Update README.md

README.md

@@ -1,702 +1,12 @@
-# Axolotl
-
-Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
-
-Features:
-- Train various Huggingface models such as llama, pythia, falcon, mpt
-- Supports fullfinetune, lora, qlora, relora, and gptq
-- Customize configurations using a simple yaml file or CLI overwrite
-- Load different dataset formats, use custom formats, or bring your own tokenized datasets
-- Integrated with xformer, flash attention, rope scaling, and multipacking
-- Works with single GPU or multiple GPUs via FSDP or Deepspeed
-- Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb or mlflow
-- And more!
-
-<a href="https://www.phorm.ai/query?projectId=e315ba4a-4e14-421f-ab05-38a1f9076f25">
-  <img alt="phorm.ai" src="https://img.shields.io/badge/Phorm-Ask_AI-%23F2777A.svg?&logo=data:image/svg+xml;base64,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">
-</a>
-
-<table>
-<tr>
-<td>
-
-## Table of Contents
-- [Introduction](#axolotl)
-- [Supported Features](#axolotl-supports)
-- [Quickstart](#quickstart-)
-- [Environment](#environment)
-  - [Docker](#docker)
-  - [Conda/Pip venv](#condapip-venv)
-  - [Cloud GPU](#cloud-gpu) - Latitude.sh, JarvisLabs, RunPod
-  - [Bare Metal Cloud GPU](#bare-metal-cloud-gpu)
-  - [Windows](#windows)
-  - [Mac](#mac)
-  - [Google Colab](#google-colab)
-  - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
-  - [Launching on public clouds via dstack](#launching-on-public-clouds-via-dstack)
-- [Dataset](#dataset)
-- [Config](#config)
-  - [Train](#train)
-  - [Inference](#inference-playground)
-  - [Merge LORA to Base](#merge-lora-to-base)
-  - [Special Tokens](#special-tokens)
-  - [All Config Options](#all-config-options)
-- Advanced Topics
-  - [Multipack](./docs/multipack.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-  - [RLHF & DPO](./docs/rlhf.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-  - [Dataset Pre-Processing](./docs/dataset_preprocessing.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
-- [Common Errors](#common-errors-)
-  - [Tokenization Mismatch b/w Training & Inference](#tokenization-mismatch-bw-inference--training)
-- [Debugging Axolotl](#debugging-axolotl)
-- [Need Help?](#need-help-)
-- [Badge](#badge-)
-- [Community Showcase](#community-showcase)
-- [Contributing](#contributing-)
-- [Sponsors](#sponsors-)
-
-</td>
-<td>
-
-<div align="center">
-  <img src="image/axolotl.png" alt="axolotl" width="160">
-  <div>
-    <p>
-      <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
-    </p>
-    <p>
-      Go ahead and Axolotl questions!!
-    </p>
-    <img src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/pre-commit.yml/badge.svg?branch=main" alt="pre-commit">
-    <img alt="PyTest Status" src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/tests.yml/badge.svg?branch=main">
-  </div>
-</div>
-
-</td>
-</tr>
-</table>
-
-## Axolotl supports
-
-|             | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
-|-------------|:----------|:-----|-------|------|-------------------|------------|--------------|
-| llama       | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mistral     | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mixtral-MoE | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Mixtral8X22 | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Pythia      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| cerebras    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| btlm        | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| mpt         | ✅         | ❌    | ❓     | ❌             | ❌                 | ❌          | ❓            |
-| falcon      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| gpt-j       | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓            |
-| XGen        | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅            |
-| phi         | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
-| Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Gemma       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
-
-✅: supported
-❌: not supported
-❓: untested
-
-## Quickstart ⚡
-
-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.
-
-**Requirements**: Python >=3.10 and Pytorch >=2.1.1.
