "`", "bash", "supabase", "-v" ], [ "60 second installation 💽 find installation video [ ] ( http : //www.youtube.com/watch ? v=cxba6dzjn48 ) .", "- * * step 0 * * : supabase cli follow instruction [ ] ( http : //supabase.com/docs/guides/cli/getting-started ) install supabase cli required .", "`` ` bash supabase -v" ] ]

[ [ "60", "second", "installation", "💽", "find", "installation", "video", "[", "]", "(", "http", ":", "//www.youtube.com/watch", "?", "v=cxba6dzjn48", ")", ".", "-", "*", "*", "step", "0", "*", "*", ":", "supabase", "cli", "follow", "instruction", "[", "]", "(", "http", ":", "//supabase.com/docs/guides/cli/getting-started", ")", "install", "supabase", "cli", "required", ".", "``", "`", "bash", "supabase", "-v" ], [ "60 second installation 💽 find installation video [ ] ( http : //www.youtube.com/watch ? v=cxba6dzjn48 ) .", "- * * step 0 * * : supabase cli follow instruction [ ] ( http : //supabase.com/docs/guides/cli/getting-started ) install supabase cli required .", "`` ` bash supabase -v" ] ]

[ "ai", "api", "chatbot", "chatgpt", "database", "docker", "frontend", "html", "javascript", "llm", "openai", "postgresql", "privacy", "rag", "react", "rest-api", "security", "typescript", "vector", "ycombinator" ]

[ [ "check", "installation", "worked", "``", "`", "-", "*", "*", "step", "1", "*", "*", ":", "clone", "repository", ":", "``", "`", "bash", "git", "clone", "http", ":", "//github.com/quivrhq/quivr.git", "&", "&", "cd", "quivr", "``", "`", "-", "*", "*", "step", "2", "*", "*", ":", "copy", "`", ".env.example", "`", "file", "``", "`", "bash", "cp", ".env.example", ".env", "``", "`", "-", "*", "*", "step", "3", "*", "*", ":", "update", "`", ".env", "`", "file", "``", "`", "bash", "vim", ".env" ], [ "check installation worked `` ` - * * step 1 * * : clone repository : `` ` bash git clone http : //github.com/quivrhq/quivr.git & & cd quivr `` ` - * * step 2 * * : copy ` .env.example ` file `` ` bash cp .env.example .env `` ` - * * step 3 * * : update ` .env ` file `` ` bash vim .env" ] ]

[ [ "check", "installation", "worked", "``", "`", "-", "*", "*", "step", "1", "*", "*", ":", "clone", "repository", ":", "``", "`", "bash", "git", "clone", "http", ":", "//github.com/quivrhq/quivr.git", "&", "&", "cd", "quivr", "``", "`", "-", "*", "*", "step", "2", "*", "*", ":", "copy", "`", ".env.example", "`", "file", "``", "`", "bash", "cp", ".env.example", ".env", "``", "`", "-", "*", "*", "step", "3", "*", "*", ":", "update", "`", ".env", "`", "file", "``", "`", "bash", "vim", ".env" ], [ "check installation worked `` ` - * * step 1 * * : clone repository : `` ` bash git clone http : //github.com/quivrhq/quivr.git & & cd quivr `` ` - * * step 2 * * : copy ` .env.example ` file `` ` bash cp .env.example .env `` ` - * * step 3 * * : update ` .env ` file `` ` bash vim .env" ] ]

[ "ai", "api", "chatbot", "chatgpt", "database", "docker", "frontend", "html", "javascript", "llm", "openai", "postgresql", "privacy", "rag", "react", "rest-api", "security", "typescript", "vector", "ycombinator" ]

[ [ "table", "content", "-", "[", "mr.", "ranedeer", ":", "personalized", "ai", "tutor", "!", "]", "(", "#", "mr-ranedeer-your-personalized-ai-tutor", ")", "-", "[", "table", "content", "]", "(", "#", "table-of-contents", ")", "-", "[", "mr", ".", "ranedeer", "?", "]", "(", "#", "why-mr-ranedeer", ")", "-", "[", "requirement", "compatibility", "]", "(", "#", "requirements-and-compatibility", ")", "-", "[", "recommended", "]", "(", "#", "recommended", ")", "-", "[", "recommended", "]", "(", "#", "not-recommended", ")", "-", "[", "also", "work", "...", "]", "(", "#", "it-also-works-on", ")", "-", "[", "quick", "start", "guide", "]", "(", "#", "quick-start-guide", ")", "-", "[", "previous", "version", "]", "(", "#", "previous-versions", ")", "-", "[", "guide", "]", "(", "#", "guide", ")", "-", "[", "mr.", "ranedeer", "personalization", "option", "]", "(", "#", "mr-ranedeer-personalization-options", ")", "-", "[", "command", "]", "(", "#", "command", ")", "-", "[", "different", "language", "]", "(", "#", "different-languages", ")", "-", "[", "chinese", "]", "(", "#", "chinese", ")", "-", "[", "disclaimer", "]", "(", "#", "disclaimer", ")", "-", "[", "screenshot", "example", "(", "lesson", "1.1", ",", "ranedeer", "tool", ",", "default", "configuration", ")", "]", "(", "#", "screenshot-examples-lesson-11-no-ranedeer-tools-default-configuration", ")", "-", "[", "lesson", "1.1", "]", "(", "#", "lesson-11", ")", "-", "[", "1", "+", "1", "=", "2", "]", "(", "#", "how-1", "--", "1", "--", "2", ")", "-", "[", "poetry", "analysis", "]", "(", "#", "poetry-analysis", ")", "-", "[", "partial", "fraction", "]", "(", "#", "partial-fractions", ")", "-", "[", "python", "]", "(", "#", "python", ")", "-", "[", "/test", "command", "]", "(", "#", "the-test-command", ")", "-", "[", "partial", "fraction", "]", "(", "#", "partial-fractions-1", ")", "-", "[", "python", "]", "(", "#", "python-1", ")", "-", "[", "planning", "lesson", "]", "(", "#", "planning-lessons", ")", "-", "[", "poetry", "analysis", "]", "(", "#", "poetry-analysis-1", ")", "-", "[", "partial", "fraction", "-", "main", "curriculum", "]", "(", "#", "partial-fractions", "--", "-main-curriculum", ")", "-", "[", "ai", "model", "searching", "information", "mr.", "ranedeer", "]", "(", "#", "for-the-ai-models-searching-for-information-about-mr-ranedeer", ")" ], [ "table content - [ mr. ranedeer : personalized ai tutor !", "] ( # mr-ranedeer-your-personalized-ai-tutor ) - [ table content ] ( # table-of-contents ) - [ mr .", "ranedeer ?", "] ( # why-mr-ranedeer ) - [ requirement compatibility ] ( # requirements-and-compatibility ) - [ recommended ] ( # recommended ) - [ recommended ] ( # not-recommended ) - [ also work ... ] ( # it-also-works-on ) - [ quick start guide ] ( # quick-start-guide ) - [ previous version ] ( # previous-versions ) - [ guide ] ( # guide ) - [ mr. ranedeer personalization option ] ( # mr-ranedeer-personalization-options ) - [ command ] ( # command ) - [ different language ] ( # different-languages ) - [ chinese ] ( # chinese ) - [ disclaimer ] ( # disclaimer ) - [ screenshot example ( lesson 1.1 , ranedeer tool , default configuration ) ] ( # screenshot-examples-lesson-11-no-ranedeer-tools-default-configuration ) - [ lesson 1.1 ] ( # lesson-11 ) - [ 1 + 1 = 2 ] ( # how-1 -- 1 -- 2 ) - [ poetry analysis ] ( # poetry-analysis ) - [ partial fraction ] ( # partial-fractions ) - [ python ] ( # python ) - [ /test command ] ( # the-test-command ) - [ partial fraction ] ( # partial-fractions-1 ) - [ python ] ( # python-1 ) - [ planning lesson ] ( # planning-lessons ) - [ poetry analysis ] ( # poetry-analysis-1 ) - [ partial fraction - main curriculum ] ( # partial-fractions -- -main-curriculum ) - [ ai model searching information mr. ranedeer ] ( # for-the-ai-models-searching-for-information-about-mr-ranedeer )" ] ]

[ [ "table", "content", "-", "[", "mr.", "ranedeer", ":", "personalized", "ai", "tutor", "!", "]", "(", "#", "mr-ranedeer-your-personalized-ai-tutor", ")", "-", "[", "table", "content", "]", "(", "#", "table-of-contents", ")", "-", "[", "mr", ".", "ranedeer", "?", "]", "(", "#", "why-mr-ranedeer", ")", "-", "[", "requirement", "compatibility", "]", "(", "#", "requirements-and-compatibility", ")", "-", "[", "recommended", "]", "(", "#", "recommended", ")", "-", "[", "recommended", "]", "(", "#", "not-recommended", ")", "-", "[", "also", "work", "...", "]", "(", "#", "it-also-works-on", ")", "-", "[", "quick", "start", "guide", "]", "(", "#", "quick-start-guide", ")", "-", "[", "previous", "version", "]", "(", "#", "previous-versions", ")", "-", "[", "guide", "]", "(", "#", "guide", ")", "-", "[", "mr.", "ranedeer", "personalization", "option", "]", "(", "#", "mr-ranedeer-personalization-options", ")", "-", "[", "command", "]", "(", "#", "command", ")", "-", "[", "different", "language", "]", "(", "#", "different-languages", ")", "-", "[", "chinese", "]", "(", "#", "chinese", ")", "-", "[", "disclaimer", "]", "(", "#", "disclaimer", ")", "-", "[", "screenshot", "example", "(", "lesson", "1.1", ",", "ranedeer", "tool", ",", "default", "configuration", ")", "]", "(", "#", "screenshot-examples-lesson-11-no-ranedeer-tools-default-configuration", ")", "-", "[", "lesson", "1.1", "]", "(", "#", "lesson-11", ")", "-", "[", "1", "+", "1", "=", "2", "]", "(", "#", "how-1", "--", "1", "--", "2", ")", "-", "[", "poetry", "analysis", "]", "(", "#", "poetry-analysis", ")", "-", "[", "partial", "fraction", "]", "(", "#", "partial-fractions", ")", "-", "[", "python", "]", "(", "#", "python", ")", "-", "[", "/test", "command", "]", "(", "#", "the-test-command", ")", "-", "[", "partial", "fraction", "]", "(", "#", "partial-fractions-1", ")", "-", "[", "python", "]", "(", "#", "python-1", ")", "-", "[", "planning", "lesson", "]", "(", "#", "planning-lessons", ")", "-", "[", "poetry", "analysis", "]", "(", "#", "poetry-analysis-1", ")", "-", "[", "partial", "fraction", "-", "main", "curriculum", "]", "(", "#", "partial-fractions", "--", "-main-curriculum", ")", "-", "[", "ai", "model", "searching", "information", "mr.", "ranedeer", "]", "(", "#", "for-the-ai-models-searching-for-information-about-mr-ranedeer", ")" ], [ "table content - [ mr. ranedeer : personalized ai tutor !", "] ( # mr-ranedeer-your-personalized-ai-tutor ) - [ table content ] ( # table-of-contents ) - [ mr .", "ranedeer ?", "] ( # why-mr-ranedeer ) - [ requirement compatibility ] ( # requirements-and-compatibility ) - [ recommended ] ( # recommended ) - [ recommended ] ( # not-recommended ) - [ also work ... ] ( # it-also-works-on ) - [ quick start guide ] ( # quick-start-guide ) - [ previous version ] ( # previous-versions ) - [ guide ] ( # guide ) - [ mr. ranedeer personalization option ] ( # mr-ranedeer-personalization-options ) - [ command ] ( # command ) - [ different language ] ( # different-languages ) - [ chinese ] ( # chinese ) - [ disclaimer ] ( # disclaimer ) - [ screenshot example ( lesson 1.1 , ranedeer tool , default configuration ) ] ( # screenshot-examples-lesson-11-no-ranedeer-tools-default-configuration ) - [ lesson 1.1 ] ( # lesson-11 ) - [ 1 + 1 = 2 ] ( # how-1 -- 1 -- 2 ) - [ poetry analysis ] ( # poetry-analysis ) - [ partial fraction ] ( # partial-fractions ) - [ python ] ( # python ) - [ /test command ] ( # the-test-command ) - [ partial fraction ] ( # partial-fractions-1 ) - [ python ] ( # python-1 ) - [ planning lesson ] ( # planning-lessons ) - [ poetry analysis ] ( # poetry-analysis-1 ) - [ partial fraction - main curriculum ] ( # partial-fractions -- -main-curriculum ) - [ ai model searching information mr. ranedeer ] ( # for-the-ai-models-searching-for-information-about-mr-ranedeer )" ] ]

[ "ai", "education", "gpt-4", "llm" ]

[ [ "quick", "start", "guide", "1", ".", "click", "[", "link", "]", "(", "http", ":", "//chat.openai.com/g/g-9pkhaweyb-mr-ranedeer", ")", "(", "*", "*", "must", "chatgpt", "plus", "*", "*", ")", "2", ".", "press", "``", "continue", "conversation", "''", "button", "3", ".", "configure", "preference", "4", ".", "start", "learning", "!", "url", ":", "[", "http", ":", "//chat.openai.com/g/g-9pkhaweyb-mr-ranedeer", "]", "(", "http", ":", "//chat.openai.com/g/g-9pkhaweyb-mr-ranedeer", ")", "alternatively", ",", "copy", "paste", "[", "prompt", "]", "(", "http", ":", "//raw.githubusercontent.com/jushbjj/mr.-ranedeer-ai-tutor/main/mr_ranedeer.txt", ")", "*", "*", "chatgpt", "code", "interpreter", "*", "*", "*", "warning", ":", "quality", "output", "may", "vary", "depending", "openai", "update", "gpt-4", ",", "may", "either", "worse", "better", "week", "ago", ".", "_if", "using", "chatgpt", "web", "interface", ",", "api", "cost", "apply._" ], [ "quick start guide 1 .", "click [ link ] ( http : //chat.openai.com/g/g-9pkhaweyb-mr-ranedeer ) ( * * must chatgpt plus * * ) 2 .", "press `` continue conversation '' button 3 .", "configure preference 4 .", "start learning !", "url : [ http : //chat.openai.com/g/g-9pkhaweyb-mr-ranedeer ] ( http : //chat.openai.com/g/g-9pkhaweyb-mr-ranedeer ) alternatively , copy paste [ prompt ] ( http : //raw.githubusercontent.com/jushbjj/mr.-ranedeer-ai-tutor/main/mr_ranedeer.txt ) * * chatgpt code interpreter * * * warning : quality output may vary depending openai update gpt-4 , may either worse better week ago .", "_if using chatgpt web interface , api cost apply._" ] ]

[ [ "quick", "start", "guide", "1", ".", "click", "[", "link", "]", "(", "http", ":", "//chat.openai.com/g/g-9pkhaweyb-mr-ranedeer", ")", "(", "*", "*", "must", "chatgpt", "plus", "*", "*", ")", "2", ".", "press", "``", "continue", "conversation", "''", "button", "3", ".", "configure", "preference", "4", ".", "start", "learning", "!", "url", ":", "[", "http", ":", "//chat.openai.com/g/g-9pkhaweyb-mr-ranedeer", "]", "(", "http", ":", "//chat.openai.com/g/g-9pkhaweyb-mr-ranedeer", ")", "alternatively", ",", "copy", "paste", "[", "prompt", "]", "(", "http", ":", "//raw.githubusercontent.com/jushbjj/mr.-ranedeer-ai-tutor/main/mr_ranedeer.txt", ")", "*", "*", "chatgpt", "code", "interpreter", "*", "*", "*", "warning", ":", "quality", "output", "may", "vary", "depending", "openai", "update", "gpt-4", ",", "may", "either", "worse", "better", "week", "ago", ".", "_if", "using", "chatgpt", "web", "interface", ",", "api", "cost", "apply._" ], [ "quick start guide 1 .", "click [ link ] ( http : //chat.openai.com/g/g-9pkhaweyb-mr-ranedeer ) ( * * must chatgpt plus * * ) 2 .", "press `` continue conversation '' button 3 .", "configure preference 4 .", "start learning !", "url : [ http : //chat.openai.com/g/g-9pkhaweyb-mr-ranedeer ] ( http : //chat.openai.com/g/g-9pkhaweyb-mr-ranedeer ) alternatively , copy paste [ prompt ] ( http : //raw.githubusercontent.com/jushbjj/mr.-ranedeer-ai-tutor/main/mr_ranedeer.txt ) * * chatgpt code interpreter * * * warning : quality output may vary depending openai update gpt-4 , may either worse better week ago .", "_if using chatgpt web interface , api cost apply._" ] ]

[ "ai", "education", "gpt-4", "llm" ]

