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 2024-10-27 04:48:29

Having misalignment between your prompts during training and inference can cause models to perform very poorly, so it is worth checking this. See this blog post for a concrete example.

Axolotl supports a variety of dataset formats. It is recommended to use a JSONL. The schema of the JSONL depends upon the task and the prompt template you wish to use. Instead of a JSONL, you can also use a HuggingFace dataset with columns for each JSONL field.

The following command will merge your LORA adapater with your base model. You can optionally pass the argument --lora_model_dir to specify the directory where your LORA adapter was saved, otherwhise, this will be inferred from output_dir in your axolotl config file. The merged model is saved in the sub-directory {lora_model_dir}/merged.

Check out some of the projects and models that have been built using Axolotl! Have a model you'd like to add to our Community Showcase? Open a PR with your model.

Deepspeed is an optimization suite for multi-gpu systems allowing you to train much larger models than you might typically be able to fit into your GPU's VRAM. More information about the various optimization types for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed#what-is-integrated

If you want to debug axolotl or prefer to use Docker as your development environment, see the debugging guide's section on Docker.

Axolotl allows you to load your model in an interactive terminal playground for quick experimentation. The config file is the same config file used for training.

★ ”Gripping. A strong, emotionally intelligent story.” — Kirkus Reviews, starred review★ ”The suspenseful story is immediately intriguing, skillfully conveying the high-stakes situation and effortlessly drawing the audience in. A tremendous testament to the power in plotting your own course.“—Booklist, starred review★ ”Lovingly crafted with depth and compassion. Nuance suffuses this story of discovery, as Fern’s blind faith grows tenuous.” — Bulletin, starred review“Driven by a growing self-awareness that she can choose who and what she believes, this is a moving portrait of a girl undergoing drastic change.” —Publishers Weekly“This coming-of-age story centers a girl trying to decide what is right as she struggles to change from one culture to another. The first-person narrative is believable and thought-provoking as Fern reconsiders everything she’s thought to be true.” —School Library Journal“A cleverly written and supremely literary story, while also remaining pretty gripping in its telling.” —SLJ’s Fuse 8“O’Shaughnessy presents a high stakes situation and zeroes in on a child narrator’s believable emotions. That tight focus on the narrator even when she is misguided or doesn’t have all the facts allows readers to draw their own conclusions.” — The Horn Book Magazine“Kate O’Shaughnessy is an extraordinarily talented author whose gift is an innate understanding of the inner life of twelve-year-olds.” —Gennifer Choldenko, Newbery Honor Winner for Al Capone Does My Shirts“To leave behind a world that seems totally secure and safe and comfortable, for a world in which you make your own decisions and claim your own life—that is one of our great journeys. In The Wrong Way Home, Fern has to battle her own fear, elaborate illusions, misunderstandings, and the past mistakes of others to get on with that journey—and, dear reader, you will not be able to turn the pages fast enough to see if she makes it. Plan on reading this in one sitting; matters of the human soul don’t bear interruption.” —Gary D. Schmidt, Newbery Honor Winner for The Wednesday Wars“Young readers will cheer for Fern as she finds the courage to confront the troubling doctrines she has been raised with and reinvent her ideas of home.” —Jacquetta Nammar Feldman, author of Wishing Upon the Same Stars and The Puttermans Are in the House

Twelve-year-old Fern believes she’s living a noble life–but what if everything she’s been told is a lie? This is a huge-hearted story about a girl learning to question everything—and to trust in herself.Fern’s lived at the Ranch, an off-the-grid, sustainable community in upstate New York, since she was six. The work is hard, but Fern admires the Ranch’s leader, Dr. Ben. So when Fern’s mother sneaks them away in the middle of the night and says Dr. Ben is dangerous, Fern doesn’t believe it. She wants desperately to go back, but her mom just keeps driving.Suddenly thrust into the treacherous, toxic, outside world, Fern thinks only about how to get home again. She has a plan, but it will take time. As that time goes by, though, Fern realizes there are things she will miss from this place—the library, a friend from school, the ocean—and there are things she learned at the Ranch that are just…not true.Now Fern will have to decide. How much is she willing to give up to return to the Ranch? Should she trust Dr. Ben’s vision for her life? Or listen to the growing feeling that she can live by her own rules?