-
-```bash
-git clone https://github.com/OpenAccess-AI-Collective/axolotl
-cd axolotl
-
-pip3 install packaging ninja
-pip3 install -e '.[flash-attn,deepspeed]'
-```
-
-### Usage
-```bash
-# preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess examples/openllama-3b/lora.yml
-
-# finetune lora
-accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
-
-# inference
-accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
-    --lora_model_dir="./outputs/lora-out"
-
-# gradio
-accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
-    --lora_model_dir="./outputs/lora-out" --gradio
-
-# remote yaml files - the yaml config can be hosted on a public URL
-# Note: the yaml config must directly link to the **raw** yaml
-accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/examples/openllama-3b/lora.yml
-```
-
-## Advanced Setup
-
-### Environment
-
-#### Docker
-
-  ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest
-  ```
-
-  Or run on the current files for development:
-
-  ```sh
-  docker compose up -d
-  ```
-
->[!Tip]
-> If you want to debug axolotl or prefer to use Docker as your development environment, see the [debugging guide's section on Docker](docs/debugging.qmd#debugging-with-docker).
-
-  <details>
-
-  <summary>Docker advanced</summary>
-
-  A more powerful Docker command to run would be this:
-
-  ```bash
-docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-latest
-  ```
-
-  It additionally:
-  * Prevents memory issues when running e.g. deepspeed (e.g. you could hit SIGBUS/signal 7 error) through `--ipc` and `--ulimit` args.
-  * Persists the downloaded HF data (models etc.) and your modifications to axolotl code through `--mount`/`-v` args.
-  * The `--name` argument simply makes it easier to refer to the container in vscode (`Dev Containers: Attach to Running Container...`) or in your terminal.
-  * The `--privileged` flag gives all capabilities to the container.
-  * The `--shm-size 10g` argument increases the shared memory size. Use this if you see `exitcode: -7` errors using deepspeed.
-
-  [More information on nvidia website](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#setincshmem)
-
-  </details>
-
-#### Conda/Pip venv
-  1. Install python >=**3.10**
-
-  2. Install pytorch stable https://pytorch.org/get-started/locally/
-
-  3. Install Axolotl along with python dependencies
-        ```bash
-        pip3 install packaging
-        pip3 install -e '.[flash-attn,deepspeed]'
-        ```
-  4. (Optional) Login to Huggingface to use gated models/datasets.
-        ```bash
-        huggingface-cli login
-        ```
-        Get the token at huggingface.co/settings/tokens
-
-#### Cloud GPU
-
-For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud:main-latest`](https://hub.docker.com/r/winglian/axolotl-cloud/tags)
-
-- on Latitude.sh use this [direct link](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
-- on JarvisLabs.ai use this [direct link](https://jarvislabs.ai/templates/axolotl)
-- on RunPod use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
-
-#### Bare Metal Cloud GPU
-
-##### LambdaLabs
-
-  <details>
-
-  <summary>Click to Expand</summary>
-
-  1. Install python
-  ```bash
-  sudo apt update
-  sudo apt install -y python3.10
-
-  sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1
-  sudo update-alternatives --config python # pick 3.10 if given option
-  python -V # should be 3.10
-
-  ```
-
-  2. Install pip
-  ```bash
-  wget https://bootstrap.pypa.io/get-pip.py
-  python get-pip.py
-  ```
-
-  3. Install Pytorch https://pytorch.org/get-started/locally/
-
-  4. Follow instructions on quickstart.
-
-  5. Run
-  ```bash
-  pip3 install protobuf==3.20.3
-  pip3 install -U --ignore-installed requests Pillow psutil scipy
-  ```
-
-  6. Set path
-  ```bash
-  export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
-  ```
-  </details>
-
-##### GCP
-
-<details>
-
-<summary>Click to Expand</summary>
-
-Use a Deeplearning linux OS with cuda and pytorch installed. Then follow instructions on quickstart.
-
-Make sure to run the below to uninstall xla.
-```bash
-pip uninstall -y torch_xla[tpu]
-```
-
-</details>
-
-#### Windows
-Please use WSL or Docker!
-
-#### Mac
-
-Use the below instead of the install method in QuickStart.
-```
-pip3 install -e '.'
-```
-More info: [mac.md](/docs/mac.qmd)
-
-#### Google Colab
-
-Please use this example [notebook](examples/colab-notebooks/colab-axolotl-example.ipynb).