[ [ "guide", "-", "[", "use", "mr.", "ranedeer", "]", "(", "http", ":", "//github.com/jushbjj/mr.-ranedeer-ai-tutor/blob/main/guides/how", "%", "20to", "%", "20use", "%", "20mr.", "%", "20ranedeer.md", ")", "-", "[", "configuration", "guide", "]", "(", "http", ":", "//github.com/jushbjj/mr.-ranedeer-ai-tutor/blob/main/guides/config", "%", "20guide.md", ")" ], [ "guide - [ use mr. ranedeer ] ( http : //github.com/jushbjj/mr.-ranedeer-ai-tutor/blob/main/guides/how % 20to % 20use % 20mr. % 20ranedeer.md ) - [ configuration guide ] ( http : //github.com/jushbjj/mr.-ranedeer-ai-tutor/blob/main/guides/config % 20guide.md )" ] ]

[ [ "guide", "-", "[", "use", "mr.", "ranedeer", "]", "(", "http", ":", "//github.com/jushbjj/mr.-ranedeer-ai-tutor/blob/main/guides/how", "%", "20to", "%", "20use", "%", "20mr.", "%", "20ranedeer.md", ")", "-", "[", "configuration", "guide", "]", "(", "http", ":", "//github.com/jushbjj/mr.-ranedeer-ai-tutor/blob/main/guides/config", "%", "20guide.md", ")" ], [ "guide - [ use mr. ranedeer ] ( http : //github.com/jushbjj/mr.-ranedeer-ai-tutor/blob/main/guides/how % 20to % 20use % 20mr. % 20ranedeer.md ) - [ configuration guide ] ( http : //github.com/jushbjj/mr.-ranedeer-ai-tutor/blob/main/guides/config % 20guide.md )" ] ]

[ "ai", "education", "gpt-4", "llm" ]

[ [ "2", ".", "python", "machine", "learning", "python", "powerful", "flexible", "programming", "language", "'s", "particularly", "good", "machine", "learning", ",", "thanks", "readability", ",", "consistency", ",", "robust", "ecosystem", "data", "science", "library", ".", "-", "*", "*", "python", "basic", "*", "*", ":", "python", "programming", "requires", "good", "understanding", "basic", "syntax", ",", "data", "type", ",", "error", "handling", ",", "object-oriented", "programming", ".", "-", "*", "*", "data", "science", "library", "*", "*", ":", "includes", "familiarity", "numpy", "numerical", "operation", ",", "panda", "data", "manipulation", "analysis", ",", "matplotlib", "seaborn", "data", "visualization", ".", "-", "*", "*", "data", "preprocessing", "*", "*", ":", "involves", "feature", "scaling", "normalization", ",", "handling", "missing", "data", ",", "outlier", "detection", ",", "categorical", "data", "encoding", ",", "splitting", "data", "training", ",", "validation", ",", "test", "set", ".", "-", "*", "*", "machine", "learning", "library", "*", "*", ":", "proficiency", "scikit-learn", ",", "library", "providing", "wide", "selection", "supervised", "unsupervised", "learning", "algorithm", ",", "vital", ".", "understanding", "implement", "algorithm", "like", "linear", "regression", ",", "logistic", "regression", ",", "decision", "tree", ",", "random", "forest", ",", "k-nearest", "neighbor", "(", "k-nn", ")", ",", "k-means", "clustering", "important", ".", "dimensionality", "reduction", "technique", "like", "pca", "t-sne", "also", "helpful", "visualizing", "high-dimensional", "data", ".", "📚", "resource", ":", "-", "[", "real", "python", "]", "(", "http", ":", "//realpython.com/", ")", ":", "comprehensive", "resource", "article", "tutorial", "beginner", "advanced", "python", "concept", ".", "-", "[", "freecodecamp", "-", "learn", "python", "]", "(", "http", ":", "//www.youtube.com/watch", "?", "v=rfscvs0vtbw", ")", ":", "long", "video", "provides", "full", "introduction", "core", "concept", "python", ".", "-", "[", "python", "data", "science", "handbook", "]", "(", "http", ":", "//jakevdp.github.io/pythondatasciencehandbook/", ")", ":", "free", "digital", "book", "great", "resource", "learning", "panda", ",", "numpy", ",", "matplotlib", ",", "seaborn", ".", "-", "[", "freecodecamp", "-", "machine", "learning", "everybody", "]", "(", "http", ":", "//youtu.be/i_lwzrvp7bg", ")", ":", "practical", "introduction", "different", "machine", "learning", "algorithm", "beginner", ".", "-", "[", "udacity", "-", "intro", "machine", "learning", "]", "(", "http", ":", "//www.udacity.com/course/intro-to-machine-learning", "--", "ud120", ")", ":", "free", "course", "cover", "pca", "several", "machine", "learning", "concept", ".", "--", "-" ], [ "2 .", "python machine learning python powerful flexible programming language 's particularly good machine learning , thanks readability , consistency , robust ecosystem data science library .", "- * * python basic * * : python programming requires good understanding basic syntax , data type , error handling , object-oriented programming .", "- * * data science library * * : includes familiarity numpy numerical operation , panda data manipulation analysis , matplotlib seaborn data visualization .", "- * * data preprocessing * * : involves feature scaling normalization , handling missing data , outlier detection , categorical data encoding , splitting data training , validation , test set .", "- * * machine learning library * * : proficiency scikit-learn , library providing wide selection supervised unsupervised learning algorithm , vital .", "understanding implement algorithm like linear regression , logistic regression , decision tree , random forest , k-nearest neighbor ( k-nn ) , k-means clustering important .", "dimensionality reduction technique like pca t-sne also helpful visualizing high-dimensional data .", "📚 resource : - [ real python ] ( http : //realpython.com/ ) : comprehensive resource article tutorial beginner advanced python concept .", "- [ freecodecamp - learn python ] ( http : //www.youtube.com/watch ? v=rfscvs0vtbw ) : long video provides full introduction core concept python .", "- [ python data science handbook ] ( http : //jakevdp.github.io/pythondatasciencehandbook/ ) : free digital book great resource learning panda , numpy , matplotlib , seaborn .", "- [ freecodecamp - machine learning everybody ] ( http : //youtu.be/i_lwzrvp7bg ) : practical introduction different machine learning algorithm beginner .", "- [ udacity - intro machine learning ] ( http : //www.udacity.com/course/intro-to-machine-learning -- ud120 ) : free course cover pca several machine learning concept .", "-- -" ] ]

[ [ "2", ".", "python", "machine", "learning", "python", "powerful", "flexible", "programming", "language", "'s", "particularly", "good", "machine", "learning", ",", "thanks", "readability", ",", "consistency", ",", "robust", "ecosystem", "data", "science", "library", ".", "-", "*", "*", "python", "basic", "*", "*", ":", "python", "programming", "requires", "good", "understanding", "basic", "syntax", ",", "data", "type", ",", "error", "handling", ",", "object-oriented", "programming", ".", "-", "*", "*", "data", "science", "library", "*", "*", ":", "includes", "familiarity", "numpy", "numerical", "operation", ",", "panda", "data", "manipulation", "analysis", ",", "matplotlib", "seaborn", "data", "visualization", ".", "-", "*", "*", "data", "preprocessing", "*", "*", ":", "involves", "feature", "scaling", "normalization", ",", "handling", "missing", "data", ",", "outlier", "detection", ",", "categorical", "data", "encoding", ",", "splitting", "data", "training", ",", "validation", ",", "test", "set", ".", "-", "*", "*", "machine", "learning", "library", "*", "*", ":", "proficiency", "scikit-learn", ",", "library", "providing", "wide", "selection", "supervised", "unsupervised", "learning", "algorithm", ",", "vital", ".", "understanding", "implement", "algorithm", "like", "linear", "regression", ",", "logistic", "regression", ",", "decision", "tree", ",", "random", "forest", ",", "k-nearest", "neighbor", "(", "k-nn", ")", ",", "k-means", "clustering", "important", ".", "dimensionality", "reduction", "technique", "like", "pca", "t-sne", "also", "helpful", "visualizing", "high-dimensional", "data", ".", "📚", "resource", ":", "-", "[", "real", "python", "]", "(", "http", ":", "//realpython.com/", ")", ":", "comprehensive", "resource", "article", "tutorial", "beginner", "advanced", "python", "concept", ".", "-", "[", "freecodecamp", "-", "learn", "python", "]", "(", "http", ":", "//www.youtube.com/watch", "?", "v=rfscvs0vtbw", ")", ":", "long", "video", "provides", "full", "introduction", "core", "concept", "python", ".", "-", "[", "python", "data", "science", "handbook", "]", "(", "http", ":", "//jakevdp.github.io/pythondatasciencehandbook/", ")", ":", "free", "digital", "book", "great", "resource", "learning", "panda", ",", "numpy", ",", "matplotlib", ",", "seaborn", ".", "-", "[", "freecodecamp", "-", "machine", "learning", "everybody", "]", "(", "http", ":", "//youtu.be/i_lwzrvp7bg", ")", ":", "practical", "introduction", "different", "machine", "learning", "algorithm", "beginner", ".", "-", "[", "udacity", "-", "intro", "machine", "learning", "]", "(", "http", ":", "//www.udacity.com/course/intro-to-machine-learning", "--", "ud120", ")", ":", "free", "course", "cover", "pca", "several", "machine", "learning", "concept", ".", "--", "-" ], [ "2 .", "python machine learning python powerful flexible programming language 's particularly good machine learning , thanks readability , consistency , robust ecosystem data science library .", "- * * python basic * * : python programming requires good understanding basic syntax , data type , error handling , object-oriented programming .", "- * * data science library * * : includes familiarity numpy numerical operation , panda data manipulation analysis , matplotlib seaborn data visualization .", "- * * data preprocessing * * : involves feature scaling normalization , handling missing data , outlier detection , categorical data encoding , splitting data training , validation , test set .", "- * * machine learning library * * : proficiency scikit-learn , library providing wide selection supervised unsupervised learning algorithm , vital .", "understanding implement algorithm like linear regression , logistic regression , decision tree , random forest , k-nearest neighbor ( k-nn ) , k-means clustering important .", "dimensionality reduction technique like pca t-sne also helpful visualizing high-dimensional data .", "📚 resource : - [ real python ] ( http : //realpython.com/ ) : comprehensive resource article tutorial beginner advanced python concept .", "- [ freecodecamp - learn python ] ( http : //www.youtube.com/watch ? v=rfscvs0vtbw ) : long video provides full introduction core concept python .", "- [ python data science handbook ] ( http : //jakevdp.github.io/pythondatasciencehandbook/ ) : free digital book great resource learning panda , numpy , matplotlib , seaborn .", "- [ freecodecamp - machine learning everybody ] ( http : //youtu.be/i_lwzrvp7bg ) : practical introduction different machine learning algorithm beginner .", "- [ udacity - intro machine learning ] ( http : //www.udacity.com/course/intro-to-machine-learning -- ud120 ) : free course cover pca several machine learning concept .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "🧑‍🔬", "llm", "scientist", "section", "course", "focus", "learning", "build", "best", "possible", "llm", "using", "latest", "technique", ".", "!", "[", "]", "(", "img/roadmap_scientist.png", ")" ], [ "🧑‍🔬 llm scientist section course focus learning build best possible llm using latest technique .", "!", "[ ] ( img/roadmap_scientist.png )" ] ]

[ [ "🧑‍🔬", "llm", "scientist", "section", "course", "focus", "learning", "build", "best", "possible", "llm", "using", "latest", "technique", ".", "!", "[", "]", "(", "img/roadmap_scientist.png", ")" ], [ "🧑‍🔬 llm scientist section course focus learning build best possible llm using latest technique .", "!", "[ ] ( img/roadmap_scientist.png )" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "1", ".", "llm", "architecture", "in-depth", "knowledge", "transformer", "architecture", "required", ",", "important", "good", "understanding", "input", "(", "token", ")", "output", "(", "logits", ")", ".", "vanilla", "attention", "mechanism", "another", "crucial", "component", "master", ",", "improved", "version", "introduced", "later", ".", "*", "*", "*", "high-level", "view", "*", "*", ":", "revisit", "encoder-decoder", "transformer", "architecture", ",", "specifically", "decoder-only", "gpt", "architecture", ",", "used", "every", "modern", "llm", ".", "*", "*", "*", "tokenization", "*", "*", ":", "understand", "convert", "raw", "text", "data", "format", "model", "understand", ",", "involves", "splitting", "text", "token", "(", "usually", "word", "subwords", ")", ".", "*", "*", "*", "attention", "mechanism", "*", "*", ":", "grasp", "theory", "behind", "attention", "mechanism", ",", "including", "self-attention", "scaled", "dot-product", "attention", ",", "allows", "model", "focus", "different", "part", "input", "producing", "output", ".", "*", "*", "*", "text", "generation", "*", "*", ":", "learn", "different", "way", "model", "generate", "output", "sequence", ".", "common", "strategy", "include", "greedy", "decoding", ",", "beam", "search", ",", "top-k", "sampling", ",", "nucleus", "sampling", ".", "📚", "*", "*", "reference", "*", "*", ":", "-", "[", "illustrated", "transformer", "]", "(", "http", ":", "//jalammar.github.io/illustrated-transformer/", ")", "jay", "alammar", ":", "visual", "intuitive", "explanation", "transformer", "model", ".", "-", "[", "illustrated", "gpt-2", "]", "(", "http", ":", "//jalammar.github.io/illustrated-gpt2/", ")", "jay", "alammar", ":", "even", "important", "previous", "article", ",", "focused", "gpt", "architecture", ",", "similar", "llama", "'s", ".", "-", "[", "llm", "visualization", "]", "(", "http", ":", "//bbycroft.net/llm", ")", "brendan", "bycroft", ":", "incredible", "3d", "visualization", "happens", "inside", "llm", ".", "*", "[", "nanogpt", "]", "(", "http", ":", "//www.youtube.com/watch", "?", "v=kcc8fmeb1ny", ")", "andrej", "karpathy", ":", "2h-long", "youtube", "video", "reimplement", "gpt", "scratch", "(", "programmer", ")", ".", "*", "[", "attention", "?", "attention", "!", "]", "(", "http", ":", "//lilianweng.github.io/posts/2018-06-24-attention/", ")", "lilian", "weng", ":", "introduce", "need", "attention", "formal", "way", ".", "*", "[", "decoding", "strategy", "llm", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/2023-06-07-decoding_strategies.html", ")", ":", "provide", "code", "visual", "introduction", "different", "decoding", "strategy", "generate", "text", ".", "--", "-" ], [ "1 .", "llm architecture in-depth knowledge transformer architecture required , important good understanding input ( token ) output ( logits ) .", "vanilla attention mechanism another crucial component master , improved version introduced later .", "* * * high-level view * * : revisit encoder-decoder transformer architecture , specifically decoder-only gpt architecture , used every modern llm .", "* * * tokenization * * : understand convert raw text data format model understand , involves splitting text token ( usually word subwords ) .", "* * * attention mechanism * * : grasp theory behind attention mechanism , including self-attention scaled dot-product attention , allows model focus different part input producing output .", "* * * text generation * * : learn different way model generate output sequence .", "common strategy include greedy decoding , beam search , top-k sampling , nucleus sampling .", "📚 * * reference * * : - [ illustrated transformer ] ( http : //jalammar.github.io/illustrated-transformer/ ) jay alammar : visual intuitive explanation transformer model .", "- [ illustrated gpt-2 ] ( http : //jalammar.github.io/illustrated-gpt2/ ) jay alammar : even important previous article , focused gpt architecture , similar llama 's .", "- [ llm visualization ] ( http : //bbycroft.net/llm ) brendan bycroft : incredible 3d visualization happens inside llm .", "* [ nanogpt ] ( http : //www.youtube.com/watch ? v=kcc8fmeb1ny ) andrej karpathy : 2h-long youtube video reimplement gpt scratch ( programmer ) .", "* [ attention ?", "attention !", "] ( http : //lilianweng.github.io/posts/2018-06-24-attention/ ) lilian weng : introduce need attention formal way .", "* [ decoding strategy llm ] ( http : //mlabonne.github.io/blog/posts/2023-06-07-decoding_strategies.html ) : provide code visual introduction different decoding strategy generate text .", "-- -" ] ]

