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emuchogu fe4b9da764 pascal support (#870) 
Co-authored-by: Edward Muchogu <muchogu@gmail.com>
2024-08-04 23:45:51 -07:00

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<div align="center">
<a href="https://unsloth.ai"><picture>
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png">
<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png">
<img alt="unsloth logo" src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png" height="110" style="max-width: 100%;">
</picture></a>
<a href="https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/start free finetune button.png" height="48"></a>
<a href="https://discord.gg/unsloth"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord button.png" height="48"></a>
<a href="https://ko-fi.com/unsloth"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy me a coffee button.png" height="48"></a>
### Finetune Llama 3.1, Mistral, Phi-3 & Gemma 2-5x faster with 80% less memory!
![](https://i.ibb.co/sJ7RhGG/image-41.png)
</div>
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, Ollama, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------|---------|--------|----------|
| **Llama 3.1 (8B)** | [▶️ Start for free](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2x faster | 60% less |
| **Mistral Nemo (12B)** | [▶️ Start for free](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing) | 2x faster | 60% less |
| **Gemma 2 (9B)** | [▶️ Start for free](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) | 2x faster | 63% less |
| **Phi-3 (mini)** | [▶️ Start for free](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) | 2x faster | 50% less |
| **Ollama** | [▶️ Start for free](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing) | 1.9x faster | 43% less |
| **Mistral v0.3 (7B)** | [▶️ Start for free](https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing) | 2.2x faster | 73% less |
| **ORPO** | [▶️ Start for free](https://colab.research.google.com/drive/11t4njE3c4Lxl-07OD8lJSMKkfyJml3Tn?usp=sharing) | 1.9x faster | 43% less |
| **DPO Zephyr** | [▶️ Start for free](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 43% less |
| **TinyLlama** | [▶️ Start for free](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
- **Kaggle Notebooks** for [Llama 3.1 (8B)](https://www.kaggle.com/danielhanchen/kaggle-llama-3-1-8b-unsloth-notebook), [Gemma 2 (9B)](https://www.kaggle.com/code/danielhanchen/kaggle-gemma-7b-unsloth-notebook/), [Mistral (7B)](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)
- Run [Llama 3 conversational notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) and [Mistral v0.3 ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing)
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for continued pretraining / raw text
- This [continued pretraining notebook](https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing) is for learning another language
- Click [here](https://github.com/unslothai/unsloth/wiki) for detailed documentation for Unsloth.
## 🦥 Unsloth.ai News
- 📣 NEW! [Gemma-2-2b](https://colab.research.google.com/drive/1weTpKOjBZxZJ5PQ-Ql8i6ptAY2x-FWVA?usp=sharing) now supported! Gemma-2-9b and Gemma-2-27b are alrady supported! And uploaded [GGUF quants](https://huggingface.co/unsloth/gemma-2-it-GGUF) Try out [Chat interface](https://colab.research.google.com/drive/1i-8ESvtLRGNkkUQQr_-z_rcSAIo9c3lM?usp=sharing) for Gemma-2-2b Instruct!
- 📣 NEW! [Llama 3.1 8b, 70b](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) both Base and Instruct now supported
- 📣 NEW! [Mistral Nemo-12b](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing) both Base and Instruct now supported
- 📣 NEW! [Gemma-2-9b](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) and Gemma-2-27b now supported
- 📣 UPDATE! [Phi-3 mini](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing) model updated. [Phi-3 Medium](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing) 2x faster finetuning.
- 📣 NEW! Continued Pretraining [notebook](https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing) for other languages like Korean!
- 📣 NEW! Qwen2 now works
- 📣 [Mistral v0.3 Base](https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing) and [Mistral v0.3 Instruct]
- 📣 [ORPO support](https://colab.research.google.com/drive/11t4njE3c4Lxl-07OD8lJSMKkfyJml3Tn?usp=sharing) is here + [2x faster inference](https://colab.research.google.com/drive/1aqlNQi7MMJbynFDyOQteD2t0yVfjb9Zh?usp=sharing) added for all our models
- 📣 We cut memory usage by a [further 30%](https://unsloth.ai/blog/long-context) and now support [4x longer context windows](https://unsloth.ai/blog/long-context)!
## 🔗 Links and Resources
| Type | Links |
| ------------------------------- | --------------------------------------- |
| 📚 **Documentation & Wiki** | [Read Our Wiki](https://github.com/unslothai/unsloth/wiki) |
| <img height="14" src="https://upload.wikimedia.org/wikipedia/commons/6/6f/Logo_of_Twitter.svg" />&nbsp; **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai)|
| 💾 **Installation** | [unsloth/README.md](https://github.com/unslothai/unsloth/tree/main#installation-instructions)|
| 🥇 **Benchmarking** | [Performance Tables](https://github.com/unslothai/unsloth/tree/main#-performance-benchmarking)
| 🌐 **Released Models** | [Unsloth Releases](https://huggingface.co/unsloth)|
| ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog)|
## ⭐ Key Features
- All kernels written in [OpenAI's Triton](https://openai.com/research/triton) language. **Manual backprop engine**.
