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     "href": "index.html#quickstart",
     "title": "Axolotl",
     "section": "Quickstart ⚡",
-    "text": "Quickstart ⚡\nGet started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.\nRequirements: Nvidia GPU (Ampere architecture or newer for bf16 and Flash Attention) or AMD GPU, Python >=3.10 and PyTorch >=2.3.1.\npip3 install --no-build-isolation axolotl[flash-attn,deepspeed]\n\n# download examples and optionally deepspeed configs to the local path\naxolotl fetch examples\naxolotl fetch deepspeed_configs  # OPTIONAL\n\n# finetune using lora\naxolotl train examples/llama-3/lora-1b.yml\n\nEdge Builds 🏎️\nIf you’re looking for the latest features and updates between releases, you’ll need to install from source.\ngit clone https://github.com/axolotl-ai-cloud/axolotl.git\ncd axolotl\npip3 install packaging ninja\npip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'\n\n\nAxolotl CLI Usage\nWe now support a new, more streamlined CLI using click.\n# preprocess datasets - optional but recommended\nCUDA_VISIBLE_DEVICES=\"0\" axolotl preprocess examples/llama-3/lora-1b.yml\n\n# finetune lora\naxolotl train examples/llama-3/lora-1b.yml\n\n# inference\naxolotl inference examples/llama-3/lora-1b.yml \\\n    --lora-model-dir=\"./outputs/lora-out\"\n\n# gradio\naxolotl inference examples/llama-3/lora-1b.yml \\\n    --lora-model-dir=\"./outputs/lora-out\" --gradio\n\n# remote yaml files - the yaml config can be hosted on a public URL\n# Note: the yaml config must directly link to the **raw** yaml\naxolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml\nWe’ve also added a new command for fetching examples and deepspeed_configs to your local machine. This will come in handy when installing axolotl from PyPI.\n# Fetch example YAML files (stores in \"examples/\" folder)\naxolotl fetch examples\n\n# Fetch deepspeed config files (stores in \"deepspeed_configs/\" folder)\naxolotl fetch deepspeed_configs\n\n# Optionally, specify a destination folder\naxolotl fetch examples --dest path/to/folder\n\n\nLegacy Usage\n\n\nClick to Expand\n\nWhile the Axolotl CLI is the preferred method for interacting with axolotl, we still support the legacy -m axolotl.cli.* usage.\n# preprocess datasets - optional but recommended\nCUDA_VISIBLE_DEVICES=\"0\" python -m axolotl.cli.preprocess examples/llama-3/lora-1b.yml\n\n# finetune lora\naccelerate launch -m axolotl.cli.train examples/llama-3/lora-1b.yml\n\n# inference\naccelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \\\n    --lora_model_dir=\"./outputs/lora-out\"\n\n# gradio\naccelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \\\n    --lora_model_dir=\"./outputs/lora-out\" --gradio\n\n# remote yaml files - the yaml config can be hosted on a public URL\n# Note: the yaml config must directly link to the **raw** yaml\naccelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml",
+    "text": "Quickstart ⚡\nGet started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.\nRequirements: Nvidia GPU (Ampere architecture or newer for bf16 and Flash Attention) or AMD GPU, Python >=3.10 and PyTorch >=2.4.1.\npip3 install --no-build-isolation axolotl[flash-attn,deepspeed]\n\n# download examples and optionally deepspeed configs to the local path\naxolotl fetch examples\naxolotl fetch deepspeed_configs  # OPTIONAL\n\n# finetune using lora\naxolotl train examples/llama-3/lora-1b.yml\n\nEdge Builds 🏎️\nIf you’re looking for the latest features and updates between releases, you’ll need to install from source.\ngit clone https://github.com/axolotl-ai-cloud/axolotl.git\ncd axolotl\npip3 install packaging ninja\npip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'\n\n\nAxolotl CLI Usage\nWe now support a new, more streamlined CLI using click.\n# preprocess datasets - optional but recommended\nCUDA_VISIBLE_DEVICES=\"0\" axolotl preprocess examples/llama-3/lora-1b.yml\n\n# finetune lora\naxolotl train examples/llama-3/lora-1b.yml\n\n# inference\naxolotl inference examples/llama-3/lora-1b.yml \\\n    --lora-model-dir=\"./outputs/lora-out\"\n\n# gradio\naxolotl inference examples/llama-3/lora-1b.yml \\\n    --lora-model-dir=\"./outputs/lora-out\" --gradio\n\n# remote yaml files - the yaml config can be hosted on a public URL\n# Note: the yaml config must directly link to the **raw** yaml\naxolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml\nWe’ve also added a new command for fetching examples and deepspeed_configs to your local machine. This will come in handy when installing axolotl from PyPI.\n# Fetch example YAML files (stores in \"examples/\" folder)\naxolotl fetch examples\n\n# Fetch deepspeed config files (stores in \"deepspeed_configs/\" folder)\naxolotl fetch deepspeed_configs\n\n# Optionally, specify a destination folder\naxolotl fetch examples --dest path/to/folder\n\n\nLegacy Usage\n\n\nClick to Expand\n\nWhile the Axolotl CLI is the preferred method for interacting with axolotl, we still support the legacy -m axolotl.cli.* usage.\n# preprocess datasets - optional but recommended\nCUDA_VISIBLE_DEVICES=\"0\" python -m axolotl.cli.preprocess examples/llama-3/lora-1b.yml\n\n# finetune lora\naccelerate launch -m axolotl.cli.train examples/llama-3/lora-1b.yml\n\n# inference\naccelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \\\n    --lora_model_dir=\"./outputs/lora-out\"\n\n# gradio\naccelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \\\n    --lora_model_dir=\"./outputs/lora-out\" --gradio\n\n# remote yaml files - the yaml config can be hosted on a public URL\n# Note: the yaml config must directly link to the **raw** yaml\naccelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml",
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