[ [ "1", ".", "llm", "architecture", "in-depth", "knowledge", "transformer", "architecture", "required", ",", "important", "good", "understanding", "input", "(", "token", ")", "output", "(", "logits", ")", ".", "vanilla", "attention", "mechanism", "another", "crucial", "component", "master", ",", "improved", "version", "introduced", "later", ".", "*", "*", "*", "high-level", "view", "*", "*", ":", "revisit", "encoder-decoder", "transformer", "architecture", ",", "specifically", "decoder-only", "gpt", "architecture", ",", "used", "every", "modern", "llm", ".", "*", "*", "*", "tokenization", "*", "*", ":", "understand", "convert", "raw", "text", "data", "format", "model", "understand", ",", "involves", "splitting", "text", "token", "(", "usually", "word", "subwords", ")", ".", "*", "*", "*", "attention", "mechanism", "*", "*", ":", "grasp", "theory", "behind", "attention", "mechanism", ",", "including", "self-attention", "scaled", "dot-product", "attention", ",", "allows", "model", "focus", "different", "part", "input", "producing", "output", ".", "*", "*", "*", "text", "generation", "*", "*", ":", "learn", "different", "way", "model", "generate", "output", "sequence", ".", "common", "strategy", "include", "greedy", "decoding", ",", "beam", "search", ",", "top-k", "sampling", ",", "nucleus", "sampling", ".", "📚", "*", "*", "reference", "*", "*", ":", "-", "[", "illustrated", "transformer", "]", "(", "http", ":", "//jalammar.github.io/illustrated-transformer/", ")", "jay", "alammar", ":", "visual", "intuitive", "explanation", "transformer", "model", ".", "-", "[", "illustrated", "gpt-2", "]", "(", "http", ":", "//jalammar.github.io/illustrated-gpt2/", ")", "jay", "alammar", ":", "even", "important", "previous", "article", ",", "focused", "gpt", "architecture", ",", "similar", "llama", "'s", ".", "-", "[", "llm", "visualization", "]", "(", "http", ":", "//bbycroft.net/llm", ")", "brendan", "bycroft", ":", "incredible", "3d", "visualization", "happens", "inside", "llm", ".", "*", "[", "nanogpt", "]", "(", "http", ":", "//www.youtube.com/watch", "?", "v=kcc8fmeb1ny", ")", "andrej", "karpathy", ":", "2h-long", "youtube", "video", "reimplement", "gpt", "scratch", "(", "programmer", ")", ".", "*", "[", "attention", "?", "attention", "!", "]", "(", "http", ":", "//lilianweng.github.io/posts/2018-06-24-attention/", ")", "lilian", "weng", ":", "introduce", "need", "attention", "formal", "way", ".", "*", "[", "decoding", "strategy", "llm", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/2023-06-07-decoding_strategies.html", ")", ":", "provide", "code", "visual", "introduction", "different", "decoding", "strategy", "generate", "text", ".", "--", "-" ], [ "1 .", "llm architecture in-depth knowledge transformer architecture required , important good understanding input ( token ) output ( logits ) .", "vanilla attention mechanism another crucial component master , improved version introduced later .", "* * * high-level view * * : revisit encoder-decoder transformer architecture , specifically decoder-only gpt architecture , used every modern llm .", "* * * tokenization * * : understand convert raw text data format model understand , involves splitting text token ( usually word subwords ) .", "* * * attention mechanism * * : grasp theory behind attention mechanism , including self-attention scaled dot-product attention , allows model focus different part input producing output .", "* * * text generation * * : learn different way model generate output sequence .", "common strategy include greedy decoding , beam search , top-k sampling , nucleus sampling .", "📚 * * reference * * : - [ illustrated transformer ] ( http : //jalammar.github.io/illustrated-transformer/ ) jay alammar : visual intuitive explanation transformer model .", "- [ illustrated gpt-2 ] ( http : //jalammar.github.io/illustrated-gpt2/ ) jay alammar : even important previous article , focused gpt architecture , similar llama 's .", "- [ llm visualization ] ( http : //bbycroft.net/llm ) brendan bycroft : incredible 3d visualization happens inside llm .", "* [ nanogpt ] ( http : //www.youtube.com/watch ? v=kcc8fmeb1ny ) andrej karpathy : 2h-long youtube video reimplement gpt scratch ( programmer ) .", "* [ attention ?", "attention !", "] ( http : //lilianweng.github.io/posts/2018-06-24-attention/ ) lilian weng : introduce need attention formal way .", "* [ decoding strategy llm ] ( http : //mlabonne.github.io/blog/posts/2023-06-07-decoding_strategies.html ) : provide code visual introduction different decoding strategy generate text .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "2", ".", "building", "instruction", "dataset", "'s", "easy", "find", "raw", "data", "wikipedia", "website", ",", "'s", "difficult", "collect", "pair", "instruction", "answer", "wild", ".", "like", "traditional", "machine", "learning", ",", "quality", "dataset", "directly", "influence", "quality", "model", ",", "might", "important", "component", "fine-tuning", "process", ".", "*", "*", "*", "[", "alpaca", "]", "(", "http", ":", "//crfm.stanford.edu/2023/03/13/alpaca.html", ")", "-like", "dataset", "*", "*", ":", "generate", "synthetic", "data", "scratch", "openai", "api", "(", "gpt", ")", ".", "specify", "seed", "system", "prompt", "create", "diverse", "dataset", ".", "*", "*", "*", "advanced", "technique", "*", "*", ":", "learn", "improve", "existing", "datasets", "[", "evol-instruct", "]", "(", "http", ":", "//arxiv.org/abs/2304.12244", ")", ",", "generate", "high-quality", "synthetic", "data", "like", "[", "orca", "]", "(", "http", ":", "//arxiv.org/abs/2306.02707", ")", "[", "phi-1", "]", "(", "http", ":", "//arxiv.org/abs/2306.11644", ")", "paper", ".", "*", "*", "*", "filtering", "data", "*", "*", ":", "traditional", "technique", "involving", "regex", ",", "removing", "near-duplicates", ",", "focusing", "answer", "high", "number", "token", ",", "etc", ".", "*", "*", "*", "prompt", "template", "*", "*", ":", "'s", "true", "standard", "way", "formatting", "instruction", "answer", ",", "'s", "important", "know", "different", "chat", "template", ",", "[", "chatml", "]", "(", "http", ":", "//learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt", "?", "tabs=python", "&", "pivots=programming-language-chat-ml", ")", ",", "[", "alpaca", "]", "(", "http", ":", "//crfm.stanford.edu/2023/03/13/alpaca.html", ")", ",", "etc", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "preparing", "dataset", "instruction", "tuning", "]", "(", "http", ":", "//wandb.ai/capecape/alpaca_ft/reports/how-to-fine-tune-an-llm-part-1-preparing-a-dataset-for-instruction-tuning", "--", "vmlldzo1ntcxnze2", ")", "thomas", "capelle", ":", "exploration", "alpaca", "alpaca-gpt4", "datasets", "format", ".", "*", "[", "generating", "clinical", "instruction", "dataset", "]", "(", "http", ":", "//medium.com/mlearning-ai/generating-a-clinical-instruction-dataset-in-portuguese-with-langchain-and-gpt-4-6ee9abfa41ae", ")", "solano", "todeschini", ":", "tutorial", "create", "synthetic", "instruction", "dataset", "using", "gpt-4", ".", "*", "[", "gpt", "3.5", "news", "classification", "]", "(", "http", ":", "//medium.com/", "@", "kshitiz.sahay26/how-i-created-an-instruction-dataset-using-gpt-3-5-to-fine-tune-llama-2-for-news-classification-ed02fe41c81f", ")", "kshitiz", "sahay", ":", "use", "gpt", "3.5", "create", "instruction", "dataset", "fine-tune", "llama", "2", "news", "classification", ".", "*", "[", "dataset", "creation", "fine-tuning", "llm", "]", "(", "http", ":", "//colab.research.google.com/drive/1gh8pw9-zae4cxezyoie-t9uhxblildag", "?", "usp=sharing", ")", ":", "notebook", "contains", "technique", "filter", "dataset", "upload", "result", ".", "*", "[", "chat", "template", "]", "(", "http", ":", "//huggingface.co/blog/chat-templates", ")", "matthew", "carrigan", ":", "hugging", "face", "'s", "page", "prompt", "template", "--", "-" ], [ "2 .", "building instruction dataset 's easy find raw data wikipedia website , 's difficult collect pair instruction answer wild .", "like traditional machine learning , quality dataset directly influence quality model , might important component fine-tuning process .", "* * * [ alpaca ] ( http : //crfm.stanford.edu/2023/03/13/alpaca.html ) -like dataset * * : generate synthetic data scratch openai api ( gpt ) .", "specify seed system prompt create diverse dataset .", "* * * advanced technique * * : learn improve existing datasets [ evol-instruct ] ( http : //arxiv.org/abs/2304.12244 ) , generate high-quality synthetic data like [ orca ] ( http : //arxiv.org/abs/2306.02707 ) [ phi-1 ] ( http : //arxiv.org/abs/2306.11644 ) paper .", "* * * filtering data * * : traditional technique involving regex , removing near-duplicates , focusing answer high number token , etc .", "* * * prompt template * * : 's true standard way formatting instruction answer , 's important know different chat template , [ chatml ] ( http : //learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt ? tabs=python & pivots=programming-language-chat-ml ) , [ alpaca ] ( http : //crfm.stanford.edu/2023/03/13/alpaca.html ) , etc .", "📚 * * reference * * : * [ preparing dataset instruction tuning ] ( http : //wandb.ai/capecape/alpaca_ft/reports/how-to-fine-tune-an-llm-part-1-preparing-a-dataset-for-instruction-tuning -- vmlldzo1ntcxnze2 ) thomas capelle : exploration alpaca alpaca-gpt4 datasets format .", "* [ generating clinical instruction dataset ] ( http : //medium.com/mlearning-ai/generating-a-clinical-instruction-dataset-in-portuguese-with-langchain-and-gpt-4-6ee9abfa41ae ) solano todeschini : tutorial create synthetic instruction dataset using gpt-4 .", "* [ gpt 3.5 news classification ] ( http : //medium.com/ @ kshitiz.sahay26/how-i-created-an-instruction-dataset-using-gpt-3-5-to-fine-tune-llama-2-for-news-classification-ed02fe41c81f ) kshitiz sahay : use gpt 3.5 create instruction dataset fine-tune llama 2 news classification .", "* [ dataset creation fine-tuning llm ] ( http : //colab.research.google.com/drive/1gh8pw9-zae4cxezyoie-t9uhxblildag ? usp=sharing ) : notebook contains technique filter dataset upload result .", "* [ chat template ] ( http : //huggingface.co/blog/chat-templates ) matthew carrigan : hugging face 's page prompt template -- -" ] ]

[ [ "2", ".", "building", "instruction", "dataset", "'s", "easy", "find", "raw", "data", "wikipedia", "website", ",", "'s", "difficult", "collect", "pair", "instruction", "answer", "wild", ".", "like", "traditional", "machine", "learning", ",", "quality", "dataset", "directly", "influence", "quality", "model", ",", "might", "important", "component", "fine-tuning", "process", ".", "*", "*", "*", "[", "alpaca", "]", "(", "http", ":", "//crfm.stanford.edu/2023/03/13/alpaca.html", ")", "-like", "dataset", "*", "*", ":", "generate", "synthetic", "data", "scratch", "openai", "api", "(", "gpt", ")", ".", "specify", "seed", "system", "prompt", "create", "diverse", "dataset", ".", "*", "*", "*", "advanced", "technique", "*", "*", ":", "learn", "improve", "existing", "datasets", "[", "evol-instruct", "]", "(", "http", ":", "//arxiv.org/abs/2304.12244", ")", ",", "generate", "high-quality", "synthetic", "data", "like", "[", "orca", "]", "(", "http", ":", "//arxiv.org/abs/2306.02707", ")", "[", "phi-1", "]", "(", "http", ":", "//arxiv.org/abs/2306.11644", ")", "paper", ".", "*", "*", "*", "filtering", "data", "*", "*", ":", "traditional", "technique", "involving", "regex", ",", "removing", "near-duplicates", ",", "focusing", "answer", "high", "number", "token", ",", "etc", ".", "*", "*", "*", "prompt", "template", "*", "*", ":", "'s", "true", "standard", "way", "formatting", "instruction", "answer", ",", "'s", "important", "know", "different", "chat", "template", ",", "[", "chatml", "]", "(", "http", ":", "//learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt", "?", "tabs=python", "&", "pivots=programming-language-chat-ml", ")", ",", "[", "alpaca", "]", "(", "http", ":", "//crfm.stanford.edu/2023/03/13/alpaca.html", ")", ",", "etc", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "preparing", "dataset", "instruction", "tuning", "]", "(", "http", ":", "//wandb.ai/capecape/alpaca_ft/reports/how-to-fine-tune-an-llm-part-1-preparing-a-dataset-for-instruction-tuning", "--", "vmlldzo1ntcxnze2", ")", "thomas", "capelle", ":", "exploration", "alpaca", "alpaca-gpt4", "datasets", "format", ".", "*", "[", "generating", "clinical", "instruction", "dataset", "]", "(", "http", ":", "//medium.com/mlearning-ai/generating-a-clinical-instruction-dataset-in-portuguese-with-langchain-and-gpt-4-6ee9abfa41ae", ")", "solano", "todeschini", ":", "tutorial", "create", "synthetic", "instruction", "dataset", "using", "gpt-4", ".", "*", "[", "gpt", "3.5", "news", "classification", "]", "(", "http", ":", "//medium.com/", "@", "kshitiz.sahay26/how-i-created-an-instruction-dataset-using-gpt-3-5-to-fine-tune-llama-2-for-news-classification-ed02fe41c81f", ")", "kshitiz", "sahay", ":", "use", "gpt", "3.5", "create", "instruction", "dataset", "fine-tune", "llama", "2", "news", "classification", ".", "*", "[", "dataset", "creation", "fine-tuning", "llm", "]", "(", "http", ":", "//colab.research.google.com/drive/1gh8pw9-zae4cxezyoie-t9uhxblildag", "?", "usp=sharing", ")", ":", "notebook", "contains", "technique", "filter", "dataset", "upload", "result", ".", "*", "[", "chat", "template", "]", "(", "http", ":", "//huggingface.co/blog/chat-templates", ")", "matthew", "carrigan", ":", "hugging", "face", "'s", "page", "prompt", "template", "--", "-" ], [ "2 .", "building instruction dataset 's easy find raw data wikipedia website , 's difficult collect pair instruction answer wild .", "like traditional machine learning , quality dataset directly influence quality model , might important component fine-tuning process .", "* * * [ alpaca ] ( http : //crfm.stanford.edu/2023/03/13/alpaca.html ) -like dataset * * : generate synthetic data scratch openai api ( gpt ) .", "specify seed system prompt create diverse dataset .", "* * * advanced technique * * : learn improve existing datasets [ evol-instruct ] ( http : //arxiv.org/abs/2304.12244 ) , generate high-quality synthetic data like [ orca ] ( http : //arxiv.org/abs/2306.02707 ) [ phi-1 ] ( http : //arxiv.org/abs/2306.11644 ) paper .", "* * * filtering data * * : traditional technique involving regex , removing near-duplicates , focusing answer high number token , etc .", "* * * prompt template * * : 's true standard way formatting instruction answer , 's important know different chat template , [ chatml ] ( http : //learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt ? tabs=python & pivots=programming-language-chat-ml ) , [ alpaca ] ( http : //crfm.stanford.edu/2023/03/13/alpaca.html ) , etc .", "📚 * * reference * * : * [ preparing dataset instruction tuning ] ( http : //wandb.ai/capecape/alpaca_ft/reports/how-to-fine-tune-an-llm-part-1-preparing-a-dataset-for-instruction-tuning -- vmlldzo1ntcxnze2 ) thomas capelle : exploration alpaca alpaca-gpt4 datasets format .", "* [ generating clinical instruction dataset ] ( http : //medium.com/mlearning-ai/generating-a-clinical-instruction-dataset-in-portuguese-with-langchain-and-gpt-4-6ee9abfa41ae ) solano todeschini : tutorial create synthetic instruction dataset using gpt-4 .", "* [ gpt 3.5 news classification ] ( http : //medium.com/ @ kshitiz.sahay26/how-i-created-an-instruction-dataset-using-gpt-3-5-to-fine-tune-llama-2-for-news-classification-ed02fe41c81f ) kshitiz sahay : use gpt 3.5 create instruction dataset fine-tune llama 2 news classification .", "* [ dataset creation fine-tuning llm ] ( http : //colab.research.google.com/drive/1gh8pw9-zae4cxezyoie-t9uhxblildag ? usp=sharing ) : notebook contains technique filter dataset upload result .", "* [ chat template ] ( http : //huggingface.co/blog/chat-templates ) matthew carrigan : hugging face 's page prompt template -- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "3", ".", "pre-training", "model", "pre-training", "long", "costly", "process", ",", "focus", "course", ".", "'s", "good", "level", "understanding", "happens", "pre-training", ",", "hands-on", "experience", "required", ".", "*", "*", "*", "data", "pipeline", "*", "*", ":", "pre-training", "requires", "huge", "datasets", "(", "e.g.", ",", "[", "llama", "2", "]", "(", "http", ":", "//arxiv.org/abs/2307.09288", ")", "trained", "2", "trillion", "token", ")", "need", "filtered", ",", "tokenized", ",", "collated", "pre-defined", "vocabulary", ".", "*", "*", "*", "causal", "language", "modeling", "*", "*", ":", "learn", "difference", "causal", "masked", "language", "modeling", ",", "well", "loss", "function", "used", "case", ".", "efficient", "pre-training", ",", "learn", "[", "megatron-lm", "]", "(", "http", ":", "//github.com/nvidia/megatron-lm", ")", "[", "gpt-neox", "]", "(", "http", ":", "//github.com/eleutherai/gpt-neox", ")", ".", "*", "*", "*", "scaling", "law", "*", "*", ":", "[", "scaling", "law", "]", "(", "http", ":", "//arxiv.org/pdf/2001.08361.pdf", ")", "describe", "expected", "model", "performance", "based", "model", "size", ",", "dataset", "size", ",", "amount", "compute", "used", "training", ".", "*", "*", "*", "high-performance", "computing", "*", "*", ":", "scope", ",", "knowledge", "hpc", "fundamental", "'re", "planning", "create", "llm", "scratch", "(", "hardware", ",", "distributed", "workload", ",", "etc", ".", ")", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "llmdatahub", "]", "(", "http", ":", "//github.com/zjh-819/llmdatahub", ")", "junhao", "zhao", ":", "curated", "list", "datasets", "pre-training", ",", "fine-tuning", ",", "rlhf", ".", "*", "[", "training", "causal", "language", "model", "scratch", "]", "(", "http", ":", "//huggingface.co/learn/nlp-course/chapter7/6", "?", "fw=pt", ")", "hugging", "face", ":", "pre-train", "gpt-2", "model", "scratch", "using", "transformer", "library", ".", "*", "[", "tinyllama", "]", "(", "http", ":", "//github.com/jzhang38/tinyllama", ")", "zhang", "et", "al", ".", ":", "check", "project", "get", "good", "understanding", "llama", "model", "trained", "scratch", ".", "*", "[", "causal", "language", "modeling", "]", "(", "http", ":", "//huggingface.co/docs/transformers/tasks/language_modeling", ")", "hugging", "face", ":", "explain", "difference", "causal", "masked", "language", "modeling", "quickly", "fine-tune", "distilgpt-2", "model", ".", "*", "[", "chinchilla", "'s", "wild", "implication", "]", "(", "http", ":", "//www.lesswrong.com/posts/6fpvch8rr29qlewnh/chinchilla-s-wild-implications", ")", "nostalgebraist", ":", "discus", "scaling", "law", "explain", "mean", "llm", "general", ".", "*", "[", "bloom", "]", "(", "http", ":", "//bigscience.notion.site/bloom-bigscience-176b-model-ad073ca07cdf479398d5f95d88e218c4", ")", "bigscience", ":", "notion", "page", "describes", "bloom", "model", "built", ",", "lot", "useful", "information", "engineering", "part", "problem", "encountered", ".", "*", "[", "opt-175", "logbook", "]", "(", "http", ":", "//github.com/facebookresearch/metaseq/blob/main/projects/opt/chronicles/opt175b_logbook.pdf", ")", "meta", ":", "research", "log", "showing", "went", "wrong", "went", "right", ".", "useful", "'re", "planning", "pre-train", "large", "language", "model", "(", "case", ",", "175b", "parameter", ")", ".", "*", "[", "llm", "360", "]", "(", "http", ":", "//www.llm360.ai/", ")", ":", "framework", "open-source", "llm", "training", "data", "preparation", "code", ",", "data", ",", "metric", ",", "model", ".", "--", "-" ], [ "3 .", "pre-training model pre-training long costly process , focus course .", "'s good level understanding happens pre-training , hands-on experience required .", "* * * data pipeline * * : pre-training requires huge datasets ( e.g. , [ llama 2 ] ( http : //arxiv.org/abs/2307.09288 ) trained 2 trillion token ) need filtered , tokenized , collated pre-defined vocabulary .", "* * * causal language modeling * * : learn difference causal masked language modeling , well loss function used case .", "efficient pre-training , learn [ megatron-lm ] ( http : //github.com/nvidia/megatron-lm ) [ gpt-neox ] ( http : //github.com/eleutherai/gpt-neox ) .", "* * * scaling law * * : [ scaling law ] ( http : //arxiv.org/pdf/2001.08361.pdf ) describe expected model performance based model size , dataset size , amount compute used training .", "* * * high-performance computing * * : scope , knowledge hpc fundamental 're planning create llm scratch ( hardware , distributed workload , etc . ) .", "📚 * * reference * * : * [ llmdatahub ] ( http : //github.com/zjh-819/llmdatahub ) junhao zhao : curated list datasets pre-training , fine-tuning , rlhf .", "* [ training causal language model scratch ] ( http : //huggingface.co/learn/nlp-course/chapter7/6 ? fw=pt ) hugging face : pre-train gpt-2 model scratch using transformer library .", "* [ tinyllama ] ( http : //github.com/jzhang38/tinyllama ) zhang et al .", ": check project get good understanding llama model trained scratch .", "* [ causal language modeling ] ( http : //huggingface.co/docs/transformers/tasks/language_modeling ) hugging face : explain difference causal masked language modeling quickly fine-tune distilgpt-2 model .", "* [ chinchilla 's wild implication ] ( http : //www.lesswrong.com/posts/6fpvch8rr29qlewnh/chinchilla-s-wild-implications ) nostalgebraist : discus scaling law explain mean llm general .", "* [ bloom ] ( http : //bigscience.notion.site/bloom-bigscience-176b-model-ad073ca07cdf479398d5f95d88e218c4 ) bigscience : notion page describes bloom model built , lot useful information engineering part problem encountered .", "* [ opt-175 logbook ] ( http : //github.com/facebookresearch/metaseq/blob/main/projects/opt/chronicles/opt175b_logbook.pdf ) meta : research log showing went wrong went right .", "useful 're planning pre-train large language model ( case , 175b parameter ) .", "* [ llm 360 ] ( http : //www.llm360.ai/ ) : framework open-source llm training data preparation code , data , metric , model .", "-- -" ] ]