- **0% loss in accuracy** - no approximation methods - all exact.
- No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow.
- Works on **Linux** and **Windows** via WSL.
- Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
- Open source trains 5x faster - see [Unsloth Pro](https://unsloth.ai/) for up to **30x faster training**!
- If you trained a model with 🦥Unsloth, you can use this cool sticker! &nbsp; <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" height="50" align="center" />
## 🥇 Performance Benchmarking
- For the full list of **reproducible** benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)
| 1 A100 40GB | 🤗Hugging Face | Flash Attention | 🦥Unsloth Open Source | 🦥[Unsloth Pro](https://unsloth.ai/pricing) |
|--------------|--------------|-----------------|---------------------|-----------------|
| Alpaca | 1x | 1.04x | 1.98x | **15.64x** |
| LAION Chip2 | 1x | 0.92x | 1.61x | **20.73x** |
| OASST | 1x | 1.19x | 2.17x | **14.83x** |
| Slim Orca | 1x | 1.18x | 2.22x | **14.82x** |
- Benchmarking table below was conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl).
| Free Colab T4 | Dataset | 🤗Hugging Face | Pytorch 2.1.1 | 🦥Unsloth | 🦥 VRAM reduction |
| --- | --- | --- | --- | --- | --- |
| Llama-2 7b | OASST | 1x | 1.19x | 1.95x | -43.3% |
| Mistral 7b | Alpaca | 1x | 1.07x | 1.56x | -13.7% |
| Tiny Llama 1.1b | Alpaca | 1x | 2.06x | 3.87x | -73.8% |
| DPO with Zephyr | Ultra Chat | 1x | 1.09x | 1.55x | -18.6% |
![](https://i.ibb.co/sJ7RhGG/image-41.png)
## 💾 Installation Instructions
### Conda Installation
Select either `pytorch-cuda=11.8` for CUDA 11.8 or `pytorch-cuda=12.1` for CUDA 12.1. If you have `mamba`, use `mamba` instead of `conda` for faster solving. See this [Github issue](https://github.com/unslothai/unsloth/issues/73) for help on debugging Conda installs.
```bash
conda create --name unsloth_env \
python=3.10 \
pytorch-cuda=<11.8/12.1> \
pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \
-y
conda activate unsloth_env
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps "trl<0.9.0" peft accelerate bitsandbytes
```
### Pip Installation
Do **NOT** use this if you have Anaconda. You must use the Conda install method, or else stuff will BREAK.
1. Find your CUDA version via
```python
import torch; torch.version.cuda
```
2. For Pytorch 2.1.0: You can update Pytorch via Pip (interchange `cu121` / `cu118`). Go to https://pytorch.org/ to learn more. Select either `cu118` for CUDA 11.8 or `cu121` for CUDA 12.1. If you have a RTX 3060 or higher (A100, H100 etc), use the `"ampere"` path. For Pytorch 2.1.1: go to step 3. For Pytorch 2.2.0: go to step 4.
```bash
pip install --upgrade --force-reinstall --no-cache-dir torch==2.1.0 triton \
--index-url https://download.pytorch.org/whl/cu121
```
```bash
pip install "unsloth[cu118] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere] @ git+https://github.com/unslothai/unsloth.git"
```
3. For Pytorch 2.1.1: Use the `"ampere"` path for newer RTX 30xx GPUs or higher.
```bash
pip install --upgrade --force-reinstall --no-cache-dir torch==2.1.1 triton \
--index-url https://download.pytorch.org/whl/cu121
```
```bash
pip install "unsloth[cu118-torch211] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch211] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch211] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch211] @ git+https://github.com/unslothai/unsloth.git"
```
4. For Pytorch 2.2.0: Use the `"ampere"` path for newer RTX 30xx GPUs or higher.
```bash
pip install --upgrade --force-reinstall --no-cache-dir torch==2.2.0 triton \
--index-url https://download.pytorch.org/whl/cu121
```
```bash
pip install "unsloth[cu118-torch220] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch220] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch220] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch220] @ git+https://github.com/unslothai/unsloth.git"
```
5. If you get errors, try the below first, then go back to step 1:
```bash
pip install --upgrade pip
```
6. For Pytorch 2.2.1:
```bash
# RTX 3090, 4090 Ampere GPUs:
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes
# Pre Ampere RTX 2080, T4, GTX 1080 GPUs:
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes
```
7. For Pytorch 2.3.0: Use the `"ampere"` path for newer RTX 30xx GPUs or higher.
```bash
pip install "unsloth[cu118-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"
```
8. To troubleshoot installs try the below (all must succeed). Xformers should mostly all be available.
```bash
nvcc
python -m xformers.info
python -m bitsandbytes
```
## 📜 Documentation
- Go to our [Wiki page](https://github.com/unslothai/unsloth/wiki) for saving to GGUF, checkpointing, evaluation and more!
- We support Huggingface's TRL, Trainer, Seq2SeqTrainer or even Pytorch code!
