559 lines
16 KiB
Markdown
559 lines
16 KiB
Markdown
# Axolotl
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<div align="center">
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<img src="image/axolotl.png" alt="axolotl" width="160">
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<div>
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<p>
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<b>One repo to finetune them all! </b>
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</p>
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<p>
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Go ahead and axolotl questions!!
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</p>
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<img src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/pre-commit.yml/badge.svg?branch=main" alt="pre-commit">
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<img alt="PyTest Status" src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/tests.yml/badge.svg?branch=main">
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</div>
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</div>
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## Axolotl supports
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| | fp16/fp32 | lora | qlora | gptq | gptq w/ lora | gptq w/flash attn | flash attn | xformers attn |
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|----------|:----------|:-----|-------|------|:-------------|-------------------|------------|---------------|
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| llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Pythia | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ❓ |
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| cerebras | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ✅ |
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| mpt | ✅ | ❌ | ❓ | ❌ | ❓ | ❌ | ❌ | ❓ |
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| falcon | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ✅ |
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| gpt-j | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❓ | ✅ |
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## Quickstart ⚡
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**Requirements**: Python 3.9 and Pytorch 2.0.
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```bash
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git clone https://github.com/OpenAccess-AI-Collective/axolotl
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pip3 install -e .
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pip3 install -U git+https://github.com/huggingface/peft.git
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accelerate config
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# finetune lora
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accelerate launch scripts/finetune.py examples/lora-openllama-3b/config.yml
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# inference
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accelerate launch scripts/finetune.py examples/lora-openllama-3b/config.yml \
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--inference --lora_model_dir="./lora-out"
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```
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## Installation
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### Environment
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- Docker
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```bash
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docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.9-cu118-2.0.0
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```
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- `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0`: for runpod
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- `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0-gptq`: for gptq
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- `winglian/axolotl:dev`: dev branch (not usually up to date)
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Or run on the current files for development:
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```sh
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docker compose up -d
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```
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- Conda/Pip venv
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1. Install python **3.9**
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2. Install pytorch stable https://pytorch.org/get-started/locally/
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3. Install python dependencies with ONE of the following:
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- Recommended, supports QLoRA, NO gptq/int4 support
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```bash
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pip3 install -e .
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pip3 install -U git+https://github.com/huggingface/peft.git
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```
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- gptq/int4 support, NO QLoRA
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```bash
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pip3 install -e .[gptq]
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```
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- same as above but not recommended
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```bash
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pip3 install -e .[gptq_triton]
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```
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- LambdaLabs
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<details>
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<summary>Click to Expand</summary>
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1. Install python
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```bash
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sudo apt update
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sudo apt install -y python3.9
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sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1
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sudo update-alternatives --config python # pick 3.9 if given option
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python -V # should be 3.9
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```
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2. Install pip
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```bash
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wget https://bootstrap.pypa.io/get-pip.py
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python get-pip.py
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```
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3. Install torch
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```bash
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
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```
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4. Axolotl
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```bash
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git clone https://github.com/OpenAccess-AI-Collective/axolotl
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cd axolotl
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pip3 install -e . # change depend on needs
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pip3 install protobuf==3.20.3
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pip3 install -U requests
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pip3 install -U --ignore-installed psutil
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pip3 install -U scipy
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pip3 install git+https://github.com/huggingface/peft.git # not for gptq
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```
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5. Set path
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```bash
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export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
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```
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</details>
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### Dataset
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Have dataset(s) in one of the following format (JSONL recommended):
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- `alpaca`: instruction; input(optional)
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```json
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{"instruction": "...", "input": "...", "output": "..."}
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```
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- `sharegpt`: conversations
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```json
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{"conversations": [{"from": "...", "value": "..."}]}
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```
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- `completion`: raw corpus
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```json
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{"text": "..."}
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```
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<details>
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<summary>See other formats</summary>
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- `jeopardy`: question and answer
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```json
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{"question": "...", "category": "...", "answer": "..."}
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```
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- `oasst`: instruction
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```json
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{"INSTRUCTION": "...", "RESPONSE": "..."}
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```
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- `gpteacher`: instruction; input(optional)
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```json
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{"instruction": "...", "input": "...", "response": "..."}
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```
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- `reflection`: instruction with reflect; input(optional)
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```json
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{"instruction": "...", "input": "...", "output": "...", "reflection": "...", "corrected": "..."}
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```
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- `explainchoice`: question, choices, (solution OR explanation)
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```json
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{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
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```
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- `concisechoice`: question, choices, (solution OR explanation)
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```json
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{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
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```
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- `summarizetldr`: article and summary
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```json
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{"article": "...", "summary": "..."}
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```
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- `alpaca_chat`: basic instruct for alpaca chat
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```json
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{"instruction": "...", "input": "...", "response": "..."}
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```
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- `alpaca_chat.load_qa`: question and answer for alpaca chat
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```json
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{"question": "...", "answer": "..."}
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```
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- `alpaca_chat.load_concise`: question and answer for alpaca chat, for concise answers
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```json
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{"instruction": "...", "input": "...", "response": "..."}
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```
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- `alpaca_chat.load_camel_ai`: question and answer for alpaca chat, for load_camel_ai
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```json
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{"message_1": "...", "message_2": "..."}
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```
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- `context_qa`: in context question answering from an article
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```json
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{"article": "...", "question": "...", "answer": "..."}
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```
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- `context_qa.load_404`: in context question answering from an article, with default response for no answer from context
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```json
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{"article": "...", "unanswerable_question": "..."}
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```
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- `creative_acr.load_answer`: instruction and revision
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```json
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{"instruction": "...", "revision": "..."}
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```
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- `creative_acr.load_critique`: critique
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```json
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{"scores": "...", "critiques": "...", "instruction": "...", "answer": "..."}
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```
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- `creative_acr.load_revise`: critique and revise
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```json
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{"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
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```
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- `pygmalion`: pygmalion
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```json
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{"conversations": [{"role": "...", "value": "..."}]}
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```
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</details>
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#### How to add custom prompts
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1. Add your method to a file in [prompt_strategies](src/axolotl/prompt_strategies). Please see other files as example.