[ [ "3", ".", "pre-training", "model", "pre-training", "long", "costly", "process", ",", "focus", "course", ".", "'s", "good", "level", "understanding", "happens", "pre-training", ",", "hands-on", "experience", "required", ".", "*", "*", "*", "data", "pipeline", "*", "*", ":", "pre-training", "requires", "huge", "datasets", "(", "e.g.", ",", "[", "llama", "2", "]", "(", "http", ":", "//arxiv.org/abs/2307.09288", ")", "trained", "2", "trillion", "token", ")", "need", "filtered", ",", "tokenized", ",", "collated", "pre-defined", "vocabulary", ".", "*", "*", "*", "causal", "language", "modeling", "*", "*", ":", "learn", "difference", "causal", "masked", "language", "modeling", ",", "well", "loss", "function", "used", "case", ".", "efficient", "pre-training", ",", "learn", "[", "megatron-lm", "]", "(", "http", ":", "//github.com/nvidia/megatron-lm", ")", "[", "gpt-neox", "]", "(", "http", ":", "//github.com/eleutherai/gpt-neox", ")", ".", "*", "*", "*", "scaling", "law", "*", "*", ":", "[", "scaling", "law", "]", "(", "http", ":", "//arxiv.org/pdf/2001.08361.pdf", ")", "describe", "expected", "model", "performance", "based", "model", "size", ",", "dataset", "size", ",", "amount", "compute", "used", "training", ".", "*", "*", "*", "high-performance", "computing", "*", "*", ":", "scope", ",", "knowledge", "hpc", "fundamental", "'re", "planning", "create", "llm", "scratch", "(", "hardware", ",", "distributed", "workload", ",", "etc", ".", ")", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "llmdatahub", "]", "(", "http", ":", "//github.com/zjh-819/llmdatahub", ")", "junhao", "zhao", ":", "curated", "list", "datasets", "pre-training", ",", "fine-tuning", ",", "rlhf", ".", "*", "[", "training", "causal", "language", "model", "scratch", "]", "(", "http", ":", "//huggingface.co/learn/nlp-course/chapter7/6", "?", "fw=pt", ")", "hugging", "face", ":", "pre-train", "gpt-2", "model", "scratch", "using", "transformer", "library", ".", "*", "[", "tinyllama", "]", "(", "http", ":", "//github.com/jzhang38/tinyllama", ")", "zhang", "et", "al", ".", ":", "check", "project", "get", "good", "understanding", "llama", "model", "trained", "scratch", ".", "*", "[", "causal", "language", "modeling", "]", "(", "http", ":", "//huggingface.co/docs/transformers/tasks/language_modeling", ")", "hugging", "face", ":", "explain", "difference", "causal", "masked", "language", "modeling", "quickly", "fine-tune", "distilgpt-2", "model", ".", "*", "[", "chinchilla", "'s", "wild", "implication", "]", "(", "http", ":", "//www.lesswrong.com/posts/6fpvch8rr29qlewnh/chinchilla-s-wild-implications", ")", "nostalgebraist", ":", "discus", "scaling", "law", "explain", "mean", "llm", "general", ".", "*", "[", "bloom", "]", "(", "http", ":", "//bigscience.notion.site/bloom-bigscience-176b-model-ad073ca07cdf479398d5f95d88e218c4", ")", "bigscience", ":", "notion", "page", "describes", "bloom", "model", "built", ",", "lot", "useful", "information", "engineering", "part", "problem", "encountered", ".", "*", "[", "opt-175", "logbook", "]", "(", "http", ":", "//github.com/facebookresearch/metaseq/blob/main/projects/opt/chronicles/opt175b_logbook.pdf", ")", "meta", ":", "research", "log", "showing", "went", "wrong", "went", "right", ".", "useful", "'re", "planning", "pre-train", "large", "language", "model", "(", "case", ",", "175b", "parameter", ")", ".", "*", "[", "llm", "360", "]", "(", "http", ":", "//www.llm360.ai/", ")", ":", "framework", "open-source", "llm", "training", "data", "preparation", "code", ",", "data", ",", "metric", ",", "model", ".", "--", "-" ], [ "3 .", "pre-training model pre-training long costly process , focus course .", "'s good level understanding happens pre-training , hands-on experience required .", "* * * data pipeline * * : pre-training requires huge datasets ( e.g. , [ llama 2 ] ( http : //arxiv.org/abs/2307.09288 ) trained 2 trillion token ) need filtered , tokenized , collated pre-defined vocabulary .", "* * * causal language modeling * * : learn difference causal masked language modeling , well loss function used case .", "efficient pre-training , learn [ megatron-lm ] ( http : //github.com/nvidia/megatron-lm ) [ gpt-neox ] ( http : //github.com/eleutherai/gpt-neox ) .", "* * * scaling law * * : [ scaling law ] ( http : //arxiv.org/pdf/2001.08361.pdf ) describe expected model performance based model size , dataset size , amount compute used training .", "* * * high-performance computing * * : scope , knowledge hpc fundamental 're planning create llm scratch ( hardware , distributed workload , etc . ) .", "📚 * * reference * * : * [ llmdatahub ] ( http : //github.com/zjh-819/llmdatahub ) junhao zhao : curated list datasets pre-training , fine-tuning , rlhf .", "* [ training causal language model scratch ] ( http : //huggingface.co/learn/nlp-course/chapter7/6 ? fw=pt ) hugging face : pre-train gpt-2 model scratch using transformer library .", "* [ tinyllama ] ( http : //github.com/jzhang38/tinyllama ) zhang et al .", ": check project get good understanding llama model trained scratch .", "* [ causal language modeling ] ( http : //huggingface.co/docs/transformers/tasks/language_modeling ) hugging face : explain difference causal masked language modeling quickly fine-tune distilgpt-2 model .", "* [ chinchilla 's wild implication ] ( http : //www.lesswrong.com/posts/6fpvch8rr29qlewnh/chinchilla-s-wild-implications ) nostalgebraist : discus scaling law explain mean llm general .", "* [ bloom ] ( http : //bigscience.notion.site/bloom-bigscience-176b-model-ad073ca07cdf479398d5f95d88e218c4 ) bigscience : notion page describes bloom model built , lot useful information engineering part problem encountered .", "* [ opt-175 logbook ] ( http : //github.com/facebookresearch/metaseq/blob/main/projects/opt/chronicles/opt175b_logbook.pdf ) meta : research log showing went wrong went right .", "useful 're planning pre-train large language model ( case , 175b parameter ) .", "* [ llm 360 ] ( http : //www.llm360.ai/ ) : framework open-source llm training data preparation code , data , metric , model .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "4", ".", "supervised", "fine-tuning", "pre-trained", "model", "trained", "next-token", "prediction", "task", ",", "'re", "helpful", "assistant", ".", "sft", "allows", "tweak", "respond", "instruction", ".", "moreover", ",", "allows", "fine-tune", "model", "data", "(", "private", ",", "seen", "gpt-4", ",", "etc", ".", ")", "use", "without", "pay", "api", "like", "openai", "'s", ".", "*", "*", "*", "full", "fine-tuning", "*", "*", ":", "full", "fine-tuning", "refers", "training", "parameter", "model", ".", "efficient", "technique", ",", "produce", "slightly", "better", "result", ".", "*", "[", "*", "*", "lora", "*", "*", "]", "(", "http", ":", "//arxiv.org/abs/2106.09685", ")", ":", "parameter-efficient", "technique", "(", "peft", ")", "based", "low-rank", "adapter", ".", "instead", "training", "parameter", ",", "train", "adapter", ".", "*", "[", "*", "*", "qlora", "*", "*", "]", "(", "http", ":", "//arxiv.org/abs/2305.14314", ")", ":", "another", "peft", "based", "lora", ",", "also", "quantizes", "weight", "model", "4", "bit", "introduce", "paged", "optimizers", "manage", "memory", "spike", ".", "combine", "[", "unsloth", "]", "(", "http", ":", "//github.com/unslothai/unsloth", ")", "run", "efficiently", "free", "colab", "notebook", ".", "*", "*", "*", "[", "axolotl", "]", "(", "http", ":", "//github.com/openaccess-ai-collective/axolotl", ")", "*", "*", ":", "user-friendly", "powerful", "fine-tuning", "tool", "used", "lot", "state-of-the-art", "open-source", "model", ".", "*", "[", "*", "*", "deepspeed", "*", "*", "]", "(", "http", ":", "//www.deepspeed.ai/", ")", ":", "efficient", "pre-training", "fine-tuning", "llm", "multi-gpu", "multi-node", "setting", "(", "implemented", "axolotl", ")", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "novice", "'s", "llm", "training", "guide", "]", "(", "http", ":", "//rentry.org/llm-training", ")", "alpin", ":", "overview", "main", "concept", "parameter", "consider", "fine-tuning", "llm", ".", "*", "[", "lora", "insight", "]", "(", "http", ":", "//lightning.ai/pages/community/lora-insights/", ")", "sebastian", "raschka", ":", "practical", "insight", "lora", "select", "best", "parameter", ".", "*", "[", "fine-tune", "llama", "2", "model", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/fine_tune_your_own_llama_2_model_in_a_colab_notebook.html", ")", ":", "hands-on", "tutorial", "fine-tune", "llama", "2", "model", "using", "hugging", "face", "library", ".", "*", "[", "padding", "large", "language", "model", "]", "(", "http", ":", "//towardsdatascience.com/padding-large-language-models-examples-with-llama-2-199fb10df8ff", ")", "benjamin", "marie", ":", "best", "practice", "pad", "training", "example", "causal", "llm", "*", "[", "beginner", "'s", "guide", "llm", "fine-tuning", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/a_beginners_guide_to_llm_finetuning.html", ")", ":", "tutorial", "fine-tune", "codellama", "model", "using", "axolotl", ".", "--", "-" ], [ "4 .", "supervised fine-tuning pre-trained model trained next-token prediction task , 're helpful assistant .", "sft allows tweak respond instruction .", "moreover , allows fine-tune model data ( private , seen gpt-4 , etc . )", "use without pay api like openai 's .", "* * * full fine-tuning * * : full fine-tuning refers training parameter model .", "efficient technique , produce slightly better result .", "* [ * * lora * * ] ( http : //arxiv.org/abs/2106.09685 ) : parameter-efficient technique ( peft ) based low-rank adapter .", "instead training parameter , train adapter .", "* [ * * qlora * * ] ( http : //arxiv.org/abs/2305.14314 ) : another peft based lora , also quantizes weight model 4 bit introduce paged optimizers manage memory spike .", "combine [ unsloth ] ( http : //github.com/unslothai/unsloth ) run efficiently free colab notebook .", "* * * [ axolotl ] ( http : //github.com/openaccess-ai-collective/axolotl ) * * : user-friendly powerful fine-tuning tool used lot state-of-the-art open-source model .", "* [ * * deepspeed * * ] ( http : //www.deepspeed.ai/ ) : efficient pre-training fine-tuning llm multi-gpu multi-node setting ( implemented axolotl ) .", "📚 * * reference * * : * [ novice 's llm training guide ] ( http : //rentry.org/llm-training ) alpin : overview main concept parameter consider fine-tuning llm .", "* [ lora insight ] ( http : //lightning.ai/pages/community/lora-insights/ ) sebastian raschka : practical insight lora select best parameter .", "* [ fine-tune llama 2 model ] ( http : //mlabonne.github.io/blog/posts/fine_tune_your_own_llama_2_model_in_a_colab_notebook.html ) : hands-on tutorial fine-tune llama 2 model using hugging face library .", "* [ padding large language model ] ( http : //towardsdatascience.com/padding-large-language-models-examples-with-llama-2-199fb10df8ff ) benjamin marie : best practice pad training example causal llm * [ beginner 's guide llm fine-tuning ] ( http : //mlabonne.github.io/blog/posts/a_beginners_guide_to_llm_finetuning.html ) : tutorial fine-tune codellama model using axolotl .", "-- -" ] ]