- We're in 🤗Hugging Face's official docs! Check out the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!
```python
from unsloth import FastLanguageModel
from unsloth import is_bfloat16_supported
import torch
from trl import SFTTrainer
from transformers import TrainingArguments
from datasets import load_dataset
max_seq_length = 2048 # Supports RoPE Scaling interally, so choose any!
# Get LAION dataset
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
dataset = load_dataset("json", data_files = {"train" : url}, split = "train")
# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
"unsloth/mistral-7b-v0.3-bnb-4bit", # New Mistral v3 2x faster!
"unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"unsloth/llama-3-8b-bnb-4bit", # Llama-3 15 trillion tokens model 2x faster!
"unsloth/llama-3-8b-Instruct-bnb-4bit",
"unsloth/llama-3-70b-bnb-4bit",
"unsloth/Phi-3-mini-4k-instruct", # Phi-3 2x faster!
"unsloth/Phi-3-medium-4k-instruct",
"unsloth/mistral-7b-bnb-4bit",
"unsloth/gemma-7b-bnb-4bit", # Gemma 2.2x faster!
] # More models at https://huggingface.co/unsloth
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "unsloth/llama-3-8b-bnb-4bit",
max_seq_length = max_seq_length,
dtype = None,
load_in_4bit = True,
)
# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
model,
r = 16,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 16,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
max_seq_length = max_seq_length,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
trainer = SFTTrainer(
model = model,
train_dataset = dataset,
dataset_text_field = "text",
max_seq_length = max_seq_length,
tokenizer = tokenizer,
args = TrainingArguments(
per_device_train_batch_size = 2,
gradient_accumulation_steps = 4,
warmup_steps = 10,
max_steps = 60,
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
logging_steps = 1,
output_dir = "outputs",
optim = "adamw_8bit",
seed = 3407,
),
)
trainer.train()
# Go to https://github.com/unslothai/unsloth/wiki for advanced tips like
# (1) Saving to GGUF / merging to 16bit for vLLM
# (2) Continued training from a saved LoRA adapter
# (3) Adding an evaluation loop / OOMs
# (4) Customized chat templates
```
<a name="DPO"></a>
## DPO Support
DPO (Direct Preference Optimization), PPO, Reward Modelling all seem to work as per 3rd party independent testing from [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory). We have a preliminary Google Colab notebook for reproducing Zephyr on Tesla T4 here: [notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing).
We're in 🤗Hugging Face's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!
```python
from unsloth import FastLanguageModel, PatchDPOTrainer
from unsloth import is_bfloat16_supported
PatchDPOTrainer()
import torch
from transformers import TrainingArguments
from trl import DPOTrainer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "unsloth/zephyr-sft-bnb-4bit",
max_seq_length = max_seq_length,
dtype = None,
load_in_4bit = True,
)
# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
model,
r = 64,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 64,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
max_seq_length = max_seq_length,
)
dpo_trainer = DPOTrainer(
model = model,
ref_model = None,
args = TrainingArguments(
per_device_train_batch_size = 4,
gradient_accumulation_steps = 8,
warmup_ratio = 0.1,
num_train_epochs = 3,
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
seed = 42,
output_dir = "outputs",
),
beta = 0.1,
train_dataset = YOUR_DATASET_HERE,
# eval_dataset = YOUR_DATASET_HERE,
tokenizer = tokenizer,
max_length = 1024,
max_prompt_length = 512,
)
dpo_trainer.train()
```
## 🥇 Detailed Benchmarking Tables
- Click "Code" for fully reproducible examples
- "Unsloth Equal" is a preview of our PRO version, with code stripped out. All settings and the loss curve remains identical.
- For the full list of benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)
| 1 A100 40GB | 🤗Hugging Face | Flash Attention 2 | 🦥Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
| Alpaca | 1x | 1.04x | 1.98x | 2.48x | 5.32x | **15.64x** |
| code | [Code](https://colab.research.google.com/drive/1u4dBeM-0vGNVmmO6X7cScAut-Hyt4KDF?usp=sharing) | [Code](https://colab.research.google.com/drive/1fgTOxpMbVjloQBvZyz4lF4BacKSZOB2A?usp=sharing) | [Code](https://colab.research.google.com/drive/1YIPY_18xm-K0iJDgvNkRoJsgkPMPAO3G?usp=sharing) | [Code](https://colab.research.google.com/drive/1ANW8EFL3LVyTD7Gq4TkheC1Z7Rxw-rHp?usp=sharing) | | |
| seconds| 1040 | 1001 | 525 | 419 | 196 | 67 |
| memory MB| 18235 | 15365 | 9631 | 8525 | | |
| % saved| | 15.74 | 47.18 | 53.25 | | | |
### Llama-Factory 3rd party benchmarking
- [Link to performance table.](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-Comparison) TGS: tokens per GPU per second. Model: LLaMA2-7B. GPU: NVIDIA A100 * 1. Batch size: 4. Gradient accumulation: 2. LoRA rank: 8. Max length: 1024.