-
-#### Launching on public clouds via SkyPilot
-To launch on GPU instances (both on-demand and spot instances) on 7+ clouds (GCP, AWS, Azure, OCI, and more), you can use [SkyPilot](https://skypilot.readthedocs.io/en/latest/index.html):
-
-```bash
-pip install "skypilot-nightly[gcp,aws,azure,oci,lambda,kubernetes,ibm,scp]"  # choose your clouds
-sky check
-```
-
-Get the [example YAMLs](https://github.com/skypilot-org/skypilot/tree/master/llm/axolotl) of using Axolotl to finetune `mistralai/Mistral-7B-v0.1`:
-```
-git clone https://github.com/skypilot-org/skypilot.git
-cd skypilot/llm/axolotl
-```
-
-Use one command to launch:
-```bash
-# On-demand
-HF_TOKEN=xx sky launch axolotl.yaml --env HF_TOKEN
-
-# Managed spot (auto-recovery on preemption)
-HF_TOKEN=xx BUCKET=<unique-name> sky spot launch axolotl-spot.yaml --env HF_TOKEN --env BUCKET
-```
-
-#### Launching on public clouds via dstack
-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](https://dstack.ai/).
-
-Write a job description in YAML as below:
-
-```yaml
-# dstack.yaml
-type: task
-
-image: winglian/axolotl-cloud:main-20240429-py3.11-cu121-2.2.2
-
-env:
-  - HUGGING_FACE_HUB_TOKEN
-  - WANDB_API_KEY
-
-commands:
-  - accelerate launch -m axolotl.cli.train config.yaml
-
-ports:
-  - 6006
-
-resources:
-  gpu:
-    memory: 24GB..
-    count: 2
-```
-
-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:
-
-```bash
-pip install dstack
-HUGGING_FACE_HUB_TOKEN=xxx WANDB_API_KEY=xxx dstack run . -f dstack.yaml # --spot
-```
-
-For further and fine-grained use cases, please refer to the official [dstack documents](https://dstack.ai/docs/) and the detailed description of [axolotl example](https://github.com/dstackai/dstack/tree/master/examples/fine-tuning/axolotl) on the official repository.
-
-### Dataset
-
-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.
-
-See [these docs](https://openaccess-ai-collective.github.io/axolotl/docs/dataset-formats/) for more information on how to use different dataset formats.
-
-### Config
-
-See [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
-
-- model
-  ```yaml
-  base_model: ./llama-7b-hf # local or huggingface repo
-  ```
-  Note: The code will load the right architecture.
-
-- dataset
-  ```yaml
-  datasets:
-      # huggingface repo
-    - path: vicgalle/alpaca-gpt4
-      type: alpaca
-
-      # huggingface repo with specific configuration/subset
-    - path: EleutherAI/pile
-      name: enron_emails
-      type: completion # format from earlier
-      field: text # Optional[str] default: text, field to use for completion data
-
-      # huggingface repo with multiple named configurations/subsets
-    - path: bigcode/commitpackft
-      name:
-        - ruby
-        - python
-        - typescript
-      type: ... # unimplemented custom format
-
-      # fastchat conversation
-      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
-    - path: ...
-      type: sharegpt
-      conversation: chatml # default: vicuna_v1.1
-
-      # local
-    - path: data.jsonl # or json
-      ds_type: json # see other options below
-      type: alpaca
-
-      # dataset with splits, but no train split
-    - path: knowrohit07/know_sql
-      type: context_qa.load_v2
-      train_on_split: validation
-
-      # loading from s3 or gcs
-      # s3 creds will be loaded from the system default and gcs only supports public access
-    - path: s3://path_to_ds # Accepts folder with arrow/parquet or file path like above. Supports s3, gcs.
-      ...
-
-      # Loading Data From a Public URL
-      # - The file format is `json` (which includes `jsonl`) by default. For different formats, adjust the `ds_type` option accordingly.
-    - path: https://some.url.com/yourdata.jsonl # The URL should be a direct link to the file you wish to load. URLs must use HTTPS protocol, not HTTP.
-      ds_type: json # this is the default, see other options below.
-  ```
-
-- loading
-  ```yaml
-  load_in_4bit: true
-  load_in_8bit: true
-
-  bf16: auto # require >=ampere, auto will detect if your GPU supports this and choose automatically.