[ [ "4", ".", "supervised", "fine-tuning", "pre-trained", "model", "trained", "next-token", "prediction", "task", ",", "'re", "helpful", "assistant", ".", "sft", "allows", "tweak", "respond", "instruction", ".", "moreover", ",", "allows", "fine-tune", "model", "data", "(", "private", ",", "seen", "gpt-4", ",", "etc", ".", ")", "use", "without", "pay", "api", "like", "openai", "'s", ".", "*", "*", "*", "full", "fine-tuning", "*", "*", ":", "full", "fine-tuning", "refers", "training", "parameter", "model", ".", "efficient", "technique", ",", "produce", "slightly", "better", "result", ".", "*", "[", "*", "*", "lora", "*", "*", "]", "(", "http", ":", "//arxiv.org/abs/2106.09685", ")", ":", "parameter-efficient", "technique", "(", "peft", ")", "based", "low-rank", "adapter", ".", "instead", "training", "parameter", ",", "train", "adapter", ".", "*", "[", "*", "*", "qlora", "*", "*", "]", "(", "http", ":", "//arxiv.org/abs/2305.14314", ")", ":", "another", "peft", "based", "lora", ",", "also", "quantizes", "weight", "model", "4", "bit", "introduce", "paged", "optimizers", "manage", "memory", "spike", ".", "combine", "[", "unsloth", "]", "(", "http", ":", "//github.com/unslothai/unsloth", ")", "run", "efficiently", "free", "colab", "notebook", ".", "*", "*", "*", "[", "axolotl", "]", "(", "http", ":", "//github.com/openaccess-ai-collective/axolotl", ")", "*", "*", ":", "user-friendly", "powerful", "fine-tuning", "tool", "used", "lot", "state-of-the-art", "open-source", "model", ".", "*", "[", "*", "*", "deepspeed", "*", "*", "]", "(", "http", ":", "//www.deepspeed.ai/", ")", ":", "efficient", "pre-training", "fine-tuning", "llm", "multi-gpu", "multi-node", "setting", "(", "implemented", "axolotl", ")", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "novice", "'s", "llm", "training", "guide", "]", "(", "http", ":", "//rentry.org/llm-training", ")", "alpin", ":", "overview", "main", "concept", "parameter", "consider", "fine-tuning", "llm", ".", "*", "[", "lora", "insight", "]", "(", "http", ":", "//lightning.ai/pages/community/lora-insights/", ")", "sebastian", "raschka", ":", "practical", "insight", "lora", "select", "best", "parameter", ".", "*", "[", "fine-tune", "llama", "2", "model", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/fine_tune_your_own_llama_2_model_in_a_colab_notebook.html", ")", ":", "hands-on", "tutorial", "fine-tune", "llama", "2", "model", "using", "hugging", "face", "library", ".", "*", "[", "padding", "large", "language", "model", "]", "(", "http", ":", "//towardsdatascience.com/padding-large-language-models-examples-with-llama-2-199fb10df8ff", ")", "benjamin", "marie", ":", "best", "practice", "pad", "training", "example", "causal", "llm", "*", "[", "beginner", "'s", "guide", "llm", "fine-tuning", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/a_beginners_guide_to_llm_finetuning.html", ")", ":", "tutorial", "fine-tune", "codellama", "model", "using", "axolotl", ".", "--", "-" ], [ "4 .", "supervised fine-tuning pre-trained model trained next-token prediction task , 're helpful assistant .", "sft allows tweak respond instruction .", "moreover , allows fine-tune model data ( private , seen gpt-4 , etc . )", "use without pay api like openai 's .", "* * * full fine-tuning * * : full fine-tuning refers training parameter model .", "efficient technique , produce slightly better result .", "* [ * * lora * * ] ( http : //arxiv.org/abs/2106.09685 ) : parameter-efficient technique ( peft ) based low-rank adapter .", "instead training parameter , train adapter .", "* [ * * qlora * * ] ( http : //arxiv.org/abs/2305.14314 ) : another peft based lora , also quantizes weight model 4 bit introduce paged optimizers manage memory spike .", "combine [ unsloth ] ( http : //github.com/unslothai/unsloth ) run efficiently free colab notebook .", "* * * [ axolotl ] ( http : //github.com/openaccess-ai-collective/axolotl ) * * : user-friendly powerful fine-tuning tool used lot state-of-the-art open-source model .", "* [ * * deepspeed * * ] ( http : //www.deepspeed.ai/ ) : efficient pre-training fine-tuning llm multi-gpu multi-node setting ( implemented axolotl ) .", "📚 * * reference * * : * [ novice 's llm training guide ] ( http : //rentry.org/llm-training ) alpin : overview main concept parameter consider fine-tuning llm .", "* [ lora insight ] ( http : //lightning.ai/pages/community/lora-insights/ ) sebastian raschka : practical insight lora select best parameter .", "* [ fine-tune llama 2 model ] ( http : //mlabonne.github.io/blog/posts/fine_tune_your_own_llama_2_model_in_a_colab_notebook.html ) : hands-on tutorial fine-tune llama 2 model using hugging face library .", "* [ padding large language model ] ( http : //towardsdatascience.com/padding-large-language-models-examples-with-llama-2-199fb10df8ff ) benjamin marie : best practice pad training example causal llm * [ beginner 's guide llm fine-tuning ] ( http : //mlabonne.github.io/blog/posts/a_beginners_guide_to_llm_finetuning.html ) : tutorial fine-tune codellama model using axolotl .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "6", ".", "evaluation", "evaluating", "llm", "undervalued", "part", "pipeline", ",", "time-consuming", "moderately", "reliable", ".", "downstream", "task", "dictate", "want", "evaluate", ",", "always", "remember", "goodhart", "'s", "law", ":", "``", "measure", "becomes", "target", ",", "cease", "good", "measure", ".", "''", "*", "*", "*", "traditional", "metric", "*", "*", ":", "metric", "like", "perplexity", "bleu", "score", "popular", "'re", "flawed", "context", ".", "still", "important", "understand", "applied", ".", "*", "*", "*", "general", "benchmark", "*", "*", ":", "based", "[", "language", "model", "evaluation", "harness", "]", "(", "http", ":", "//github.com/eleutherai/lm-evaluation-harness", ")", ",", "[", "open", "llm", "leaderboard", "]", "(", "http", ":", "//huggingface.co/spaces/huggingfaceh4/open_llm_leaderboard", ")", "main", "benchmark", "general-purpose", "llm", "(", "like", "chatgpt", ")", ".", "popular", "benchmark", "like", "[", "bigbench", "]", "(", "http", ":", "//github.com/google/big-bench", ")", ",", "[", "mt-bench", "]", "(", "http", ":", "//arxiv.org/abs/2306.05685", ")", ",", "etc", ".", "*", "*", "*", "task-specific", "benchmark", "*", "*", ":", "task", "like", "summarization", ",", "translation", ",", "question", "answering", "dedicated", "benchmark", ",", "metric", ",", "even", "subdomains", "(", "medical", ",", "financial", ",", "etc", ".", ")", ",", "[", "pubmedqa", "]", "(", "http", ":", "//pubmedqa.github.io/", ")", "biomedical", "question", "answering", ".", "*", "*", "*", "human", "evaluation", "*", "*", ":", "reliable", "evaluation", "acceptance", "rate", "user", "comparison", "made", "human", ".", "want", "know", "model", "performs", "well", ",", "simplest", "surest", "way", "use", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "perplexity", "fixed-length", "model", "]", "(", "http", ":", "//huggingface.co/docs/transformers/perplexity", ")", "hugging", "face", ":", "overview", "perplexity", "code", "implement", "transformer", "library", ".", "*", "[", "bleu", "risk", "]", "(", "http", ":", "//towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213", ")", "rachael", "tatman", ":", "overview", "bleu", "score", "many", "issue", "example", ".", "*", "[", "survey", "evaluation", "llm", "]", "(", "http", ":", "//arxiv.org/abs/2307.03109", ")", "chang", "et", "al", ".", ":", "comprehensive", "paper", "evaluate", ",", "evaluate", ",", "evaluate", ".", "*", "[", "chatbot", "arena", "leaderboard", "]", "(", "http", ":", "//huggingface.co/spaces/lmsys/chatbot-arena-leaderboard", ")", "lmsys", ":", "elo", "rating", "general-purpose", "llm", ",", "based", "comparison", "made", "human", ".", "--", "-" ], [ "6 .", "evaluation evaluating llm undervalued part pipeline , time-consuming moderately reliable .", "downstream task dictate want evaluate , always remember goodhart 's law : `` measure becomes target , cease good measure . ''", "* * * traditional metric * * : metric like perplexity bleu score popular 're flawed context .", "still important understand applied .", "* * * general benchmark * * : based [ language model evaluation harness ] ( http : //github.com/eleutherai/lm-evaluation-harness ) , [ open llm leaderboard ] ( http : //huggingface.co/spaces/huggingfaceh4/open_llm_leaderboard ) main benchmark general-purpose llm ( like chatgpt ) .", "popular benchmark like [ bigbench ] ( http : //github.com/google/big-bench ) , [ mt-bench ] ( http : //arxiv.org/abs/2306.05685 ) , etc .", "* * * task-specific benchmark * * : task like summarization , translation , question answering dedicated benchmark , metric , even subdomains ( medical , financial , etc .", ") , [ pubmedqa ] ( http : //pubmedqa.github.io/ ) biomedical question answering .", "* * * human evaluation * * : reliable evaluation acceptance rate user comparison made human .", "want know model performs well , simplest surest way use .", "📚 * * reference * * : * [ perplexity fixed-length model ] ( http : //huggingface.co/docs/transformers/perplexity ) hugging face : overview perplexity code implement transformer library .", "* [ bleu risk ] ( http : //towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213 ) rachael tatman : overview bleu score many issue example .", "* [ survey evaluation llm ] ( http : //arxiv.org/abs/2307.03109 ) chang et al .", ": comprehensive paper evaluate , evaluate , evaluate .", "* [ chatbot arena leaderboard ] ( http : //huggingface.co/spaces/lmsys/chatbot-arena-leaderboard ) lmsys : elo rating general-purpose llm , based comparison made human .", "-- -" ] ]

[ [ "6", ".", "evaluation", "evaluating", "llm", "undervalued", "part", "pipeline", ",", "time-consuming", "moderately", "reliable", ".", "downstream", "task", "dictate", "want", "evaluate", ",", "always", "remember", "goodhart", "'s", "law", ":", "``", "measure", "becomes", "target", ",", "cease", "good", "measure", ".", "''", "*", "*", "*", "traditional", "metric", "*", "*", ":", "metric", "like", "perplexity", "bleu", "score", "popular", "'re", "flawed", "context", ".", "still", "important", "understand", "applied", ".", "*", "*", "*", "general", "benchmark", "*", "*", ":", "based", "[", "language", "model", "evaluation", "harness", "]", "(", "http", ":", "//github.com/eleutherai/lm-evaluation-harness", ")", ",", "[", "open", "llm", "leaderboard", "]", "(", "http", ":", "//huggingface.co/spaces/huggingfaceh4/open_llm_leaderboard", ")", "main", "benchmark", "general-purpose", "llm", "(", "like", "chatgpt", ")", ".", "popular", "benchmark", "like", "[", "bigbench", "]", "(", "http", ":", "//github.com/google/big-bench", ")", ",", "[", "mt-bench", "]", "(", "http", ":", "//arxiv.org/abs/2306.05685", ")", ",", "etc", ".", "*", "*", "*", "task-specific", "benchmark", "*", "*", ":", "task", "like", "summarization", ",", "translation", ",", "question", "answering", "dedicated", "benchmark", ",", "metric", ",", "even", "subdomains", "(", "medical", ",", "financial", ",", "etc", ".", ")", ",", "[", "pubmedqa", "]", "(", "http", ":", "//pubmedqa.github.io/", ")", "biomedical", "question", "answering", ".", "*", "*", "*", "human", "evaluation", "*", "*", ":", "reliable", "evaluation", "acceptance", "rate", "user", "comparison", "made", "human", ".", "want", "know", "model", "performs", "well", ",", "simplest", "surest", "way", "use", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "perplexity", "fixed-length", "model", "]", "(", "http", ":", "//huggingface.co/docs/transformers/perplexity", ")", "hugging", "face", ":", "overview", "perplexity", "code", "implement", "transformer", "library", ".", "*", "[", "bleu", "risk", "]", "(", "http", ":", "//towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213", ")", "rachael", "tatman", ":", "overview", "bleu", "score", "many", "issue", "example", ".", "*", "[", "survey", "evaluation", "llm", "]", "(", "http", ":", "//arxiv.org/abs/2307.03109", ")", "chang", "et", "al", ".", ":", "comprehensive", "paper", "evaluate", ",", "evaluate", ",", "evaluate", ".", "*", "[", "chatbot", "arena", "leaderboard", "]", "(", "http", ":", "//huggingface.co/spaces/lmsys/chatbot-arena-leaderboard", ")", "lmsys", ":", "elo", "rating", "general-purpose", "llm", ",", "based", "comparison", "made", "human", ".", "--", "-" ], [ "6 .", "evaluation evaluating llm undervalued part pipeline , time-consuming moderately reliable .", "downstream task dictate want evaluate , always remember goodhart 's law : `` measure becomes target , cease good measure . ''", "* * * traditional metric * * : metric like perplexity bleu score popular 're flawed context .", "still important understand applied .", "* * * general benchmark * * : based [ language model evaluation harness ] ( http : //github.com/eleutherai/lm-evaluation-harness ) , [ open llm leaderboard ] ( http : //huggingface.co/spaces/huggingfaceh4/open_llm_leaderboard ) main benchmark general-purpose llm ( like chatgpt ) .", "popular benchmark like [ bigbench ] ( http : //github.com/google/big-bench ) , [ mt-bench ] ( http : //arxiv.org/abs/2306.05685 ) , etc .", "* * * task-specific benchmark * * : task like summarization , translation , question answering dedicated benchmark , metric , even subdomains ( medical , financial , etc .", ") , [ pubmedqa ] ( http : //pubmedqa.github.io/ ) biomedical question answering .", "* * * human evaluation * * : reliable evaluation acceptance rate user comparison made human .", "want know model performs well , simplest surest way use .", "📚 * * reference * * : * [ perplexity fixed-length model ] ( http : //huggingface.co/docs/transformers/perplexity ) hugging face : overview perplexity code implement transformer library .", "* [ bleu risk ] ( http : //towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213 ) rachael tatman : overview bleu score many issue example .", "* [ survey evaluation llm ] ( http : //arxiv.org/abs/2307.03109 ) chang et al .", ": comprehensive paper evaluate , evaluate , evaluate .", "* [ chatbot arena leaderboard ] ( http : //huggingface.co/spaces/lmsys/chatbot-arena-leaderboard ) lmsys : elo rating general-purpose llm , based comparison made human .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "7", ".", "quantization", "quantization", "process", "converting", "weight", "(", "activation", ")", "model", "using", "lower", "precision", ".", "example", ",", "weight", "stored", "using", "16", "bit", "converted", "4-bit", "representation", ".", "technique", "become", "increasingly", "important", "reduce", "computational", "memory", "cost", "associated", "llm", ".", "*", "*", "*", "base", "technique", "*", "*", ":", "learn", "different", "level", "precision", "(", "fp32", ",", "fp16", ",", "int8", ",", "etc", ".", ")", "perform", "naïve", "quantization", "absmax", "zero-point", "technique", ".", "*", "*", "*", "gguf", "llama.cpp", "*", "*", ":", "originally", "designed", "run", "cpu", ",", "[", "llama.cpp", "]", "(", "http", ":", "//github.com/ggerganov/llama.cpp", ")", "gguf", "format", "become", "popular", "tool", "run", "llm", "consumer-grade", "hardware", ".", "*", "*", "*", "gptq", "exl2", "*", "*", ":", "[", "gptq", "]", "(", "http", ":", "//arxiv.org/abs/2210.17323", ")", ",", "specifically", ",", "[", "exl2", "]", "(", "http", ":", "//github.com/turboderp/exllamav2", ")", "format", "offer", "incredible", "speed", "run", "gpus", ".", "model", "also", "take", "long", "time", "quantized", ".", "*", "*", "*", "awq", "*", "*", ":", "new", "format", "accurate", "gptq", "(", "lower", "perplexity", ")", "us", "lot", "vram", "necessarily", "faster", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "introduction", "quantization", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/introduction_to_weight_quantization.html", ")", ":", "overview", "quantization", ",", "absmax", "zero-point", "quantization", ",", "llm.int8", "(", ")", "code", ".", "*", "[", "quantize", "llama", "model", "llama.cpp", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/quantize_llama_2_models_using_ggml.html", ")", ":", "tutorial", "quantize", "llama", "2", "model", "using", "llama.cpp", "gguf", "format", ".", "*", "[", "4-bit", "llm", "quantization", "gptq", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/introduction_to_weight_quantization.html", ")", ":", "tutorial", "quantize", "llm", "using", "gptq", "algorithm", "autogptq", ".", "*", "[", "exllamav2", ":", "fastest", "library", "run", "llm", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/exllamav2_the_fastest_library_to_run", "%", "c2", "%", "a0llms.html", ")", ":", "guide", "quantize", "mistral", "model", "using", "exl2", "format", "run", "exllamav2", "library", ".", "*", "[", "understanding", "activation-aware", "weight", "quantization", "]", "(", "http", ":", "//medium.com/friendliai/understanding-activation-aware-weight-quantization-awq-boosting-inference-serving-efficiency-in-10bb0faf63a8", ")", "friendliai", ":", "overview", "awq", "technique", "benefit", ".", "--", "-" ], [ "7 .", "quantization quantization process converting weight ( activation ) model using lower precision .", "example , weight stored using 16 bit converted 4-bit representation .", "technique become increasingly important reduce computational memory cost associated llm .", "* * * base technique * * : learn different level precision ( fp32 , fp16 , int8 , etc . )", "perform naïve quantization absmax zero-point technique .", "* * * gguf llama.cpp * * : originally designed run cpu , [ llama.cpp ] ( http : //github.com/ggerganov/llama.cpp ) gguf format become popular tool run llm consumer-grade hardware .", "* * * gptq exl2 * * : [ gptq ] ( http : //arxiv.org/abs/2210.17323 ) , specifically , [ exl2 ] ( http : //github.com/turboderp/exllamav2 ) format offer incredible speed run gpus .", "model also take long time quantized .", "* * * awq * * : new format accurate gptq ( lower perplexity ) us lot vram necessarily faster .", "📚 * * reference * * : * [ introduction quantization ] ( http : //mlabonne.github.io/blog/posts/introduction_to_weight_quantization.html ) : overview quantization , absmax zero-point quantization , llm.int8 ( ) code .", "* [ quantize llama model llama.cpp ] ( http : //mlabonne.github.io/blog/posts/quantize_llama_2_models_using_ggml.html ) : tutorial quantize llama 2 model using llama.cpp gguf format .", "* [ 4-bit llm quantization gptq ] ( http : //mlabonne.github.io/blog/posts/introduction_to_weight_quantization.html ) : tutorial quantize llm using gptq algorithm autogptq .", "* [ exllamav2 : fastest library run llm ] ( http : //mlabonne.github.io/blog/posts/exllamav2_the_fastest_library_to_run % c2 % a0llms.html ) : guide quantize mistral model using exl2 format run exllamav2 library .", "* [ understanding activation-aware weight quantization ] ( http : //medium.com/friendliai/understanding-activation-aware-weight-quantization-awq-boosting-inference-serving-efficiency-in-10bb0faf63a8 ) friendliai : overview awq technique benefit .", "-- -" ] ]