| Method | Bits | TGS | GRAM | Speed |
| --- | --- | --- | --- | --- |
| HF | 16 | 2392 | 18GB | 100% |
| HF+FA2 | 16 | 2954 | 17GB | 123% |
| Unsloth+FA2 | 16 | 4007 | 16GB | **168%** |
| HF | 4 | 2415 | 9GB | 101% |
| Unsloth+FA2 | 4 | 3726 | 7GB | **160%** |
### Performance comparisons between popular models
<details>
<summary>Click for specific model benchmarking tables (Mistral 7b, CodeLlama 34b etc.)</summary>
### Mistral 7b
| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
| Mistral 7B Slim Orca | 1x | 1.15x | 2.15x | 2.53x | 4.61x | **13.69x** |
| code | [Code](https://colab.research.google.com/drive/1mePk3KzwTD81hr5mcNcs_AX3Kbg_Ha0x?usp=sharing) | [Code](https://colab.research.google.com/drive/1dgHxjvTmX6hb0bPcLp26RXSE6_n9DKj7?usp=sharing) | [Code](https://colab.research.google.com/drive/1SKrKGV-BZoU4kv5q3g0jtE_OhRgPtrrQ?usp=sharing) | [Code](https://colab.research.google.com/drive/18yOiyX0T81mTwZqOALFSCX_tSAqju6aD?usp=sharing) | |
| seconds | 1813 | 1571 | 842 | 718 | 393 | 132 |
| memory MB | 32853 | 19385 | 12465 | 10271 | | |
| % saved| | 40.99 | 62.06 | 68.74 | | |
### CodeLlama 34b
| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
| Code Llama 34B | OOM ❌ | 0.99x | 1.87x | 2.61x | 4.27x | 12.82x |
| code | [▶️ Code](https://colab.research.google.com/drive/1ykfz3BqrtC_AUFegCzUQjjfUNlxp6Otc?usp=sharing) | [Code](https://colab.research.google.com/drive/12ZypxQh7OC6kBXvWZI-5d05I4m-B_hoR?usp=sharing) | [Code](https://colab.research.google.com/drive/1gdHyAx8XJsz2yNV-DHvbHjR1iCef5Qmh?usp=sharing) | [Code](https://colab.research.google.com/drive/1fm7wqx9MJ0kRrwKOfmLkK1Rmw-pySahB?usp=sharing) | |
| seconds | 1953 | 1982 | 1043 | 748 | 458 | 152 |
| memory MB | 40000 | 33217 | 27413 | 22161 | | |
| % saved| | 16.96| 31.47 | 44.60 | | | |
### 1 Tesla T4
| 1 T4 16GB | Hugging Face | Flash Attention | Unsloth Open | Unsloth Pro Equal | Unsloth Pro | Unsloth Max |
|--------------|-------------|-----------------|-----------------|---------------|---------------|-------------|
| Alpaca | 1x | 1.09x | 1.69x | 1.79x | 2.93x | **8.3x** |
| code | [▶️ Code](https://colab.research.google.com/drive/1XpLIV4s8Bj5uryB-X2gqM88oRGHEGdaB?usp=sharing) | [Code](https://colab.research.google.com/drive/1LyXu6CjuymQg6ddHX8g1dpUvrMa1nn4L?usp=sharing) | [Code](https://colab.research.google.com/drive/1gsv4LpY7C32otl1rgRo5wXTk4HIitXoM?usp=sharing) | [Code](https://colab.research.google.com/drive/1VtULwRQwhEnVdNryjm27zXfdSM1tNfFK?usp=sharing) | | |
| seconds | 1599 | 1468 | 942 | 894 | 545 | 193 |
| memory MB | 7199 | 7059 | 6459 | 5443 | | |
| % saved | | 1.94 | 10.28 | 24.39 | | |
### 2 Tesla T4s via DDP
| 2 T4 DDP | Hugging Face | Flash Attention | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
|--------------|----------|-------------|-----------------|--------------|---------------|-------------|
| Alpaca | 1x | 0.99x | 4.95x | 4.44x | 7.28x | **20.61x** |
| code | [▶️ Code](https://www.kaggle.com/danielhanchen/hf-original-alpaca-t4-ddp) | [Code](https://www.kaggle.com/danielhanchen/hf-sdpa-alpaca-t4-ddp) | [Code](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp) | | |
| seconds | 9882 | 9946 | 1996 | 2227 | 1357 | 480 |
| memory MB| 9176 | 9128 | 6904 | 6782 | | |
| % saved | | 0.52 | 24.76 | 26.09 | | | |
</details>
### Performance comparisons on 1 Tesla T4 GPU:
<details>