-  fp16: # leave empty to use fp16 when bf16 is 'auto'. set to false if you want to fallback to fp32
-  tf32: true # require >=ampere
-
-  bfloat16: true # require >=ampere, use instead of bf16 when you don't want AMP (automatic mixed precision)
-  float16: true # use instead of fp16 when you don't want AMP
-  ```
-  Note: Repo does not do 4-bit quantization.
-
-- lora
-  ```yaml
-  adapter: lora # 'qlora' or leave blank for full finetune
-  lora_r: 8
-  lora_alpha: 16
-  lora_dropout: 0.05
-  lora_target_modules:
-    - q_proj
-    - v_proj
-  ```
-
-#### All Config Options
-
-See [these docs](docs/config.qmd) for all config options.
-
-### Train
-
-Run
-```bash
-accelerate launch -m axolotl.cli.train your_config.yml
-```
-
-> [!TIP]
-> 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`
-
-#### Preprocess dataset
-
-You can optionally pre-tokenize dataset with the following before finetuning.
-This is recommended for large datasets.
-
-- Set `dataset_prepared_path:` to a local folder for saving and loading pre-tokenized dataset.
-- (Optional): Set `push_dataset_to_hub: hf_user/repo` to push it to Huggingface.
-- (Optional): Use `--debug` to see preprocessed examples.
-
-```bash
-python -m axolotl.cli.preprocess your_config.yml
-```
-
-#### Multi-GPU
-
-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](https://github.com/huggingface/transformers/issues/26498).
-
-##### DeepSpeed
-
-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
-
-We provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.
-
-```yaml
-deepspeed: deepspeed_configs/zero1.json
-```
-
-```shell
-accelerate launch -m axolotl.cli.train examples/llama-2/config.yml --deepspeed deepspeed_configs/zero1.json
-```
-
-##### FSDP
-
-- llama FSDP
-```yaml
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_offload_params: true
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-```
-
-##### FSDP + QLoRA
-
-Axolotl supports training with FSDP and QLoRA, see [these docs](docs/fsdp_qlora.qmd) for more information.
-
-##### Weights & Biases Logging
-
-Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
-
-- wandb options
-```yaml
-wandb_mode:
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-```
-
-##### Special Tokens
-
-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:
-
-```yml
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-tokens: # these are delimiters
-  - "<|im_start|>"
-  - "<|im_end|>"
-```
-
-When you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer's vocabulary.
-
-### Inference Playground
-
-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.
-
-Pass the appropriate flag to the inference command, depending upon what kind of model was trained:
-
-- Pretrained LORA:
-  ```bash
-  python -m axolotl.cli.inference examples/your_config.yml --lora_model_dir="./lora-output-dir"
-  ```
-- Full weights finetune:
-  ```bash
-  python -m axolotl.cli.inference examples/your_config.yml --base_model="./completed-model"
-  ```
-- Full weights finetune w/ a prompt from a text file:
-  ```bash
-  cat /tmp/prompt.txt | python -m axolotl.cli.inference examples/your_config.yml \
-    --base_model="./completed-model" --prompter=None --load_in_8bit=True
-  ```
--- With gradio hosting
-  ```bash
-  python -m axolotl.cli.inference examples/your_config.yml --gradio
-  ```
-
-Please use `--sample_packing False` if you have it on and receive the error similar to below:
-
-> 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
-
-### Merge LORA to base
-
-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`.
-
-```bash
-python3 -m axolotl.cli.merge_lora your_config.yml --lora_model_dir="./completed-model"
-```
-
-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
-
-```bash
-CUDA_VISIBLE_DEVICES="" python3 -m axolotl.cli.merge_lora ...
-```
-
-although this will be very slow, and using the config options above are recommended instead.
-
-## Common Errors 🧰
-
-See also the [FAQ's](./docs/faq.qmd) and [debugging guide](docs/debugging.qmd).
-
-> 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:
-
-Please reduce any below
-  - `micro_batch_size`
-  - `eval_batch_size`
-  - `gradient_accumulation_steps`
-  - `sequence_len`
-
-If it does not help, try running without deepspeed and without accelerate (replace "accelerate launch" with "python") in the command.