[ [ "7", ".", "quantization", "quantization", "process", "converting", "weight", "(", "activation", ")", "model", "using", "lower", "precision", ".", "example", ",", "weight", "stored", "using", "16", "bit", "converted", "4-bit", "representation", ".", "technique", "become", "increasingly", "important", "reduce", "computational", "memory", "cost", "associated", "llm", ".", "*", "*", "*", "base", "technique", "*", "*", ":", "learn", "different", "level", "precision", "(", "fp32", ",", "fp16", ",", "int8", ",", "etc", ".", ")", "perform", "naïve", "quantization", "absmax", "zero-point", "technique", ".", "*", "*", "*", "gguf", "llama.cpp", "*", "*", ":", "originally", "designed", "run", "cpu", ",", "[", "llama.cpp", "]", "(", "http", ":", "//github.com/ggerganov/llama.cpp", ")", "gguf", "format", "become", "popular", "tool", "run", "llm", "consumer-grade", "hardware", ".", "*", "*", "*", "gptq", "exl2", "*", "*", ":", "[", "gptq", "]", "(", "http", ":", "//arxiv.org/abs/2210.17323", ")", ",", "specifically", ",", "[", "exl2", "]", "(", "http", ":", "//github.com/turboderp/exllamav2", ")", "format", "offer", "incredible", "speed", "run", "gpus", ".", "model", "also", "take", "long", "time", "quantized", ".", "*", "*", "*", "awq", "*", "*", ":", "new", "format", "accurate", "gptq", "(", "lower", "perplexity", ")", "us", "lot", "vram", "necessarily", "faster", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "introduction", "quantization", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/introduction_to_weight_quantization.html", ")", ":", "overview", "quantization", ",", "absmax", "zero-point", "quantization", ",", "llm.int8", "(", ")", "code", ".", "*", "[", "quantize", "llama", "model", "llama.cpp", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/quantize_llama_2_models_using_ggml.html", ")", ":", "tutorial", "quantize", "llama", "2", "model", "using", "llama.cpp", "gguf", "format", ".", "*", "[", "4-bit", "llm", "quantization", "gptq", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/introduction_to_weight_quantization.html", ")", ":", "tutorial", "quantize", "llm", "using", "gptq", "algorithm", "autogptq", ".", "*", "[", "exllamav2", ":", "fastest", "library", "run", "llm", "]", "(", "http", ":", "//mlabonne.github.io/blog/posts/exllamav2_the_fastest_library_to_run", "%", "c2", "%", "a0llms.html", ")", ":", "guide", "quantize", "mistral", "model", "using", "exl2", "format", "run", "exllamav2", "library", ".", "*", "[", "understanding", "activation-aware", "weight", "quantization", "]", "(", "http", ":", "//medium.com/friendliai/understanding-activation-aware-weight-quantization-awq-boosting-inference-serving-efficiency-in-10bb0faf63a8", ")", "friendliai", ":", "overview", "awq", "technique", "benefit", ".", "--", "-" ], [ "7 .", "quantization quantization process converting weight ( activation ) model using lower precision .", "example , weight stored using 16 bit converted 4-bit representation .", "technique become increasingly important reduce computational memory cost associated llm .", "* * * base technique * * : learn different level precision ( fp32 , fp16 , int8 , etc . )", "perform naïve quantization absmax zero-point technique .", "* * * gguf llama.cpp * * : originally designed run cpu , [ llama.cpp ] ( http : //github.com/ggerganov/llama.cpp ) gguf format become popular tool run llm consumer-grade hardware .", "* * * gptq exl2 * * : [ gptq ] ( http : //arxiv.org/abs/2210.17323 ) , specifically , [ exl2 ] ( http : //github.com/turboderp/exllamav2 ) format offer incredible speed run gpus .", "model also take long time quantized .", "* * * awq * * : new format accurate gptq ( lower perplexity ) us lot vram necessarily faster .", "📚 * * reference * * : * [ introduction quantization ] ( http : //mlabonne.github.io/blog/posts/introduction_to_weight_quantization.html ) : overview quantization , absmax zero-point quantization , llm.int8 ( ) code .", "* [ quantize llama model llama.cpp ] ( http : //mlabonne.github.io/blog/posts/quantize_llama_2_models_using_ggml.html ) : tutorial quantize llama 2 model using llama.cpp gguf format .", "* [ 4-bit llm quantization gptq ] ( http : //mlabonne.github.io/blog/posts/introduction_to_weight_quantization.html ) : tutorial quantize llm using gptq algorithm autogptq .", "* [ exllamav2 : fastest library run llm ] ( http : //mlabonne.github.io/blog/posts/exllamav2_the_fastest_library_to_run % c2 % a0llms.html ) : guide quantize mistral model using exl2 format run exllamav2 library .", "* [ understanding activation-aware weight quantization ] ( http : //medium.com/friendliai/understanding-activation-aware-weight-quantization-awq-boosting-inference-serving-efficiency-in-10bb0faf63a8 ) friendliai : overview awq technique benefit .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "👷", "llm", "engineer", "section", "course", "focus", "learning", "build", "llm-powered", "application", "used", "production", ",", "focus", "augmenting", "model", "deploying", ".", "!", "[", "]", "(", "img/roadmap_engineer.png", ")" ], [ "👷 llm engineer section course focus learning build llm-powered application used production , focus augmenting model deploying .", "!", "[ ] ( img/roadmap_engineer.png )" ] ]

[ [ "👷", "llm", "engineer", "section", "course", "focus", "learning", "build", "llm-powered", "application", "used", "production", ",", "focus", "augmenting", "model", "deploying", ".", "!", "[", "]", "(", "img/roadmap_engineer.png", ")" ], [ "👷 llm engineer section course focus learning build llm-powered application used production , focus augmenting model deploying .", "!", "[ ] ( img/roadmap_engineer.png )" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "1", ".", "running", "llm", "running", "llm", "difficult", "due", "high", "hardware", "requirement", ".", "depending", "use", "case", ",", "might", "want", "simply", "consume", "model", "api", "(", "like", "gpt-4", ")", "run", "locally", ".", "case", ",", "additional", "prompting", "guidance", "technique", "improve", "constrain", "output", "application", ".", "*", "*", "*", "llm", "apis", "*", "*", ":", "apis", "convenient", "way", "deploy", "llm", ".", "space", "divided", "private", "llm", "(", "[", "openai", "]", "(", "http", ":", "//platform.openai.com/", ")", ",", "[", "google", "]", "(", "http", ":", "//cloud.google.com/vertex-ai/docs/generative-ai/learn/overview", ")", ",", "[", "anthropic", "]", "(", "http", ":", "//docs.anthropic.com/claude/reference/getting-started-with-the-api", ")", ",", "[", "cohere", "]", "(", "http", ":", "//docs.cohere.com/docs", ")", ",", "etc", ".", ")", "open-source", "llm", "(", "[", "openrouter", "]", "(", "http", ":", "//openrouter.ai/", ")", ",", "[", "hugging", "face", "]", "(", "http", ":", "//huggingface.co/inference-api", ")", ",", "[", "together", "ai", "]", "(", "http", ":", "//www.together.ai/", ")", ",", "etc", ".", ")", ".", "*", "*", "*", "open-source", "llm", "*", "*", ":", "[", "hugging", "face", "hub", "]", "(", "http", ":", "//huggingface.co/models", ")", "great", "place", "find", "llm", ".", "directly", "run", "[", "hugging", "face", "space", "]", "(", "http", ":", "//huggingface.co/spaces", ")", ",", "download", "run", "locally", "apps", "like", "[", "lm", "studio", "]", "(", "http", ":", "//lmstudio.ai/", ")", "cli", "[", "llama.cpp", "]", "(", "http", ":", "//github.com/ggerganov/llama.cpp", ")", "[", "ollama", "]", "(", "http", ":", "//ollama.ai/", ")", ".", "*", "*", "*", "prompt", "engineering", "*", "*", ":", "common", "technique", "include", "zero-shot", "prompting", ",", "few-shot", "prompting", ",", "chain", "thought", ",", "react", ".", "work", "better", "bigger", "model", ",", "adapted", "smaller", "one", ".", "*", "*", "*", "structuring", "output", "*", "*", ":", "many", "task", "require", "structured", "output", ",", "like", "strict", "template", "json", "format", ".", "library", "like", "[", "lmql", "]", "(", "http", ":", "//lmql.ai/", ")", ",", "[", "outline", "]", "(", "http", ":", "//github.com/outlines-dev/outlines", ")", ",", "[", "guidance", "]", "(", "http", ":", "//github.com/guidance-ai/guidance", ")", ",", "etc", ".", "used", "guide", "generation", "respect", "given", "structure", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "run", "llm", "locally", "lm", "studio", "]", "(", "http", ":", "//www.kdnuggets.com/run-an-llm-locally-with-lm-studio", ")", "nisha", "arya", ":", "short", "guide", "use", "lm", "studio", ".", "*", "[", "prompt", "engineering", "guide", "]", "(", "http", ":", "//www.promptingguide.ai/", ")", "dair.ai", ":", "exhaustive", "list", "prompt", "technique", "example", "*", "[", "outline", "-", "quickstart", "]", "(", "http", ":", "//outlines-dev.github.io/outlines/quickstart/", ")", ":", "list", "guided", "generation", "technique", "enabled", "outline", ".", "*", "[", "lmql", "-", "overview", "]", "(", "http", ":", "//lmql.ai/docs/language/overview.html", ")", ":", "introduction", "lmql", "language", ".", "--", "-" ], [ "1 .", "running llm running llm difficult due high hardware requirement .", "depending use case , might want simply consume model api ( like gpt-4 ) run locally .", "case , additional prompting guidance technique improve constrain output application .", "* * * llm apis * * : apis convenient way deploy llm .", "space divided private llm ( [ openai ] ( http : //platform.openai.com/ ) , [ google ] ( http : //cloud.google.com/vertex-ai/docs/generative-ai/learn/overview ) , [ anthropic ] ( http : //docs.anthropic.com/claude/reference/getting-started-with-the-api ) , [ cohere ] ( http : //docs.cohere.com/docs ) , etc . )", "open-source llm ( [ openrouter ] ( http : //openrouter.ai/ ) , [ hugging face ] ( http : //huggingface.co/inference-api ) , [ together ai ] ( http : //www.together.ai/ ) , etc . ) .", "* * * open-source llm * * : [ hugging face hub ] ( http : //huggingface.co/models ) great place find llm .", "directly run [ hugging face space ] ( http : //huggingface.co/spaces ) , download run locally apps like [ lm studio ] ( http : //lmstudio.ai/ ) cli [ llama.cpp ] ( http : //github.com/ggerganov/llama.cpp ) [ ollama ] ( http : //ollama.ai/ ) .", "* * * prompt engineering * * : common technique include zero-shot prompting , few-shot prompting , chain thought , react .", "work better bigger model , adapted smaller one .", "* * * structuring output * * : many task require structured output , like strict template json format .", "library like [ lmql ] ( http : //lmql.ai/ ) , [ outline ] ( http : //github.com/outlines-dev/outlines ) , [ guidance ] ( http : //github.com/guidance-ai/guidance ) , etc .", "used guide generation respect given structure .", "📚 * * reference * * : * [ run llm locally lm studio ] ( http : //www.kdnuggets.com/run-an-llm-locally-with-lm-studio ) nisha arya : short guide use lm studio .", "* [ prompt engineering guide ] ( http : //www.promptingguide.ai/ ) dair.ai : exhaustive list prompt technique example * [ outline - quickstart ] ( http : //outlines-dev.github.io/outlines/quickstart/ ) : list guided generation technique enabled outline .", "* [ lmql - overview ] ( http : //lmql.ai/docs/language/overview.html ) : introduction lmql language .", "-- -" ] ]