<summary>Click for Time taken for 1 epoch</summary>
One Tesla T4 on Google Colab
`bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10`
| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) |
| --- | --- | --- | --- | --- | --- |
| Huggingface | 1 T4 | 23h 15m | 56h 28m | 8h 38m | 391h 41m |
| Unsloth Open | 1 T4 | 13h 7m (1.8x) | 31h 47m (1.8x) | 4h 27m (1.9x) | 240h 4m (1.6x) |
| Unsloth Pro | 1 T4 | 3h 6m (7.5x) | 5h 17m (10.7x) | 1h 7m (7.7x) | 59h 53m (6.5x) |
| Unsloth Max | 1 T4 | 2h 39m (8.8x) | 4h 31m (12.5x) | 0h 58m (8.9x) | 51h 30m (7.6x) |
**Peak Memory Usage**
| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) |
| --- | --- | --- | --- | --- | --- |
| Huggingface | 1 T4 | 7.3GB | 5.9GB | 14.0GB | 13.3GB |
| Unsloth Open | 1 T4 | 6.8GB | 5.7GB | 7.8GB | 7.7GB |
| Unsloth Pro | 1 T4 | 6.4GB | 6.4GB | 6.4GB | 6.4GB |
| Unsloth Max | 1 T4 | 11.4GB | 12.4GB | 11.9GB | 14.4GB |
</details>
<details>
<summary>Click for Performance Comparisons on 2 Tesla T4 GPUs via DDP:</summary>
**Time taken for 1 epoch**
Two Tesla T4s on Kaggle
`bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10`
| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) * |
| --- | --- | --- | --- | --- | --- |
| Huggingface | 2 T4 | 84h 47m | 163h 48m | 30h 51m | 1301h 24m * |
| Unsloth Pro | 2 T4 | 3h 20m (25.4x) | 5h 43m (28.7x) | 1h 12m (25.7x) | 71h 40m (18.1x) * |
| Unsloth Max | 2 T4 | 3h 4m (27.6x) | 5h 14m (31.3x) | 1h 6m (28.1x) | 54h 20m (23.9x) * |
**Peak Memory Usage on a Multi GPU System (2 GPUs)**
| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) * |
| --- | --- | --- | --- | --- | --- |
| Huggingface | 2 T4 | 8.4GB \| 6GB | 7.2GB \| 5.3GB | 14.3GB \| 6.6GB | 10.9GB \| 5.9GB * |
| Unsloth Pro | 2 T4 | 7.7GB \| 4.9GB | 7.5GB \| 4.9GB | 8.5GB \| 4.9GB | 6.2GB \| 4.7GB * |
| Unsloth Max | 2 T4 | 10.5GB \| 5GB | 10.6GB \| 5GB | 10.6GB \| 5GB | 10.5GB \| 5GB * |
* Slim Orca `bsz=1` for all benchmarks since `bsz=2` OOMs. We can handle `bsz=2`, but we benchmark it with `bsz=1` for consistency.
</details>
![](https://i.ibb.co/sJ7RhGG/image-41.png)
<br>
## NVIDIA Pascal Support
Support for NVIDIA Pascal family of cards, specifically the P40 and P100.
### Setup Guide
1. Create three files (`Dockerfile`, `unsloth_env_file.yml`, and `docker-compose.yml`) with the contents provided below.
2. Ensure Docker and Docker Compose are installed on your system.
3. Install the NVIDIA Container Toolkit for GPU support if not already done.
4. Place all three files in the same directory.
5. Open a terminal and navigate to the directory containing these files.
6. Run the following command to build and start the container:
```
docker-compose up --build
```
7. Once the container is running, access Jupyter Lab by opening a web browser and navigating to `http://localhost:8888`.
### Configuration Files
#### 1. Dockerfile
```dockerfile
# Stage 1: Base image with system dependencies
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 as base
# Install system dependencies
RUN apt-get update && apt-get install -y \
build-essential \
git \
vim \
curl \
wget \
&& rm -rf /var/lib/apt/lists/*
# Install Miniconda only if it's not already installed
RUN if [ ! -d "/opt/conda" ]; then \
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh && \
bash miniconda.sh -b -p /opt/conda && \
rm miniconda.sh; \
fi
# Set path to conda
ENV PATH /opt/conda/bin:$PATH
# Set path to conda
ENV PATH /opt/conda/bin:$PATH
# Stage 2: Python environment setup
FROM base as python-env
COPY unsloth_env_file.yml unsloth_env_file.yml
RUN conda env create -f unsloth_env_file.yml
SHELL ["conda", "run", "-n", "unsloth_env", "/bin/bash", "-c"]
# Stage 3: Final image
FROM python-env as final
# Install Unsloth (This step is separate because it's likely to change more frequently)
RUN pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
ENV PATH /usr/local/cuda/bin:$PATH
ENV LD_LIBRARY_PATH /usr/local/cuda/lib64:$LD_LIBRARY_PATH
# Set the working directory
WORKDIR /workspace
# Set the default command to run Jupyter Lab
CMD ["conda", "run", "--no-capture-output", "-n", "unsloth_env", "jupyter", "lab", "--ip=0.0.0.0", "--no-browser", "--allow-root", "--NotebookApp.token=''", "--NotebookApp.password=''"]
```
#### 2. unsloth_env_file.yml
```yaml
name: unsloth_env
channels:
- xformers
- pytorch
- nvidia
- conda-forge
- defaults
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_gnu
- aiohttp=3.9.5=py310h5eee18b_0
- aiosignal=1.2.0=pyhd3eb1b0_0
- anyio=4.2.0=py310h06a4308_0
- argon2-cffi=21.3.0=pyhd3eb1b0_0
- argon2-cffi-bindings=21.2.0=py310h7f8727e_0
- arrow-cpp=16.1.0=hc1eb8f0_0
- async-lru=2.0.4=pyhd8ed1ab_0
- async-timeout=4.0.3=py310h06a4308_0
- attrs=23.1.0=py310h06a4308_0
- aws-c-auth=0.6.19=h5eee18b_0
- aws-c-cal=0.5.20=hdbd6064_0
- aws-c-common=0.8.5=h5eee18b_0
- aws-c-compression=0.2.16=h5eee18b_0
- aws-c-event-stream=0.2.15=h6a678d5_0
- aws-c-http=0.6.25=h5eee18b_0
- aws-c-io=0.13.10=h5eee18b_0
- aws-c-mqtt=0.7.13=h5eee18b_0
- aws-c-s3=0.1.51=hdbd6064_0
- aws-c-sdkutils=0.1.6=h5eee18b_0
- aws-checksums=0.1.13=h5eee18b_0
- aws-crt-cpp=0.18.16=h6a678d5_0
- aws-sdk-cpp=1.10.55=h721c034_0
- babel=2.14.0=pyhd8ed1ab_0
- beautifulsoup4=4.12.3=py310h06a4308_0
- blas=1.0=mkl
- bleach=4.1.0=pyhd3eb1b0_0
- boost-cpp=1.82.0=hdb19cb5_2
- bottleneck=1.3.7=py310ha9d4c09_0
- brotli-python=1.0.9=py310h6a678d5_8
- bzip2=1.0.8=h5eee18b_6
- c-ares=1.19.1=h5eee18b_0
- ca-certificates=2024.7.4=hbcca054_0
- certifi=2024.7.4=pyhd8ed1ab_0
- cffi=1.16.0=py310h5eee18b_1
- charset-normalizer=3.3.2=pyhd3eb1b0_0
- cuda-cudart=11.8.89=0
- cuda-cupti=11.8.87=0
- cuda-libraries=11.8.0=0
- cuda-nvrtc=11.8.89=0
- cuda-nvtx=11.8.86=0
- cuda-runtime=11.8.0=0
- cuda-version=11.8=hcce14f8_3
- cudatoolkit=11.8.0=h6a678d5_0
- datasets=2.19.1=py310h06a4308_0
- debugpy=1.6.7=py310h6a678d5_0
- decorator=5.1.1=pyhd3eb1b0_0
- defusedxml=0.7.1=pyhd3eb1b0_0
- dill=0.3.8=py310h06a4308_0
- entrypoints=0.4=py310h06a4308_0
- ffmpeg=4.3=hf484d3e_0
- filelock=3.13.1=py310h06a4308_0
- freetype=2.12.1=h4a9f257_0
- frozenlist=1.4.0=py310h5eee18b_0
- fsspec=2024.3.1=py310h06a4308_0
- gflags=2.2.2=h6a678d5_1
- glog=0.5.0=h6a678d5_1
- gmp=6.2.1=h295c915_3
- gmpy2=2.1.2=py310heeb90bb_0
- gnutls=3.6.15=he1e5248_0
- h11=0.14.0=pyhd8ed1ab_0
- h2=4.1.0=pyhd8ed1ab_0
- hpack=4.0.0=pyh9f0ad1d_0
- httpcore=1.0.5=pyhd8ed1ab_0
- httpx=0.27.0=pyhd8ed1ab_0
- hyperframe=6.0.1=pyhd8ed1ab_0
- icu=73.1=h6a678d5_0
- idna=3.7=py310h06a4308_0
- importlib-metadata=7.0.1=py310h06a4308_0
- importlib_metadata=7.0.1=hd8ed1ab_0
- importlib_resources=6.4.0=pyhd8ed1ab_0
- intel-openmp=2023.1.0=hdb19cb5_46306
- ipykernel=6.28.0=py310h06a4308_0
- ipython_genutils=0.2.0=pyhd3eb1b0_1
- jedi=0.19.1=py310h06a4308_0
- jinja2=3.1.4=py310h06a4308_0
- jpeg=9e=h5eee18b_2
- json5=0.9.25=pyhd8ed1ab_0
- jsonschema=4.19.2=py310h06a4308_0
- jsonschema-specifications=2023.7.1=py310h06a4308_0
- jupyter-lsp=2.2.5=pyhd8ed1ab_0
- jupyter_client=7.4.9=py310h06a4308_0
- jupyter_core=5.7.2=py310h06a4308_0
- jupyter_events=0.10.0=py310h06a4308_0
- jupyter_server=2.14.1=py310h06a4308_0
- jupyter_server_terminals=0.4.4=py310h06a4308_1
- jupyterlab=4.2.4=pyhd8ed1ab_0
- jupyterlab_pygments=0.3.0=pyhd8ed1ab_1
- jupyterlab_server=2.27.3=pyhd8ed1ab_0
- krb5=1.20.1=h143b758_1
- lame=3.100=h7b6447c_0
- lcms2=2.12=h3be6417_0
- ld_impl_linux-64=2.38=h1181459_1
- lerc=3.0=h295c915_0
- libabseil=20240116.2=cxx17_h6a678d5_0
- libboost=1.82.0=h109eef0_2
- libbrotlicommon=1.0.9=h5eee18b_8
- libbrotlidec=1.0.9=h5eee18b_8
- libbrotlienc=1.0.9=h5eee18b_8
- libcublas=11.11.3.6=0
- libcufft=10.9.0.58=0
- libcufile=1.9.1.3=0
- libcurand=10.3.5.147=0
- libcurl=8.7.1=h251f7ec_0
- libcusolver=11.4.1.48=0
- libcusparse=11.7.5.86=0
- libdeflate=1.17=h5eee18b_1
- libedit=3.1.20230828=h5eee18b_0
- libev=4.33=h7f8727e_1
- libevent=2.1.12=hdbd6064_1
- libffi=3.4.4=h6a678d5_1
- libgcc-ng=14.1.0=h77fa898_0
- libgomp=14.1.0=h77fa898_0
- libgrpc=1.62.2=h2d74bed_0
- libiconv=1.16=h5eee18b_3
- libidn2=2.3.4=h5eee18b_0
- libjpeg-turbo=2.0.0=h9bf148f_0
- libnghttp2=1.57.0=h2d74bed_0
- libnpp=11.8.0.86=0
- libnvjpeg=11.9.0.86=0
- libpng=1.6.39=h5eee18b_0
- libprotobuf=4.25.3=he621ea3_0
- libsodium=1.0.18=h7b6447c_0
- libssh2=1.11.0=h251f7ec_0
- libstdcxx-ng=11.2.0=h1234567_1
- libtasn1=4.19.0=h5eee18b_0
- libthrift=0.15.0=h1795dd8_2
- libtiff=4.5.1=h6a678d5_0
- libunistring=0.9.10=h27cfd23_0
- libuuid=1.41.5=h5eee18b_0
- libwebp-base=1.3.2=h5eee18b_0
- llvm-openmp=14.0.6=h9e868ea_0
- lz4-c=1.9.4=h6a678d5_1
- markupsafe=2.1.3=py310h5eee18b_0
- mistune=2.0.4=py310h06a4308_0
- mkl=2023.1.0=h213fc3f_46344
- mkl-service=2.4.0=py310h5eee18b_1
- mkl_fft=1.3.8=py310h5eee18b_0
- mkl_random=1.2.4=py310hdb19cb5_0
- mpc=1.1.0=h10f8cd9_1
- mpfr=4.0.2=hb69a4c5_1
- mpmath=1.3.0=py310h06a4308_0
- multidict=6.0.4=py310h5eee18b_0
- multiprocess=0.70.15=py310h06a4308_0
- nb_conda_kernels=2.3.1=py310h06a4308_0
- nbclassic=1.1.0=py310h06a4308_0
- nbclient=0.8.0=py310h06a4308_0
- nbconvert=7.10.0=py310h06a4308_0
- nbformat=5.9.2=py310h06a4308_0
- ncurses=6.4=h6a678d5_0
- nest-asyncio=1.6.0=py310h06a4308_0
- nettle=3.7.3=hbbd107a_1
- networkx=3.3=py310h06a4308_0
- notebook=6.5.7=py310h06a4308_0
- notebook-shim=0.2.3=py310h06a4308_0
- numexpr=2.8.7=py310h85018f9_0
- numpy=1.26.4=py310h5f9d8c6_0
- numpy-base=1.26.4=py310hb5e798b_0
- openh264=2.1.1=h4ff587b_0
- openjpeg=2.4.0=h9ca470c_2
- openssl=3.3.1=h4bc722e_2
- orc=2.0.1=h2d29ad5_0
- overrides=7.4.0=py310h06a4308_0
- packaging=24.1=py310h06a4308_0
- pandas=2.2.2=py310h6a678d5_0
- pandocfilters=1.5.0=pyhd3eb1b0_0
- pillow=10.4.0=py310h5eee18b_0
- pip=24.0=py310h06a4308_0
- platformdirs=3.10.0=py310h06a4308_0
- prometheus_client=0.14.1=py310h06a4308_0
- prompt_toolkit=3.0.43=hd3eb1b0_0
- psutil=5.9.0=py310h5eee18b_0
- ptyprocess=0.7.0=pyhd3eb1b0_2
- pure_eval=0.2.2=pyhd3eb1b0_0
- pyarrow=16.1.0=py310h1128e8f_0
- pycparser=2.21=pyhd3eb1b0_0
- pysocks=1.7.1=py310h06a4308_0
- python=3.10.14=h955ad1f_1
- python-dateutil=2.9.0post0=py310h06a4308_2