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2. Use your custom file name as the dataset type.
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Optionally, download some datasets, see [data/README.md](data/README.md)
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### Config
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See sample configs in [configs](configs) folder or [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
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- model
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```yaml
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base_model: ./llama-7b-hf # local or huggingface repo
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```
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Note: The code will load the right architecture.
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- dataset
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```yaml
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datasets:
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- path: vicgalle/alpaca-gpt4 # local or huggingface repo
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type: alpaca # format from earlier
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sequence_len: 2048 # max token length / prompt
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```
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- loading
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```yaml
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load_in_4bit: true
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load_in_8bit: true
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bf16: true # require >=ampere
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fp16: true
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tf32: true # require >=ampere
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```
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Note: Repo does not do 4-bit quantization.
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- lora
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```yaml
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adapter: lora # qlora or leave blank for full finetune
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lora_r: 8
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules:
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- q_proj
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- v_proj
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```
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<details>
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<summary>All yaml options</summary>
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```yaml
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# this is the huggingface model that contains *.pt, *.safetensors, or *.bin files
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# this can also be a relative path to a model on disk
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base_model: ./llama-7b-hf
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# you can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
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base_model_ignore_patterns:
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# if the base_model repo on hf hub doesn't include configuration .json files,
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# you can set that here, or leave this empty to default to base_model
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base_model_config: ./llama-7b-hf
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# Optional tokenizer configuration override in case you want to use a different tokenizer
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# than the one defined in the base model
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tokenizer_config:
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# If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
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model_type: AutoModelForCausalLM
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# Corresponding tokenizer for the model AutoTokenizer is a good choice
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tokenizer_type: AutoTokenizer
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# Trust remote code for untrusted source
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trust_remote_code:
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# whether you are training a 4-bit GPTQ quantized model
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gptq: true
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gptq_groupsize: 128 # group size
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gptq_model_v1: false # v1 or v2
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# this will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
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load_in_8bit: true
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# use bitsandbytes 4 bit
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load_in_4bit:
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# Use CUDA bf16
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bf16: true # bool or 'full' for `bf16_full_eval`. require >=ampere
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# Use CUDA fp16
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fp16: true
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# Use CUDA tf32
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tf32: true # require >=ampere
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# a list of one or more datasets to finetune the model with
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datasets:
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# this can be either a hf dataset, or relative path
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- path: vicgalle/alpaca-gpt4
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# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
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type: alpaca # format OR format:prompt_style (chat/instruct)
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data_files: # path to source data files
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shards: # number of shards to split data into
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# axolotl attempts to save the dataset as an arrow after packing the data together so
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# subsequent training attempts load faster, relative path
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dataset_prepared_path: data/last_run_prepared
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# push prepared dataset to hub
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push_dataset_to_hub: # repo path
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# whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
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# required to be true when used in combination with `push_dataset_to_hub`
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hf_use_auth_token: # boolean
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# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc
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val_set_size: 0.04
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# Num shards for whole dataset
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dataset_shard_num:
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# Index of shard to use for whole dataset
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dataset_shard_idx:
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# the maximum length of an input to train with, this should typically be less than 2048
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# as most models have a token/context limit of 2048
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sequence_len: 2048
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# max sequence length to concatenate training samples together up to
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# inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
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max_packed_sequence_len: 1024
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# if you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
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adapter: lora
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# if you already have a lora model trained that you want to load, put that here
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# lora hyperparameters
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lora_model_dir:
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lora_r: 8
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules:
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- q_proj
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- v_proj
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# - k_proj
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# - o_proj
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# - gate_proj
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# - down_proj
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# - up_proj
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lora_target_linear: # if true, will target all linear layers
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lora_modules_to_save:
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# - embed_tokens
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# - lm_head
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lora_out_dir:
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lora_fan_in_fan_out: false
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# wandb configuration if you're using it
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wandb_mode:
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wandb_project:
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wandb_watch:
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wandb_run_id:
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wandb_log_model: # 'checkpoint'
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# where to save the finished model to
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output_dir: ./completed-model
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# training hyperparameters
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gradient_accumulation_steps: 1