[ [ "1", ".", "running", "llm", "running", "llm", "difficult", "due", "high", "hardware", "requirement", ".", "depending", "use", "case", ",", "might", "want", "simply", "consume", "model", "api", "(", "like", "gpt-4", ")", "run", "locally", ".", "case", ",", "additional", "prompting", "guidance", "technique", "improve", "constrain", "output", "application", ".", "*", "*", "*", "llm", "apis", "*", "*", ":", "apis", "convenient", "way", "deploy", "llm", ".", "space", "divided", "private", "llm", "(", "[", "openai", "]", "(", "http", ":", "//platform.openai.com/", ")", ",", "[", "google", "]", "(", "http", ":", "//cloud.google.com/vertex-ai/docs/generative-ai/learn/overview", ")", ",", "[", "anthropic", "]", "(", "http", ":", "//docs.anthropic.com/claude/reference/getting-started-with-the-api", ")", ",", "[", "cohere", "]", "(", "http", ":", "//docs.cohere.com/docs", ")", ",", "etc", ".", ")", "open-source", "llm", "(", "[", "openrouter", "]", "(", "http", ":", "//openrouter.ai/", ")", ",", "[", "hugging", "face", "]", "(", "http", ":", "//huggingface.co/inference-api", ")", ",", "[", "together", "ai", "]", "(", "http", ":", "//www.together.ai/", ")", ",", "etc", ".", ")", ".", "*", "*", "*", "open-source", "llm", "*", "*", ":", "[", "hugging", "face", "hub", "]", "(", "http", ":", "//huggingface.co/models", ")", "great", "place", "find", "llm", ".", "directly", "run", "[", "hugging", "face", "space", "]", "(", "http", ":", "//huggingface.co/spaces", ")", ",", "download", "run", "locally", "apps", "like", "[", "lm", "studio", "]", "(", "http", ":", "//lmstudio.ai/", ")", "cli", "[", "llama.cpp", "]", "(", "http", ":", "//github.com/ggerganov/llama.cpp", ")", "[", "ollama", "]", "(", "http", ":", "//ollama.ai/", ")", ".", "*", "*", "*", "prompt", "engineering", "*", "*", ":", "common", "technique", "include", "zero-shot", "prompting", ",", "few-shot", "prompting", ",", "chain", "thought", ",", "react", ".", "work", "better", "bigger", "model", ",", "adapted", "smaller", "one", ".", "*", "*", "*", "structuring", "output", "*", "*", ":", "many", "task", "require", "structured", "output", ",", "like", "strict", "template", "json", "format", ".", "library", "like", "[", "lmql", "]", "(", "http", ":", "//lmql.ai/", ")", ",", "[", "outline", "]", "(", "http", ":", "//github.com/outlines-dev/outlines", ")", ",", "[", "guidance", "]", "(", "http", ":", "//github.com/guidance-ai/guidance", ")", ",", "etc", ".", "used", "guide", "generation", "respect", "given", "structure", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "run", "llm", "locally", "lm", "studio", "]", "(", "http", ":", "//www.kdnuggets.com/run-an-llm-locally-with-lm-studio", ")", "nisha", "arya", ":", "short", "guide", "use", "lm", "studio", ".", "*", "[", "prompt", "engineering", "guide", "]", "(", "http", ":", "//www.promptingguide.ai/", ")", "dair.ai", ":", "exhaustive", "list", "prompt", "technique", "example", "*", "[", "outline", "-", "quickstart", "]", "(", "http", ":", "//outlines-dev.github.io/outlines/quickstart/", ")", ":", "list", "guided", "generation", "technique", "enabled", "outline", ".", "*", "[", "lmql", "-", "overview", "]", "(", "http", ":", "//lmql.ai/docs/language/overview.html", ")", ":", "introduction", "lmql", "language", ".", "--", "-" ], [ "1 .", "running llm running llm difficult due high hardware requirement .", "depending use case , might want simply consume model api ( like gpt-4 ) run locally .", "case , additional prompting guidance technique improve constrain output application .", "* * * llm apis * * : apis convenient way deploy llm .", "space divided private llm ( [ openai ] ( http : //platform.openai.com/ ) , [ google ] ( http : //cloud.google.com/vertex-ai/docs/generative-ai/learn/overview ) , [ anthropic ] ( http : //docs.anthropic.com/claude/reference/getting-started-with-the-api ) , [ cohere ] ( http : //docs.cohere.com/docs ) , etc . )", "open-source llm ( [ openrouter ] ( http : //openrouter.ai/ ) , [ hugging face ] ( http : //huggingface.co/inference-api ) , [ together ai ] ( http : //www.together.ai/ ) , etc . ) .", "* * * open-source llm * * : [ hugging face hub ] ( http : //huggingface.co/models ) great place find llm .", "directly run [ hugging face space ] ( http : //huggingface.co/spaces ) , download run locally apps like [ lm studio ] ( http : //lmstudio.ai/ ) cli [ llama.cpp ] ( http : //github.com/ggerganov/llama.cpp ) [ ollama ] ( http : //ollama.ai/ ) .", "* * * prompt engineering * * : common technique include zero-shot prompting , few-shot prompting , chain thought , react .", "work better bigger model , adapted smaller one .", "* * * structuring output * * : many task require structured output , like strict template json format .", "library like [ lmql ] ( http : //lmql.ai/ ) , [ outline ] ( http : //github.com/outlines-dev/outlines ) , [ guidance ] ( http : //github.com/guidance-ai/guidance ) , etc .", "used guide generation respect given structure .", "📚 * * reference * * : * [ run llm locally lm studio ] ( http : //www.kdnuggets.com/run-an-llm-locally-with-lm-studio ) nisha arya : short guide use lm studio .", "* [ prompt engineering guide ] ( http : //www.promptingguide.ai/ ) dair.ai : exhaustive list prompt technique example * [ outline - quickstart ] ( http : //outlines-dev.github.io/outlines/quickstart/ ) : list guided generation technique enabled outline .", "* [ lmql - overview ] ( http : //lmql.ai/docs/language/overview.html ) : introduction lmql language .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "4", ".", "advanced", "rag", "real-life", "application", "require", "complex", "pipeline", ",", "including", "sql", "graph", "database", ",", "well", "automatically", "selecting", "relevant", "tool", "apis", ".", "advanced", "technique", "improve", "baseline", "solution", "provide", "additional", "feature", ".", "*", "*", "*", "query", "construction", "*", "*", ":", "structured", "data", "stored", "traditional", "database", "requires", "specific", "query", "language", "like", "sql", ",", "cypher", ",", "metadata", ",", "etc", ".", "directly", "translate", "user", "instruction", "query", "access", "data", "query", "construction", ".", "*", "*", "*", "agent", "tool", "*", "*", ":", "agent", "augment", "llm", "automatically", "selecting", "relevant", "tool", "provide", "answer", ".", "tool", "simple", "using", "google", "wikipedia", ",", "complex", "like", "python", "interpreter", "jira", ".", "*", "*", "*", "post-processing", "*", "*", ":", "final", "step", "process", "input", "fed", "llm", ".", "enhances", "relevance", "diversity", "document", "retrieved", "re-ranking", ",", "[", "rag-fusion", "]", "(", "http", ":", "//github.com/raudaschl/rag-fusion", ")", ",", "classification", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "langchain", "-", "query", "construction", "]", "(", "http", ":", "//blog.langchain.dev/query-construction/", ")", ":", "blog", "post", "different", "type", "query", "construction", ".", "*", "[", "langchain", "-", "sql", "]", "(", "http", ":", "//python.langchain.com/docs/use_cases/qa_structured/sql", ")", ":", "tutorial", "interact", "sql", "database", "llm", ",", "involving", "text-to-sql", "optional", "sql", "agent", ".", "*", "[", "pinecone", "-", "llm", "agent", "]", "(", "http", ":", "//www.pinecone.io/learn/series/langchain/langchain-agents/", ")", ":", "introduction", "agent", "tool", "different", "type", ".", "*", "[", "llm", "powered", "autonomous", "agent", "]", "(", "http", ":", "//lilianweng.github.io/posts/2023-06-23-agent/", ")", "lilian", "weng", ":", "theoretical", "article", "llm", "agent", ".", "*", "[", "langchain", "-", "openai", "'s", "rag", "]", "(", "http", ":", "//blog.langchain.dev/applying-openai-rag/", ")", ":", "overview", "rag", "strategy", "employed", "openai", ",", "including", "post-processing", ".", "--", "-" ], [ "4 .", "advanced rag real-life application require complex pipeline , including sql graph database , well automatically selecting relevant tool apis .", "advanced technique improve baseline solution provide additional feature .", "* * * query construction * * : structured data stored traditional database requires specific query language like sql , cypher , metadata , etc .", "directly translate user instruction query access data query construction .", "* * * agent tool * * : agent augment llm automatically selecting relevant tool provide answer .", "tool simple using google wikipedia , complex like python interpreter jira .", "* * * post-processing * * : final step process input fed llm .", "enhances relevance diversity document retrieved re-ranking , [ rag-fusion ] ( http : //github.com/raudaschl/rag-fusion ) , classification .", "📚 * * reference * * : * [ langchain - query construction ] ( http : //blog.langchain.dev/query-construction/ ) : blog post different type query construction .", "* [ langchain - sql ] ( http : //python.langchain.com/docs/use_cases/qa_structured/sql ) : tutorial interact sql database llm , involving text-to-sql optional sql agent .", "* [ pinecone - llm agent ] ( http : //www.pinecone.io/learn/series/langchain/langchain-agents/ ) : introduction agent tool different type .", "* [ llm powered autonomous agent ] ( http : //lilianweng.github.io/posts/2023-06-23-agent/ ) lilian weng : theoretical article llm agent .", "* [ langchain - openai 's rag ] ( http : //blog.langchain.dev/applying-openai-rag/ ) : overview rag strategy employed openai , including post-processing .", "-- -" ] ]

[ [ "4", ".", "advanced", "rag", "real-life", "application", "require", "complex", "pipeline", ",", "including", "sql", "graph", "database", ",", "well", "automatically", "selecting", "relevant", "tool", "apis", ".", "advanced", "technique", "improve", "baseline", "solution", "provide", "additional", "feature", ".", "*", "*", "*", "query", "construction", "*", "*", ":", "structured", "data", "stored", "traditional", "database", "requires", "specific", "query", "language", "like", "sql", ",", "cypher", ",", "metadata", ",", "etc", ".", "directly", "translate", "user", "instruction", "query", "access", "data", "query", "construction", ".", "*", "*", "*", "agent", "tool", "*", "*", ":", "agent", "augment", "llm", "automatically", "selecting", "relevant", "tool", "provide", "answer", ".", "tool", "simple", "using", "google", "wikipedia", ",", "complex", "like", "python", "interpreter", "jira", ".", "*", "*", "*", "post-processing", "*", "*", ":", "final", "step", "process", "input", "fed", "llm", ".", "enhances", "relevance", "diversity", "document", "retrieved", "re-ranking", ",", "[", "rag-fusion", "]", "(", "http", ":", "//github.com/raudaschl/rag-fusion", ")", ",", "classification", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "langchain", "-", "query", "construction", "]", "(", "http", ":", "//blog.langchain.dev/query-construction/", ")", ":", "blog", "post", "different", "type", "query", "construction", ".", "*", "[", "langchain", "-", "sql", "]", "(", "http", ":", "//python.langchain.com/docs/use_cases/qa_structured/sql", ")", ":", "tutorial", "interact", "sql", "database", "llm", ",", "involving", "text-to-sql", "optional", "sql", "agent", ".", "*", "[", "pinecone", "-", "llm", "agent", "]", "(", "http", ":", "//www.pinecone.io/learn/series/langchain/langchain-agents/", ")", ":", "introduction", "agent", "tool", "different", "type", ".", "*", "[", "llm", "powered", "autonomous", "agent", "]", "(", "http", ":", "//lilianweng.github.io/posts/2023-06-23-agent/", ")", "lilian", "weng", ":", "theoretical", "article", "llm", "agent", ".", "*", "[", "langchain", "-", "openai", "'s", "rag", "]", "(", "http", ":", "//blog.langchain.dev/applying-openai-rag/", ")", ":", "overview", "rag", "strategy", "employed", "openai", ",", "including", "post-processing", ".", "--", "-" ], [ "4 .", "advanced rag real-life application require complex pipeline , including sql graph database , well automatically selecting relevant tool apis .", "advanced technique improve baseline solution provide additional feature .", "* * * query construction * * : structured data stored traditional database requires specific query language like sql , cypher , metadata , etc .", "directly translate user instruction query access data query construction .", "* * * agent tool * * : agent augment llm automatically selecting relevant tool provide answer .", "tool simple using google wikipedia , complex like python interpreter jira .", "* * * post-processing * * : final step process input fed llm .", "enhances relevance diversity document retrieved re-ranking , [ rag-fusion ] ( http : //github.com/raudaschl/rag-fusion ) , classification .", "📚 * * reference * * : * [ langchain - query construction ] ( http : //blog.langchain.dev/query-construction/ ) : blog post different type query construction .", "* [ langchain - sql ] ( http : //python.langchain.com/docs/use_cases/qa_structured/sql ) : tutorial interact sql database llm , involving text-to-sql optional sql agent .", "* [ pinecone - llm agent ] ( http : //www.pinecone.io/learn/series/langchain/langchain-agents/ ) : introduction agent tool different type .", "* [ llm powered autonomous agent ] ( http : //lilianweng.github.io/posts/2023-06-23-agent/ ) lilian weng : theoretical article llm agent .", "* [ langchain - openai 's rag ] ( http : //blog.langchain.dev/applying-openai-rag/ ) : overview rag strategy employed openai , including post-processing .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "5", ".", "inference", "optimization", "text", "generation", "costly", "process", "requires", "expensive", "hardware", ".", "addition", "quantization", ",", "various", "technique", "proposed", "maximize", "throughput", "reduce", "inference", "cost", ".", "*", "*", "*", "flash", "attention", "*", "*", ":", "optimization", "attention", "mechanism", "transform", "complexity", "quadratic", "linear", ",", "speeding", "training", "inference", ".", "*", "*", "*", "key-value", "cache", "*", "*", ":", "understand", "key-value", "cache", "improvement", "introduced", "[", "multi-query", "attention", "]", "(", "http", ":", "//arxiv.org/abs/1911.02150", ")", "(", "mqa", ")", "[", "grouped-query", "attention", "]", "(", "http", ":", "//arxiv.org/abs/2305.13245", ")", "(", "gqa", ")", ".", "*", "*", "*", "speculative", "decoding", "*", "*", ":", "use", "small", "model", "produce", "draft", "reviewed", "larger", "model", "speed", "text", "generation", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "gpu", "inference", "]", "(", "http", ":", "//huggingface.co/docs/transformers/main/en/perf_infer_gpu_one", ")", "hugging", "face", ":", "explain", "optimize", "inference", "gpus", ".", "*", "[", "llm", "inference", "]", "(", "http", ":", "//www.databricks.com/blog/llm-inference-performance-engineering-best-practices", ")", "databricks", ":", "best", "practice", "optimize", "llm", "inference", "production", ".", "*", "[", "optimizing", "llm", "speed", "memory", "]", "(", "http", ":", "//huggingface.co/docs/transformers/main/en/llm_tutorial_optimization", ")", "hugging", "face", ":", "explain", "three", "main", "technique", "optimize", "speed", "memory", ",", "namely", "quantization", ",", "flash", "attention", ",", "architectural", "innovation", ".", "*", "[", "assisted", "generation", "]", "(", "http", ":", "//huggingface.co/blog/assisted-generation", ")", "hugging", "face", ":", "hf", "'s", "version", "speculative", "decoding", ",", "'s", "interesting", "blog", "post", "work", "code", "implement", ".", "--", "-" ], [ "5 .", "inference optimization text generation costly process requires expensive hardware .", "addition quantization , various technique proposed maximize throughput reduce inference cost .", "* * * flash attention * * : optimization attention mechanism transform complexity quadratic linear , speeding training inference .", "* * * key-value cache * * : understand key-value cache improvement introduced [ multi-query attention ] ( http : //arxiv.org/abs/1911.02150 ) ( mqa ) [ grouped-query attention ] ( http : //arxiv.org/abs/2305.13245 ) ( gqa ) .", "* * * speculative decoding * * : use small model produce draft reviewed larger model speed text generation .", "📚 * * reference * * : * [ gpu inference ] ( http : //huggingface.co/docs/transformers/main/en/perf_infer_gpu_one ) hugging face : explain optimize inference gpus .", "* [ llm inference ] ( http : //www.databricks.com/blog/llm-inference-performance-engineering-best-practices ) databricks : best practice optimize llm inference production .", "* [ optimizing llm speed memory ] ( http : //huggingface.co/docs/transformers/main/en/llm_tutorial_optimization ) hugging face : explain three main technique optimize speed memory , namely quantization , flash attention , architectural innovation .", "* [ assisted generation ] ( http : //huggingface.co/blog/assisted-generation ) hugging face : hf 's version speculative decoding , 's interesting blog post work code implement .", "-- -" ] ]