For further and fine-grained use cases, please refer to the official dstack documents and the detailed description of axolotl example on the official repository.

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If you decode a prompt constructed by axolotl, you might see spaces between tokens (or lack thereof) that you do not expect, especially around delimiters and special tokens. When you are starting out with a new format, you should always do the following:

KATE O’SHAUGHNESSY is a book nerd, animal lover, former chef, and an outdoor enthusiast. When she’s not writing, you can find Kate in her garden, eating good food, hiking with her dog, and chronically mispronouncing words she’s read but never… More about Kate O’Shaughnessy

Below are the options available in axolotl for training with multiple GPUs. Note that DeepSpeed is the recommended multi-GPU option currently because FSDP may experience loss instability.

Twelve-year-old Fern believes she’s living a noble life–but what if everything she’s been told is a lie? This is a huge-hearted story about a girl learning to question everything—and to trust in herself.Fern’s lived at the Ranch, an off-the-grid, sustainable community in upstate New York, since she was six. The work is hard, but Fern admires the Ranch’s leader, Dr. Ben. So when Fern’s mother sneaks them away in the middle of the night and says Dr. Ben is dangerous, Fern doesn’t believe it. She wants desperately to go back, but her mom just keeps driving.Suddenly thrust into the treacherous, toxic, outside world, Fern can think only of how to get home. She has a plan, but it will take time. As that time goes by, though, Fern realizes there are things she will miss from this place—the library, a friend from school, the ocean—and there are things she learned at the Ranch that are just…not true.Now Fern will have to decide. How much is she willing to give up to return to the Ranch? Should she trust Dr. Ben’s vision for her life? Or listen to the growing feeling that she can live by her own rules?

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If you encounter a 'Cuda out of memory' error, it means your GPU ran out of memory during the training process. Here's how to resolve it:

OpenAccess AI Collective is run by volunteer contributors such as winglian, NanoCode012, tmm1, mhenrichsen, casper-hansen, hamelsmu and many more who help us accelerate forward by fixing bugs, answering community questions and implementing new features. Axolotl needs donations from sponsors for the compute needed to run our unit & integration tests, troubleshooting community issues, and providing bounties. If you love axolotl, consider sponsoring the project via GitHub Sponsors, Ko-fi or reach out directly to wing@openaccessaicollective.org.

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For many formats, Axolotl constructs prompts by concatenating token ids after tokenizing strings. The reason for concatenating token ids rather than operating on strings is to maintain precise accounting for attention masks.

then, simply run the job with dstack run command. Append --spot option if you want spot instance. dstack run command will show you the instance with cheapest price across multi cloud services:

Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.

To launch on GPU instances (both on-demand and spot instances) on 7+ clouds (GCP, AWS, Azure, OCI, and more), you can use SkyPilot:

Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.

If it does not help, try running without deepspeed and without accelerate (replace "accelerate launch" with "python") in the command.

It is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer's vocabulary. This will help you avoid tokenization issues and help your model train better. You can do this in axolotl like this:

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To launch on GPU instance (both on-demand and spot instances) on public clouds (GCP, AWS, Azure, Lambda Labs, TensorDock, Vast.ai, and CUDO), you can use dstack.

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Liger (LinkedIn GPU Efficient Runtime) Kernel is a collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The Liger Kernel composes well and is compatible with both FSDP and Deepspeed.

You may need to use the gpu_memory_limit and/or lora_on_cpu config options to avoid running out of memory. If you still run out of CUDA memory, you can try to merge in system RAM with

You can also reference a config file that is hosted on a public URL, for example accelerate launch -m axolotl.cli.train https://yourdomain.com/your_config.yml

Usually means your system has run out of system memory. Similarly, you should consider reducing the same settings as when you run out of VRAM. Additionally, look into upgrading your system RAM which should be simpler than GPU upgrades.

RuntimeError: stack expects each tensor to be equal size, but got [1, 32, 1, 128] at entry 0 and [1, 32, 8, 128] at entry 1

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