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micro_batch_size: 2
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eval_batch_size: 2
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num_epochs: 3
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warmup_steps: 100
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learning_rate: 0.00003
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logging_steps:
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# whether to mask out or include the human's prompt from the training labels
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train_on_inputs: false
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# don't use this, leads to wonky training (according to someone on the internet)
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group_by_length: false
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# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
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gradient_checkpointing: false
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# stop training after this many evaluation losses have increased in a row
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# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
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early_stopping_patience: 3
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# specify a scheduler and kwargs to use with the optimizer
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lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
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lr_scheduler_kwargs:
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# for one_cycle optim
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lr_div_factor: # learning rate div factor
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# for log_sweep optim
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log_sweep_min_lr:
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log_sweep_max_lr:
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# specify optimizer
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optimizer:
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# specify weight decay
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weight_decay:
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# whether to use xformers attention patch https://github.com/facebookresearch/xformers:
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xformers_attention:
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# whether to use flash attention patch https://github.com/HazyResearch/flash-attention:
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flash_attention: # require a100 for llama
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# whether to use scaled-dot-product attention
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# https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
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sdp_attention:
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# Landmark attention (only llama)
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landmark_attention:
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# xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
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# llama only
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xpos_rope:
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# resume from a specific checkpoint dir
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resume_from_checkpoint:
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# if resume_from_checkpoint isn't set and you simply want it to start where it left off
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# be careful with this being turned on between different models
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auto_resume_from_checkpoints: false
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# don't mess with this, it's here for accelerate and torchrun
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local_rank:
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# add or change special tokens
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special_tokens:
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# bos_token: "<s>"
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# eos_token: "</s>"
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# unk_token: "<unk>"
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# add extra tokens
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tokens:
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# FSDP
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fsdp:
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fsdp_config:
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# Deepspeed
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deepspeed:
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# Path to torch distx for optim 'adamw_anyprecision'
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torchdistx_path:
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# Set padding for data collator to 'longest'
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collator_pad_to_longest:
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# Debug mode
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debug:
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# Seed
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seed:
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# Allow overwrite yml config using from cli
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strict:
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```
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</details>
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### Accelerate
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Configure accelerate
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```bash
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accelerate config
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# Edit manually
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# nano ~/.cache/huggingface/accelerate/default_config.yaml
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```
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### Train
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Run
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```bash
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accelerate launch scripts/finetune.py configs/your_config.yml
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```
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### Inference
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Pass the appropriate flag to the train command:
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- Pretrained LORA:
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```bash
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--inference --lora_model_dir ./completed-model
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```
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- Full weights finetune:
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```bash
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--inference --base_model ./completed-model
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```
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- Full weights finetune w/ a prompt from a text file:
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```bash
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cat /tmp/prompt.txt | python scripts/finetune.py configs/your_config.yml \
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--base_model ./completed-model --inference --prompter=None --load_in_8bit=True
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```
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### Merge LORA to base
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Add below flag to train command above
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```bash
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--merge_lora --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
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```
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## Common Errors 🧰
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> Cuda out of memory
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Please reduce any below
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- `micro_batch_size`
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|
- `eval_batch_size`
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- `gradient_accumulation_steps`
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- `sequence_len`
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> RuntimeError: expected scalar type Float but found Half
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Try set `fp16: true`
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> NotImplementedError: No operator found for `memory_efficient_attention_forward` ...
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Try to turn off xformers.
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## Need help? 🙋♂️
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Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you
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## Badge ❤🏷️
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Building something cool with Axolotl? Consider adding a badge to your model card.
|
|
|
|
```markdown
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|
[<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)
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|
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## Contributing 🤝
|
|
|
|
Bugs? Please check for open issue else create a new [Issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues/new).
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|
|
PRs are **greatly welcome**!
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|
|
|
Please run below to setup env
|
|
```bash
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pip3 install -r requirements-dev.txt -r requirements-tests.txt
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pre-commit install
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|
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# test
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|
pytest tests/
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|
```
|