- python-fastjsonschema=2.16.2=py310h06a4308_0
- python-json-logger=2.0.7=py310h06a4308_0
- python-tzdata=2023.3=pyhd3eb1b0_0
- python-xxhash=2.0.2=py310h5eee18b_1
- pytorch=2.1.0=py3.10_cuda11.8_cudnn8.7.0_0
- pytorch-cuda=11.8=h7e8668a_5
- pytorch-mutex=1.0=cuda
- pytz=2024.1=py310h06a4308_0
- pyyaml=6.0.1=py310h5eee18b_0
- pyzmq=24.0.1=py310h5eee18b_0
- re2=2022.04.01=h295c915_0
- readline=8.2=h5eee18b_0
- referencing=0.30.2=py310h06a4308_0
- regex=2023.10.3=py310h5eee18b_0
- requests=2.32.3=py310h06a4308_0
- rfc3339-validator=0.1.4=py310h06a4308_0
- rfc3986-validator=0.1.1=py310h06a4308_0
- rpds-py=0.10.6=py310hb02cf49_0
- s2n=1.3.27=hdbd6064_0
- safetensors=0.4.2=py310ha89cbab_1
- send2trash=1.8.2=py310h06a4308_0
- setuptools=69.5.1=py310h06a4308_0
- six=1.16.0=pyhd3eb1b0_1
- snappy=1.1.10=h6a678d5_1
- sniffio=1.3.0=py310h06a4308_0
- soupsieve=2.5=py310h06a4308_0
- sqlite=3.45.3=h5eee18b_0
- stack_data=0.2.0=pyhd3eb1b0_0
- sympy=1.12=py310h06a4308_0
- tbb=2021.8.0=hdb19cb5_0
- terminado=0.17.1=py310h06a4308_0
- tinycss2=1.2.1=py310h06a4308_0
- tk=8.6.14=h39e8969_0
- tokenizers=0.19.1=py310hff361bb_0
- tomli=2.0.1=pyhd8ed1ab_0
- torchaudio=2.1.0=py310_cu118
- torchtriton=2.1.0=py310
- torchvision=0.16.0=py310_cu118
- tornado=6.4.1=py310h5eee18b_0
- tqdm=4.66.4=py310h2f386ee_0
- traitlets=5.14.3=py310h06a4308_0
- typing-extensions=4.11.0=py310h06a4308_0
- typing_extensions=4.11.0=py310h06a4308_0
- tzdata=2024a=h04d1e81_0
- urllib3=2.2.2=py310h06a4308_0
- utf8proc=2.6.1=h5eee18b_1
- webencodings=0.5.1=py310h06a4308_1
- websocket-client=1.8.0=py310h06a4308_0
- wheel=0.43.0=py310h06a4308_0
- xformers=0.0.22.post7=py310_cu11.8.0_pyt2.1.0
- xxhash=0.8.0=h7f8727e_3
- xz=5.4.6=h5eee18b_1
- yaml=0.2.5=h7b6447c_0
- yarl=1.9.3=py310h5eee18b_0
- zeromq=4.3.5=h6a678d5_0
- zipp=3.17.0=py310h06a4308_0
- zlib=1.2.13=h5eee18b_1
- zstd=1.5.5=hc292b87_2
- pip:
- accelerate==0.33.0
- asttokens==2.4.1
- bitsandbytes==0.43.2
- comm==0.2.2
- docstring-parser==0.16
- exceptiongroup==1.2.2
- executing==2.0.1
- gguf==0.9.1
- hf-transfer==0.1.8
- huggingface-hub==0.24.2
- iprogress==0.4
- ipython==8.26.0
- ipywidgets==8.1.3
- jupyterlab-widgets==3.0.11
- markdown-it-py==3.0.0
- matplotlib-inline==0.1.7
- mdurl==0.1.2
- parso==0.8.4
- peft==0.12.0
- pexpect==4.9.0
- prompt-toolkit==3.0.47
- protobuf==3.20.3
- pure-eval==0.2.3
- pygments==2.18.0
- rich==13.7.1
- sentencepiece==0.2.0
- shtab==1.7.1
- stack-data==0.6.3
- transformers==4.43.3
- trl==0.8.6
- tyro==0.8.5
- wcwidth==0.2.13
- widgetsnbextension==4.0.11
```
#### 3. docker-compose.yml
```yaml
version: '3.8'
services:
unsloth-env:
environment:
- NVIDIA_VISIBLE_DEVICES=all
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]
volumes:
- ./cache:/root/.cache
- ./workspace:/workspace
working_dir: /workspace
ports:
- "8888:8888" # For Jupyter Lab
tty: true
stdin_open: true
build:
context: .
dockerfile: Dockerfile
```
### Thank You to
- [HuyNguyen-hust](https://github.com/HuyNguyen-hust) for making [RoPE Embeddings 28% faster](https://github.com/unslothai/unsloth/pull/238)
- [RandomInternetPreson](https://github.com/RandomInternetPreson) for confirming WSL support
- [152334H](https://github.com/152334H) for experimental DPO support
- [atgctg](https://github.com/atgctg) for syntax highlighting
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