[ [ "5", ".", "inference", "optimization", "text", "generation", "costly", "process", "requires", "expensive", "hardware", ".", "addition", "quantization", ",", "various", "technique", "proposed", "maximize", "throughput", "reduce", "inference", "cost", ".", "*", "*", "*", "flash", "attention", "*", "*", ":", "optimization", "attention", "mechanism", "transform", "complexity", "quadratic", "linear", ",", "speeding", "training", "inference", ".", "*", "*", "*", "key-value", "cache", "*", "*", ":", "understand", "key-value", "cache", "improvement", "introduced", "[", "multi-query", "attention", "]", "(", "http", ":", "//arxiv.org/abs/1911.02150", ")", "(", "mqa", ")", "[", "grouped-query", "attention", "]", "(", "http", ":", "//arxiv.org/abs/2305.13245", ")", "(", "gqa", ")", ".", "*", "*", "*", "speculative", "decoding", "*", "*", ":", "use", "small", "model", "produce", "draft", "reviewed", "larger", "model", "speed", "text", "generation", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "gpu", "inference", "]", "(", "http", ":", "//huggingface.co/docs/transformers/main/en/perf_infer_gpu_one", ")", "hugging", "face", ":", "explain", "optimize", "inference", "gpus", ".", "*", "[", "llm", "inference", "]", "(", "http", ":", "//www.databricks.com/blog/llm-inference-performance-engineering-best-practices", ")", "databricks", ":", "best", "practice", "optimize", "llm", "inference", "production", ".", "*", "[", "optimizing", "llm", "speed", "memory", "]", "(", "http", ":", "//huggingface.co/docs/transformers/main/en/llm_tutorial_optimization", ")", "hugging", "face", ":", "explain", "three", "main", "technique", "optimize", "speed", "memory", ",", "namely", "quantization", ",", "flash", "attention", ",", "architectural", "innovation", ".", "*", "[", "assisted", "generation", "]", "(", "http", ":", "//huggingface.co/blog/assisted-generation", ")", "hugging", "face", ":", "hf", "'s", "version", "speculative", "decoding", ",", "'s", "interesting", "blog", "post", "work", "code", "implement", ".", "--", "-" ], [ "5 .", "inference optimization text generation costly process requires expensive hardware .", "addition quantization , various technique proposed maximize throughput reduce inference cost .", "* * * flash attention * * : optimization attention mechanism transform complexity quadratic linear , speeding training inference .", "* * * key-value cache * * : understand key-value cache improvement introduced [ multi-query attention ] ( http : //arxiv.org/abs/1911.02150 ) ( mqa ) [ grouped-query attention ] ( http : //arxiv.org/abs/2305.13245 ) ( gqa ) .", "* * * speculative decoding * * : use small model produce draft reviewed larger model speed text generation .", "📚 * * reference * * : * [ gpu inference ] ( http : //huggingface.co/docs/transformers/main/en/perf_infer_gpu_one ) hugging face : explain optimize inference gpus .", "* [ llm inference ] ( http : //www.databricks.com/blog/llm-inference-performance-engineering-best-practices ) databricks : best practice optimize llm inference production .", "* [ optimizing llm speed memory ] ( http : //huggingface.co/docs/transformers/main/en/llm_tutorial_optimization ) hugging face : explain three main technique optimize speed memory , namely quantization , flash attention , architectural innovation .", "* [ assisted generation ] ( http : //huggingface.co/blog/assisted-generation ) hugging face : hf 's version speculative decoding , 's interesting blog post work code implement .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "7", ".", "securing", "llm", "addition", "traditional", "security", "problem", "associated", "software", ",", "llm", "unique", "weakness", "due", "way", "trained", "prompted", ".", "*", "*", "*", "prompt", "hacking", "*", "*", ":", "different", "technique", "related", "prompt", "engineering", ",", "including", "prompt", "injection", "(", "additional", "instruction", "hijack", "model", "'s", "answer", ")", ",", "data/prompt", "leaking", "(", "retrieve", "original", "data/prompt", ")", ",", "jailbreaking", "(", "craft", "prompt", "bypass", "safety", "feature", ")", ".", "*", "*", "*", "backdoor", "*", "*", ":", "attack", "vector", "target", "training", "data", ",", "poisoning", "training", "data", "(", "e.g.", ",", "false", "information", ")", "creating", "backdoor", "(", "secret", "trigger", "change", "model", "'s", "behavior", "inference", ")", ".", "*", "*", "*", "defensive", "measure", "*", "*", ":", "best", "way", "protect", "llm", "application", "test", "vulnerability", "(", "e.g.", ",", "using", "red", "teaming", "check", "like", "[", "garak", "]", "(", "http", ":", "//github.com/leondz/garak/", ")", ")", "observe", "production", "(", "framework", "like", "[", "langfuse", "]", "(", "http", ":", "//github.com/langfuse/langfuse", ")", ")", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "owasp", "llm", "top", "10", "]", "(", "http", ":", "//owasp.org/www-project-top-10-for-large-language-model-applications/", ")", "hego", "wiki", ":", "list", "10", "critic", "vulnerability", "seen", "llm", "application", ".", "*", "[", "prompt", "injection", "primer", "]", "(", "http", ":", "//github.com/jthack/pipe", ")", "joseph", "thacker", ":", "short", "guide", "dedicated", "prompt", "injection", "engineer", ".", "*", "[", "llm", "security", "]", "(", "http", ":", "//llmsecurity.net/", ")", "[", "@", "llm_sec", "]", "(", "http", ":", "//twitter.com/llm_sec", ")", ":", "extensive", "list", "resource", "related", "llm", "security", ".", "*", "[", "red", "teaming", "llm", "]", "(", "http", ":", "//learn.microsoft.com/en-us/azure/ai-services/openai/concepts/red-teaming", ")", "microsoft", ":", "guide", "perform", "red", "teaming", "llm", ".", "--", "-" ], [ "7 .", "securing llm addition traditional security problem associated software , llm unique weakness due way trained prompted .", "* * * prompt hacking * * : different technique related prompt engineering , including prompt injection ( additional instruction hijack model 's answer ) , data/prompt leaking ( retrieve original data/prompt ) , jailbreaking ( craft prompt bypass safety feature ) .", "* * * backdoor * * : attack vector target training data , poisoning training data ( e.g. , false information ) creating backdoor ( secret trigger change model 's behavior inference ) .", "* * * defensive measure * * : best way protect llm application test vulnerability ( e.g. , using red teaming check like [ garak ] ( http : //github.com/leondz/garak/ ) ) observe production ( framework like [ langfuse ] ( http : //github.com/langfuse/langfuse ) ) .", "📚 * * reference * * : * [ owasp llm top 10 ] ( http : //owasp.org/www-project-top-10-for-large-language-model-applications/ ) hego wiki : list 10 critic vulnerability seen llm application .", "* [ prompt injection primer ] ( http : //github.com/jthack/pipe ) joseph thacker : short guide dedicated prompt injection engineer .", "* [ llm security ] ( http : //llmsecurity.net/ ) [ @ llm_sec ] ( http : //twitter.com/llm_sec ) : extensive list resource related llm security .", "* [ red teaming llm ] ( http : //learn.microsoft.com/en-us/azure/ai-services/openai/concepts/red-teaming ) microsoft : guide perform red teaming llm .", "-- -" ] ]

[ [ "7", ".", "securing", "llm", "addition", "traditional", "security", "problem", "associated", "software", ",", "llm", "unique", "weakness", "due", "way", "trained", "prompted", ".", "*", "*", "*", "prompt", "hacking", "*", "*", ":", "different", "technique", "related", "prompt", "engineering", ",", "including", "prompt", "injection", "(", "additional", "instruction", "hijack", "model", "'s", "answer", ")", ",", "data/prompt", "leaking", "(", "retrieve", "original", "data/prompt", ")", ",", "jailbreaking", "(", "craft", "prompt", "bypass", "safety", "feature", ")", ".", "*", "*", "*", "backdoor", "*", "*", ":", "attack", "vector", "target", "training", "data", ",", "poisoning", "training", "data", "(", "e.g.", ",", "false", "information", ")", "creating", "backdoor", "(", "secret", "trigger", "change", "model", "'s", "behavior", "inference", ")", ".", "*", "*", "*", "defensive", "measure", "*", "*", ":", "best", "way", "protect", "llm", "application", "test", "vulnerability", "(", "e.g.", ",", "using", "red", "teaming", "check", "like", "[", "garak", "]", "(", "http", ":", "//github.com/leondz/garak/", ")", ")", "observe", "production", "(", "framework", "like", "[", "langfuse", "]", "(", "http", ":", "//github.com/langfuse/langfuse", ")", ")", ".", "📚", "*", "*", "reference", "*", "*", ":", "*", "[", "owasp", "llm", "top", "10", "]", "(", "http", ":", "//owasp.org/www-project-top-10-for-large-language-model-applications/", ")", "hego", "wiki", ":", "list", "10", "critic", "vulnerability", "seen", "llm", "application", ".", "*", "[", "prompt", "injection", "primer", "]", "(", "http", ":", "//github.com/jthack/pipe", ")", "joseph", "thacker", ":", "short", "guide", "dedicated", "prompt", "injection", "engineer", ".", "*", "[", "llm", "security", "]", "(", "http", ":", "//llmsecurity.net/", ")", "[", "@", "llm_sec", "]", "(", "http", ":", "//twitter.com/llm_sec", ")", ":", "extensive", "list", "resource", "related", "llm", "security", ".", "*", "[", "red", "teaming", "llm", "]", "(", "http", ":", "//learn.microsoft.com/en-us/azure/ai-services/openai/concepts/red-teaming", ")", "microsoft", ":", "guide", "perform", "red", "teaming", "llm", ".", "--", "-" ], [ "7 .", "securing llm addition traditional security problem associated software , llm unique weakness due way trained prompted .", "* * * prompt hacking * * : different technique related prompt engineering , including prompt injection ( additional instruction hijack model 's answer ) , data/prompt leaking ( retrieve original data/prompt ) , jailbreaking ( craft prompt bypass safety feature ) .", "* * * backdoor * * : attack vector target training data , poisoning training data ( e.g. , false information ) creating backdoor ( secret trigger change model 's behavior inference ) .", "* * * defensive measure * * : best way protect llm application test vulnerability ( e.g. , using red teaming check like [ garak ] ( http : //github.com/leondz/garak/ ) ) observe production ( framework like [ langfuse ] ( http : //github.com/langfuse/langfuse ) ) .", "📚 * * reference * * : * [ owasp llm top 10 ] ( http : //owasp.org/www-project-top-10-for-large-language-model-applications/ ) hego wiki : list 10 critic vulnerability seen llm application .", "* [ prompt injection primer ] ( http : //github.com/jthack/pipe ) joseph thacker : short guide dedicated prompt injection engineer .", "* [ llm security ] ( http : //llmsecurity.net/ ) [ @ llm_sec ] ( http : //twitter.com/llm_sec ) : extensive list resource related llm security .", "* [ red teaming llm ] ( http : //learn.microsoft.com/en-us/azure/ai-services/openai/concepts/red-teaming ) microsoft : guide perform red teaming llm .", "-- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

[ [ "⚡quick", "start", "download", "install", "[", "nodejs", "]", "(", "http", ":", "//nodejs.org/en/download", ")", ">", "=", "18.15.0", "1", ".", "install", "flowise", "``", "`", "bash", "npm", "install", "-g", "flowise", "``", "`", "2", ".", "start", "flowise", "``", "`", "bash", "npx", "flowise", "start", "``", "`", "username", "&", "password", "``", "`", "bash", "npx", "flowise", "start", "--", "flowise_username=user", "--", "flowise_password=1234", "``", "`", "3", ".", "open", "[", "http", ":", "//localhost:3000", "]", "(", "http", ":", "//localhost:3000", ")" ], [ "⚡quick start download install [ nodejs ] ( http : //nodejs.org/en/download ) > = 18.15.0 1 .", "install flowise `` ` bash npm install -g flowise `` ` 2 .", "start flowise `` ` bash npx flowise start `` ` username & password `` ` bash npx flowise start -- flowise_username=user -- flowise_password=1234 `` ` 3 .", "open [ http : //localhost:3000 ] ( http : //localhost:3000 )" ] ]

[ [ "⚡quick", "start", "download", "install", "[", "nodejs", "]", "(", "http", ":", "//nodejs.org/en/download", ")", ">", "=", "18.15.0", "1", ".", "install", "flowise", "``", "`", "bash", "npm", "install", "-g", "flowise", "``", "`", "2", ".", "start", "flowise", "``", "`", "bash", "npx", "flowise", "start", "``", "`", "username", "&", "password", "``", "`", "bash", "npx", "flowise", "start", "--", "flowise_username=user", "--", "flowise_password=1234", "``", "`", "3", ".", "open", "[", "http", ":", "//localhost:3000", "]", "(", "http", ":", "//localhost:3000", ")" ], [ "⚡quick start download install [ nodejs ] ( http : //nodejs.org/en/download ) > = 18.15.0 1 .", "install flowise `` ` bash npm install -g flowise `` ` 2 .", "start flowise `` ` bash npx flowise start `` ` username & password `` ` bash npx flowise start -- flowise_username=user -- flowise_password=1234 `` ` 3 .", "open [ http : //localhost:3000 ] ( http : //localhost:3000 )" ] ]

[ "artificial-intelligence", "chatgpt", "javascript", "large-language-models", "llm", "low-code", "no-code", "react", "typescript" ]

[ [ "prerequisite", "-", "install", "[", "yarn", "v1", "]", "(", "http", ":", "//classic.yarnpkg.com/en/docs/install", ")", "``", "`", "bash", "npm", "-g", "yarn", "``", "`" ], [ "prerequisite - install [ yarn v1 ] ( http : //classic.yarnpkg.com/en/docs/install ) `` ` bash npm -g yarn `` `" ] ]

[ [ "prerequisite", "-", "install", "[", "yarn", "v1", "]", "(", "http", ":", "//classic.yarnpkg.com/en/docs/install", ")", "``", "`", "bash", "npm", "-g", "yarn", "``", "`" ], [ "prerequisite - install [ yarn v1 ] ( http : //classic.yarnpkg.com/en/docs/install ) `` ` bash npm -g yarn `` `" ] ]

[ "artificial-intelligence", "chatgpt", "javascript", "large-language-models", "llm", "low-code", "no-code", "react", "typescript" ]

[ [ "setup", "1", ".", "clone", "repository", "``", "`", "bash", "git", "clone", "http", ":", "//github.com/flowiseai/flowise.git", "``", "`", "2", ".", "go", "repository", "folder", "``", "`", "bash", "cd", "flowise", "``", "`", "3", ".", "install", "dependency", "module", ":", "``", "`", "bash", "yarn", "install", "``", "`", "4", ".", "build", "code", ":", "``", "`", "bash", "yarn", "build", "``", "`", "5", ".", "start", "app", ":", "``", "`", "bash", "yarn", "start", "``", "`", "access", "app", "[", "http", ":", "//localhost:3000", "]", "(", "http", ":", "//localhost:3000", ")", "6", ".", "development", "build", ":", "-", "create", "`", ".env", "`", "file", "specify", "`", "port", "`", "(", "refer", "`", ".env.example", "`", ")", "`", "packages/ui", "`", "-", "create", "`", ".env", "`", "file", "specify", "`", "port", "`", "(", "refer", "`", ".env.example", "`", ")", "`", "packages/server", "`", "-", "run", "``", "`", "bash", "yarn", "dev", "``", "`", "code", "change", "reload", "app", "automatically", "[", "http", ":", "//localhost:8080", "]", "(", "http", ":", "//localhost:8080", ")" ], [ "setup 1 .", "clone repository `` ` bash git clone http : //github.com/flowiseai/flowise.git `` ` 2 .", "go repository folder `` ` bash cd flowise `` ` 3 .", "install dependency module : `` ` bash yarn install `` ` 4 .", "build code : `` ` bash yarn build `` ` 5 .", "start app : `` ` bash yarn start `` ` access app [ http : //localhost:3000 ] ( http : //localhost:3000 ) 6 .", "development build : - create ` .env ` file specify ` port ` ( refer ` .env.example ` ) ` packages/ui ` - create ` .env ` file specify ` port ` ( refer ` .env.example ` ) ` packages/server ` - run `` ` bash yarn dev `` ` code change reload app automatically [ http : //localhost:8080 ] ( http : //localhost:8080 )" ] ]

[ [ "setup", "1", ".", "clone", "repository", "``", "`", "bash", "git", "clone", "http", ":", "//github.com/flowiseai/flowise.git", "``", "`", "2", ".", "go", "repository", "folder", "``", "`", "bash", "cd", "flowise", "``", "`", "3", ".", "install", "dependency", "module", ":", "``", "`", "bash", "yarn", "install", "``", "`", "4", ".", "build", "code", ":", "``", "`", "bash", "yarn", "build", "``", "`", "5", ".", "start", "app", ":", "``", "`", "bash", "yarn", "start", "``", "`", "access", "app", "[", "http", ":", "//localhost:3000", "]", "(", "http", ":", "//localhost:3000", ")", "6", ".", "development", "build", ":", "-", "create", "`", ".env", "`", "file", "specify", "`", "port", "`", "(", "refer", "`", ".env.example", "`", ")", "`", "packages/ui", "`", "-", "create", "`", ".env", "`", "file", "specify", "`", "port", "`", "(", "refer", "`", ".env.example", "`", ")", "`", "packages/server", "`", "-", "run", "``", "`", "bash", "yarn", "dev", "``", "`", "code", "change", "reload", "app", "automatically", "[", "http", ":", "//localhost:8080", "]", "(", "http", ":", "//localhost:8080", ")" ], [ "setup 1 .", "clone repository `` ` bash git clone http : //github.com/flowiseai/flowise.git `` ` 2 .", "go repository folder `` ` bash cd flowise `` ` 3 .", "install dependency module : `` ` bash yarn install `` ` 4 .", "build code : `` ` bash yarn build `` ` 5 .", "start app : `` ` bash yarn start `` ` access app [ http : //localhost:3000 ] ( http : //localhost:3000 ) 6 .", "development build : - create ` .env ` file specify ` port ` ( refer ` .env.example ` ) ` packages/ui ` - create ` .env ` file specify ` port ` ( refer ` .env.example ` ) ` packages/server ` - run `` ` bash yarn dev `` ` code change reload app automatically [ http : //localhost:8080 ] ( http : //localhost:8080 )" ] ]

[ "artificial-intelligence", "chatgpt", "javascript", "large-language-models", "llm", "low-code", "no-code", "react", "typescript" ]