-
-Using adamw_bnb_8bit might also save you some memory.
-
-> `failed (exitcode: -9)`
-
-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: expected scalar type Float but found Half
-
-Try set `fp16: true`
-
-> NotImplementedError: No operator found for `memory_efficient_attention_forward` ...
-
-Try to turn off xformers.
-
-> accelerate config missing
-
-It's safe to ignore it.
-
-> NCCL Timeouts during training
-
-See the [NCCL](docs/nccl.qmd) guide.
-
-
-### Tokenization Mismatch b/w Inference & Training
-
-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.
-
-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:
-
-1. Materialize some data using `python -m axolotl.cli.preprocess your_config.yml --debug`, and then decode the first few rows with your model's tokenizer.
-2. During inference, right before you pass a tensor of token ids to your model, decode these tokens back into a string.
-3. Make sure the inference string from #2 looks **exactly** like the data you fine tuned on from #1, including spaces and new lines.  If they aren't the same, adjust your inference server accordingly.
-4. As an additional troubleshooting step, you can look at the token ids between 1 and 2 to make sure they are identical.
-
-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](https://hamel.dev/notes/llm/finetuning/05_tokenizer_gotchas.html) for a concrete example.
-
-## Debugging Axolotl
-
-See [this debugging guide](docs/debugging.qmd) for tips on debugging Axolotl, along with an example configuration for debugging with VSCode.
-
-## Need help? 🙋
-
-Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we our community members can help you.
-
-Need dedicated support? Please contact us at [✉️wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org) for dedicated support options.
-
-## Badge ❤🏷️
-
-Building something cool with Axolotl? Consider adding a badge to your model card.
-
-```markdown
-[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-```
-
-[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-
-## Community Showcase
-
-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.
-
-Open Access AI Collective
-- [Minotaur 13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed)
-- [Manticore 13b](https://huggingface.co/openaccess-ai-collective/manticore-13b)
-- [Hippogriff 30b](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat)
-
-PocketDoc Labs
-- [Dan's PersonalityEngine 13b LoRA](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b-LoRA)
-
-## Contributing 🤝
-
-Please read the [contributing guide](./.github/CONTRIBUTING.md)
-
-Bugs? Please check the [open issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues/bug) else create a new Issue.
-
-PRs are **greatly welcome**!
-
-Please run the quickstart instructions followed by the below to setup env:
-```bash
-pip3 install -r requirements-dev.txt -r requirements-tests.txt
-pre-commit install
-
-# test
-pytest tests/
-
-# optional: run against all files
-pre-commit run --all-files
-```
-
-Thanks to all of our contributors to date. Help drive open source AI progress forward by contributing to Axolotl.
-
-<a href="https://github.com/openaccess-ai-collective/axolotl/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=openaccess-ai-collective/axolotl" alt="contributor chart by https://contrib.rocks"/>
-</a>
-
-## Sponsors 🤝❤
-
-OpenAccess AI Collective is run by volunteer contributors such as [winglian](https://github.com/winglian),
-[NanoCode012](https://github.com/NanoCode012), [tmm1](https://github.com/tmm1),
-[mhenrichsen](https://github.com/mhenrichsen), [casper-hansen](https://github.com/casper-hansen),
-[hamelsmu](https://github.com/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](https://github.com/sponsors/OpenAccess-AI-Collective),
-[Ko-fi](https://ko-fi.com/axolotl_ai) or reach out directly to
-[wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org).
-
----
-
-#### 💎 Diamond Sponsors - [Contact directly](mailto:wing@openaccessaicollective.org)
-
----
-
-#### 🥇 Gold Sponsors - $5000/mo
-
----
-
-#### 🥈 Silver Sponsors - $1000/mo
-
----
-
-#### 🥉 Bronze Sponsors - $500/mo
-
- - [JarvisLabs.ai](https://jarvislabs.ai)
-
 ---
+title: Nknjl
+emoji: 💻🐳
+colorFrom: gray
+colorTo: green
+sdk: docker
+pinned: false
+tags:
+- jupyterlab
+suggested_storage: small
+---
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference