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Unverified Commit 6d41c3f2 authored by Wenhao Chen's avatar Wenhao Chen Committed by GitHub
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[doc] update Coati README (#4405) 

* style: apply formatter

* fix: add outdated warnings

* docs: add dataset format and polish

* docs: polish README

* fix: fix json format

* fix: fix typos

* revert: revert 7b example
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applications/Chat/README.md
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......@@ -4,7 +4,6 @@
<span>ColossalChat</span>
</h1>
## Table of Contents
- [Table of Contents](#table-of-contents)
......@@ -34,7 +33,9 @@
- [Authors](#authors)
- [Citations](#citations)
- [Licenses](#licenses)
---
## What is ColossalChat and Coati ?
[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) is the project to implement LLM with RLHF, powered by the [Colossal-AI](https://github.com/hpcaitech/ColossalAI) project.
......@@ -42,6 +43,7 @@
Coati stands for `ColossalAI Talking Intelligence`. It is the name for the module implemented in this project and is also the name of the large language model developed by the ColossalChat project.
The Coati package provides a unified large language model framework that has implemented the following functions
- Supports comprehensive large-model training acceleration capabilities for ColossalAI, without requiring knowledge of complex distributed training algorithms
- Supervised datasets collection
- Supervised instructions fine-tuning
......@@ -56,17 +58,19 @@ The Coati package provides a unified large language model framework that has imp
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/chatgpt.png" width=700/>
</p>
Image source: https://openai.com/blog/chatgpt
Image source: https://openai.com/blog/chatgpt
</div>
**As Colossal-AI is undergoing some major updates, this project will be actively maintained to stay in line with the Colossal-AI project.**
More details can be found in the latest news.
* [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b)
* [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt)
- [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b)
- [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt)
## Online demo
<div align="center">
<a href="https://www.youtube.com/watch?v=HcTiHzApHm0">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20YouTube.png" width="700" />
......@@ -83,13 +87,13 @@ More details can be found in the latest news.
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20Speed.jpg" width=450/>
</p>
> DeepSpeedChat performance comes from its blog on 2023 April 12, ColossalChat performance can be reproduced on an AWS p4d.24xlarge node with 8 A100-40G GPUs with the following command: torchrun --standalone --nproc_per_node 8 benchmark_opt_lora_dummy.py --num_collect_steps 1 --use_kernels --strategy colossalai_zero2 --experience_batch_size 64 --train_batch_size 32
> DeepSpeedChat performance comes from its blog on 2023 April 12, ColossalChat performance can be reproduced on an AWS p4d.24xlarge node with 8 A100-40G GPUs with the following command: `torchrun --standalone --nproc_per_node 8 benchmark_opt_lora_dummy.py --num_collect_steps 1 --use_kernels --strategy colossalai_zero2 --experience_batch_size 64 --train_batch_size 32`
## Install
### Install the environment
```shell
```bash
conda create -n coati
conda activate coati
git clone https://github.com/hpcaitech/ColossalAI.git
......@@ -99,7 +103,7 @@ pip install .
### Install the Transformers
```shell
```bash
pip install transformers==4.30.2
```
......@@ -107,10 +111,11 @@ pip install transformers==4.30.2
### Supervised datasets collection
we collected 104K bilingual datasets of Chinese and English, and you can find the datasets in this repo
[InstructionWild](https://github.com/XueFuzhao/InstructionWild)
We collected 104K bilingual datasets of Chinese and English, and you can find the datasets in this repo
[InstructionWild](https://github.com/XueFuzhao/InstructionWild) and in this [file](https://github.com/XueFuzhao/InstructionWild/blob/main/data/README.md).
Here is how we collected the data
<p align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/data-collect.png" width=500/>
</p>
......@@ -122,6 +127,20 @@ Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned ea
You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning.
[[Stage1 tutorial video]](https://www.youtube.com/watch?v=-qFBZFmOJfg)
**Note**: the supervised dataset follows the following format,
```json
[
{
"instruction": "Provide a list of the top 10 most popular mobile games in Asia",
"input": "",
"output": "The top 10 most popular mobile games in Asia are:\n1) PUBG Mobile\n2) Pokemon Go\n3) Candy Crush Saga\n4) Free Fire\n5) Clash of Clans\n6) Mario Kart Tour\n7) Arena of Valor\n8) Fantasy Westward Journey\n9) Subway Surfers\n10) ARK Survival Evolved",
"id": 0
},
...
]
```
### RLHF Training Stage2 - Training reward model
Stage2 trains a reward model, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model
......@@ -140,13 +159,46 @@ Stage3 uses reinforcement learning algorithm, which is the most complex part of
You can run the `examples/train_prompts.sh` to start training PPO with human feedback.
[[Stage3 tutorial video]](https://www.youtube.com/watch?v=Z8wwSHxPL9g)
**Note**: the required datasets follow the following format,
- `pretrain dataset`
```json
[
{
"instruction": "Provide a list of the top 10 most popular mobile games in Asia",
"input": "",
"output": "The top 10 most popular mobile games in Asia are:\n1) PUBG Mobile\n2) Pokemon Go\n3) Candy Crush Saga\n4) Free Fire\n5) Clash of Clans\n6) Mario Kart Tour\n7) Arena of Valor\n8) Fantasy Westward Journey\n9) Subway Surfers\n10) ARK Survival Evolved",
"id": 0
},
...
]
```
- `prompt dataset`
```json
[
{
"instruction": "Edit this paragraph to make it more concise: \"Yesterday, I went to the store and bought some things. Then, I came home and put them away. After that, I went for a walk and met some friends.\"",
"id": 0
},
{
"instruction": "Write a descriptive paragraph about a memorable vacation you went on",
"id": 1
},
...
]
```
For more details, see [`examples/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples).
### Inference Quantization and Serving - After Training
We provide an online inference server and a benchmark. We aim to run inference on single GPU, so quantization is essential when using large models.
We support 8-bit quantization (RTN), 4-bit quantization (GPTQ), and FP16 inference. You can
We support 8-bit quantization (RTN), 4-bit quantization (GPTQ), and FP16 inference.
Online inference server scripts can help you deploy your own services.
For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference).
......@@ -158,6 +210,7 @@ For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tre
<details><summary><b>E-mail</b></summary>
![phd](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/Phd.png)
</details>
<details><summary><b>coding</b></summary>
......@@ -191,6 +244,7 @@ For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tre
</details>
### Open QA
<details><summary><b>Game</b></summary>
![Game](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/game.png)
......@@ -224,6 +278,7 @@ For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tre
You can find more examples in this [repo](https://github.com/XueFuzhao/InstructionWild/blob/main/comparison.md).
### Limitation
<details><summary><b>Limitation for LLaMA-finetuned models</b></summary>
- Both Alpaca and ColossalChat are based on LLaMA. It is hard to compensate for the missing knowledge in the pre-training stage.
- Lack of counting ability: Cannot count the number of items in a list.
......@@ -247,7 +302,7 @@ You can find more examples in this [repo](https://github.com/XueFuzhao/Instructi
We have integrated the Transformers save and load pipeline, allowing users to freely call Hugging Face's language models and save them in the HF format.
```
```python
from coati.models.llama import LlamaLM
from coati.trainer import SFTTrainer
......@@ -256,20 +311,20 @@ tokenizer = AutoTokenizer.from_pretrained(args.pretrain)
(model, optim) = strategy.prepare((model, optim))
trainer = SFTTrainer(model=model,
strategy=strategy,
optim=optim,
train_dataloader=train_dataloader,
eval_dataloader=eval_dataloader,
batch_size=args.batch_size,
max_epochs=args.max_epochs,
accumulation_steps = args.accumulation_steps
)
strategy=strategy,
optim=optim,
train_dataloader=train_dataloader,
eval_dataloader=eval_dataloader,
batch_size=args.batch_size,
max_epochs=args.max_epochs,
accumulation_steps=args.accumulation_steps
)
trainer.fit()
# this saves in pytorch format
strategy.save_model(model, args.save_path, only_rank0=True)
# this saves in HF format. ColossalAI strategy with stage-3 doesn't support this method
# this saves in HF format
strategy.save_pretrained(model, args.save_path, only_rank0=True, tokenizer=tokenizer)
```
......@@ -280,12 +335,13 @@ strategy.save_pretrained(model, args.save_path, only_rank0=True, tokenizer=token
Here are some examples that can allow you to train a 7B model on a single or multiple consumer-grade GPUs.
If you only have a single 24G GPU, you can use the following script. `batch_size`, `lora_rank` and `grad_checkpoint` are the most important parameters to successfully train the model.
```
```bash
// [INFO]: MAX GPU MEMORY ALLOCATED: 19148.9345703125 MB
torchrun --standalone --nproc_per_node=1 train_sft.py \
--pretrain "/path/to/LLaMa-7B/" \
--model 'llama' \
--strategy ddp \
--log_interval 10 \
--save_path /path/to/Coati-7B \
--dataset /path/to/data.json \
--batch_size 1 \
......@@ -298,12 +354,12 @@ torchrun --standalone --nproc_per_node=1 train_sft.py \
```
`colossalai_gemini` strategy can enable a single 24G GPU to train the whole model without using LoRA if you have sufficient CPU memory. You can use the following script.
```
```bash
torchrun --standalone --nproc_per_node=1 train_sft.py \
--pretrain "/path/to/LLaMa-7B/" \
--model 'llama' \
--strategy colossalai_gemini \
--log_interval 10 \
--save_path /path/to/Coati-7B \
--dataset /path/to/data.json \
--batch_size 1 \
......@@ -315,12 +371,12 @@ torchrun --standalone --nproc_per_node=1 train_sft.py \
```
If you have 4x32 GB GPUs, you can even train the whole 7B model using our `colossalai_zero2_cpu` strategy! The script is given as follows.
```
```bash
torchrun --standalone --nproc_per_node=4 train_sft.py \
--pretrain "/path/to/LLaMa-7B/" \
--model 'llama' \
--strategy colossalai_zero2_cpu \
--log_interval 10 \
--save_path /path/to/Coati-7B \
--dataset /path/to/data.json \
--batch_size 1 \
......@@ -330,8 +386,8 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \
--max_epochs 1 \
--grad_checkpoint
```
</details>
</details>
## The Plan
......@@ -346,24 +402,26 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \
- [ ] support chain-of-thought by [langchain](https://github.com/hwchase17/langchain)
### Real-time progress
You will find our progress in github project broad
[Coati](https://github.com/orgs/hpcaitech/projects/17/views/1)
You will find our progress in github [project broad](https://github.com/orgs/hpcaitech/projects/17/views/1).
## Invitation to open-source contribution
Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models from the starting point of replicating ChatGPT!
You may contact us or participate in the following ways:
1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks!
2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md).
3. Join the Colossal-AI community on
[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w),
and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas.
[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w),
and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas.
4. Send your official proposal to email contact@hpcaitech.com
Thanks so much to all of our amazing contributors!
## Quick Preview
<div align="center">
<a href="https://chat.colossalai.org/">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Chat-demo.png" width="700" />
......@@ -397,18 +455,22 @@ Thanks so much to all of our amazing contributors!
| Better Cases | 38 ⚔ **41** | **45** ⚔ 33 |
| Win Rate | 48% ⚔ **52%** | **58%** ⚔ 42% |
| Average Score | 7.06 ⚔ **7.13** | **7.31** ⚔ 6.82 |
- Our Coati-7B model performs better than Alpaca-7B when using GPT-4 to evaluate model performance. The Coati-7B model we evaluate is an old version we trained a few weeks ago and the new version is around the corner.
## Authors
Coati is developed by ColossalAI Team:
- [Fazzie](https://fazzie-key.cool/about/index.html)
- [FrankLeeeee](https://github.com/FrankLeeeee)
- [BlueRum](https://github.com/ht-zhou)
- [ver217](https://github.com/ver217)
- [ofey404](https://github.com/ofey404)
- [Wenhao Chen](https://github.com/CWHer)
The Phd student from [(HPC-AI) Lab](https://ai.comp.nus.edu.sg/) also contributed a lot to this project.
- [Zangwei Zheng](https://github.com/zhengzangw)
- [Xue Fuzhao](https://github.com/XueFuzhao)
......
applications/Chat/benchmarks/README.md
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......@@ -27,9 +27,12 @@ We also provide various training strategies:
We only support `torchrun` to launch now. E.g.
```shell
```bash
# run OPT-125M with no lora (lora_rank=0) on single-node single-GPU with min batch size
torchrun --standalone --nproc_per_node 1 benchmark_opt_lora_dummy.py --model 125m --critic_model 125m --strategy ddp --experience_batch_size 1 --train_batch_size 1 --lora_rank 0
torchrun --standalone --nproc_per_node 1 benchmark_opt_lora_dummy.py \
--model 125m --critic_model 125m --strategy ddp \
--experience_batch_size 1 --train_batch_size 1 --lora_rank 0
# run Actor (OPT-1.3B) and Critic (OPT-350M) with lora_rank=4 on single-node 4-GPU
torchrun --standalone --nproc_per_node 4 benchmark_opt_lora_dummy.py --model 1.3b --critic_model 350m --strategy colossalai_zero2 --lora_rank 4
torchrun --standalone --nproc_per_node 4 benchmark_opt_lora_dummy.py \
--model 1.3b --critic_model 350m --strategy colossalai_zero2 --lora_rank 4
```
applications/Chat/coati/ray/README.md
View file @ 6d41c3f2
:warning: **This content may be outdated since the major update of Colossal Chat. We will update this content soon.**
# Distributed PPO Training on Stage 3
## Detach Experience Makers and Trainers
......@@ -26,124 +28,137 @@ See examples at `ColossalAI/application/Chat/examples/ray`
- define makers' environment variables :
```python
env_info_makers = [{
'local_rank': '0',
'rank': str(rank),
'world_size': str(num_makers),
'master_port': maker_port,
'master_addr': master_addr
} for rank in range(num_makers)]
```python
env_info_makers = [{
'local_rank': '0',
'rank': str(rank),
'world_size': str(num_makers),
'master_port': maker_port,
'master_addr': master_addr
} for rank in range(num_makers)]
```
```
- define maker models :
```python
def model_fn():
actor = get_actor_from_args(...)
critic = get_critic_from_args(...)
reward_model = get_reward_model_from_args(...)
initial_model = get_actor_from_args(...)
return actor, critic, reward_model, initial_model
```
```python
def model_fn():
actor = get_actor_from_args(...)
critic = get_critic_from_args(...)
reward_model = get_reward_model_from_args(...)
initial_model = get_actor_from_args(...)
return actor, critic, reward_model, initial_model
```
- set experience_holder_refs :
```python
experience_holder_refs = [
ExperienceMakerHolder.options(
name=f"maker_{i}",
num_gpus=1,
max_concurrency=2
).remote(
detached_trainer_name_list=[f"trainer_{x}" for x in target_trainers(...)],
model_fn=model_fn,
...)
for i, env_info_maker in enumerate(env_info_makers)
]
```
The names in the `detached_trainer_name_list` refer to the target trainers that the maker should send experience to.
We set a trainer's name the same as a maker, by `.options(name="str")`. See below.
```python
experience_holder_refs = [
ExperienceMakerHolder.options(
name=f"maker_{i}",
num_gpus=1,
max_concurrency=2
).remote(
detached_trainer_name_list=[f"trainer_{x}" for x in target_trainers(...)],
model_fn=model_fn,
...)
for i, env_info_maker in enumerate(env_info_makers)
]
```
The names in the `detached_trainer_name_list` refer to the target trainers that the maker should send experience to.
We set a trainer's name the same as a maker, by `.options(name="str")`. See below.
### Setup Trainers
- define trainers' environment variables :
```python
env_info_trainers = [{
'local_rank': '0',
'rank': str(rank),
'world_size': str(num_trainers),
'master_port': trainer_port,
'master_addr': master_addr
} for rank in range(num_trainers)]
```
```python
env_info_trainers = [{
'local_rank': '0',
'rank': str(rank),
'world_size': str(num_trainers),
'master_port': trainer_port,
'master_addr': master_addr
} for rank in range(num_trainers)]
```
- define trainer models :
```python
def trainer_model_fn():
actor = get_actor_from_args(...)
critic = get_critic_from_args(...)
return actor, critic
```
```python
def trainer_model_fn():
actor = get_actor_from_args(...)
critic = get_critic_from_args(...)
return actor, critic
```
- set trainer_refs :
```python
trainer_refs = [
DetachedPPOTrainer.options(
name=f"trainer{i}",
num_gpus=1,
max_concurrency=2
).remote(
experience_maker_holder_name_list=[f"maker{x}" for x in target_makers(...)],
model_fn = trainer_model_fn(),
...)
for i, env_info_trainer in enumerate(env_info_trainers)
]
```
The names in `experience_maker_holder_name_list` refer to the target makers that the trainer should send updated models to.
By setting `detached_trainer_name_list` and `experience_maker_holder_name_list`, we can customize the transmission graph.
```python
trainer_refs = [
DetachedPPOTrainer.options(
name=f"trainer{i}",
num_gpus=1,
max_concurrency=2
).remote(
experience_maker_holder_name_list=[f"maker{x}" for x in target_makers(...)],
model_fn = trainer_model_fn(),
...)
for i, env_info_trainer in enumerate(env_info_trainers)
]
```
The names in `experience_maker_holder_name_list` refer to the target makers that the trainer should send updated models to.
By setting `detached_trainer_name_list` and `experience_maker_holder_name_list`, we can customize the transmission graph.
### Launch Jobs
- define data_loader :
```python
def data_loader_fn():
return = torch.utils.data.DataLoader(dataset=dataset)
```
```python
def data_loader_fn():
return = torch.utils.data.DataLoader(dataset=dataset)
```
- launch makers :
```python
wait_tasks = []
for experience_holder_ref in experience_holder_refs:
wait_tasks.append(
experience_holder_ref.workingloop.remote(data_loader_fn(),
num_steps=experience_steps))
```
```python
wait_tasks = []
for experience_holder_ref in experience_holder_refs:
wait_tasks.append(
experience_holder_ref.workingloop.remote(data_loader_fn(),
num_steps=experience_steps))
```
- launch trainers :
```python
for trainer_ref in trainer_refs:
wait_tasks.append(trainer_ref.fit.remote(total_steps, update_steps, train_epochs))
```
```python
for trainer_ref in trainer_refs:
wait_tasks.append(trainer_ref.fit.remote(total_steps, update_steps, train_epochs))
```
- wait for done :
```python
ray.get(wait_tasks)
```
```python
ray.get(wait_tasks)
```
## Flexible Structure
We can deploy different strategies to makers and trainers. Here are some notions.
### 2 Makers 1 Trainer
<p align="center">
<img src="https://github.com/hpcaitech/public_assets/blob/main/applications/chat/2m1t.png?raw=true" width=600/>
</p>
### 2 Makers 2 Trainer
<p align="center">
<img src="https://github.com/hpcaitech/public_assets/blob/main/applications/chat/2m2t.png?raw=true" width=600/>
</p>
### Maker Inference Quantization
<p align="center">
<img src="https://github.com/hpcaitech/public_assets/blob/main/applications/chat/2m2t_quantize.png?raw=true" width=600/>
</p>
......
applications/Chat/evaluate/README.md
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This diff is collapsed.
applications/Chat/examples/README.md
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......@@ -17,7 +17,7 @@
- [Arg List](#arg-list-2)
- [Inference example - After Stage3](#inference-example---after-stage3)
- [Attention](#attention)
- [data](#data)
- [data](#data)
- [Support Model](#support-model)
- [GPT](#gpt)
- [BLOOM](#bloom)
......@@ -28,8 +28,8 @@
- [Reward model](#reward-model)
- [Critic model](#critic-model)
---
## Install requirements
```shell
......@@ -38,10 +38,11 @@ pip install -r requirements.txt
## Supervised datasets collection
We collected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo
[InstructionWild](https://github.com/XueFuzhao/InstructionWild).
We collected 104K bilingual datasets of Chinese and English, and you can find the datasets in this repo
[InstructionWild](https://github.com/XueFuzhao/InstructionWild) and in this [file](https://github.com/XueFuzhao/InstructionWild/blob/main/data/README.md).
Here is how we collected the data
The following pic shows how we collected the data.
<p align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/data-collect.png" width=500/>
</p>
......@@ -52,38 +53,40 @@ In order to further improve the model's ability to handle multi-turn conversatio
A sample of conversation dataset should have the following fields:
* `type` (str, optional): The type of the data sample.
* `language` (str, optional): The language of the data sample.
* `dataset` (str, optional): The dataset the data sample originates from.
* `conversations` (str, compulsory): Conversation content of the data sample.
* `id` (int, optional): The ID of the data sample.
- `type` (str, optional): The type of the data sample.
- `language` (str, optional): The language of the data sample.
- `dataset` (str, optional): The dataset the data sample originates from.
- `conversations` (str, compulsory): Conversation content of the data sample.
- `id` (int, optional): The ID of the data sample.
A simple example:
```json
{
"type": "instruction",
"language": "English",
"dataset": "Alpaca",
"conversations": [
{
"from": "human",
"value": "Give three tips for staying healthy."
},
{
"from": "gpt",
"value": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."
}
],
"id": 1
"type": "instruction",
"language": "English",
"dataset": "Alpaca",
"conversations": [
{
"from": "human",
"value": "Give three tips for staying healthy."
},
{
"from": "gpt",
"value": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."
}
],
"id": 1
}
```
> **NOTE:** Only key `conversations` is compulsary for training and other keys serve as metadata. The length of `conversations` varies.
> **NOTE:** Only key `conversations` is compulsary for training and other keys serve as metadata. The length of `conversations` varies.
You can run the `examples/generate_conversation_dataset.py` to generate a conversation dataset supported by ColossalChat.
You can use the following cmd to generate conversation dataset.
```
```bash
python generate_conversation_dataset.py \
--dataset "All"
--save_path "/path/to/dataset"
......@@ -97,12 +100,12 @@ Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned ea
You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning.
You can also use the following cmd to start a supervised instructs fine-tuning with your own settings.
```
```bash
torchrun --standalone --nproc_per_node=4 train_sft.py \
--pretrain "/path/to/LLaMa-7B/" \
--model 'llama' \
--strategy colossalai_zero2 \
--log_interval 10 \
--save_path /path/to/Coati-7B \
--dataset /path/to/data.json \
--batch_size 4 \
......@@ -112,18 +115,33 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \
--max_epochs 1 \
--grad_checkpoint
```
**Note**: the supervised dataset follows the following format,
```json
[
{
"instruction": "Provide a list of the top 10 most popular mobile games in Asia",
"input": "",
"output": "The top 10 most popular mobile games in Asia are:\n1) PUBG Mobile\n2) Pokemon Go\n3) Candy Crush Saga\n4) Free Fire\n5) Clash of Clans\n6) Mario Kart Tour\n7) Arena of Valor\n8) Fantasy Westward Journey\n9) Subway Surfers\n10) ARK Survival Evolved",
"id": 0
},
...
]
```
### Arg List
- --strategy: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2'
- --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
- --pretrain: pretrain model, type=str, default=None
- --max_datasets_size: the max size of dataset, type=int, default=None
- --save_path: path to save the model, type=str, default='output'
- --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False
- --max_epochs: max epochs for training, type=int, default=3
- --batch_size: batch size while training, type=int, default=4
- --lora_rank: low-rank adaptation matrices rank, type=int, default=0
- --log_interval: how many steps to log, type=int, default=100
- --grad_checkpoint: enable gradient checkpointing, type=bool, default=False
- `--strategy`: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2'
- `--model`: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
- `--pretrain`: pretrain model, type=str, default=None
- `--max_datasets_size`: the max size of dataset, type=int, default=None
- `--save_path`: path to save the model, type=str, default='output'
- `--need_optim_ckpt`: whether to save optim ckpt, type=bool, default=False
- `--max_epochs`: max epochs for training, type=int, default=3
- `--batch_size`: batch size while training, type=int, default=4
- `--lora_rank`: low-rank adaptation matrices rank, type=int, default=0
- `--grad_checkpoint`: enable gradient checkpointing, type=bool, default=False
## Stage2 - Training reward model
......@@ -133,7 +151,8 @@ We train a reward model in stage 2, which obtains corresponding scores by manual
You can run the `examples/train_rm.sh` to start a reward model training.
You can also use the following cmd to start training a reward model.
```
```bash
torchrun --standalone --nproc_per_node=4 train_reward_model.py \
--pretrain "/path/to/LLaMa-7B/" \
--model 'llama' \
......@@ -141,16 +160,19 @@ torchrun --standalone --nproc_per_node=4 train_reward_model.py \
--loss_fn 'log_exp'\
--save_path 'rmstatic.pt' \
```
### Features and tricks in RM training
- We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)and[rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets.
- We support 2 kinds of loss_function named 'log_sig'(used by OpenAI) and 'log_exp'(used by Anthropic).
- We change the loss to valid_acc and pair_dist to monitor progress during training.
- We support 2 kinds of loss function named `log_sig`(used by OpenAI) and `log_exp`(used by Anthropic).
- We change the loss to `valid_acc` and `pair_dist` to monitor progress during training.
- We add special token to the end of the sequence to get better result.
- We use cosine-reducing lr-scheduler for RM training.
- We set value_head as 1 liner layer and initialize the weight of value_head using N(0，1/(d_model + 1)) distribution.
- We train a Bloom-560m reward model for 1 epoch and find the test acc of the model achieve the performance mentions in [Anthropics paper](https://arxiv.org/abs/2204.05862).
### Experiment result
Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862):
<div align=middle> <img width="512" alt="image" src="https://user-images.githubusercontent.com/70618399/225263321-8d64c3a8-6877-4cc8-9b61-0e1c52d3d94f.png">
......@@ -162,20 +184,20 @@ Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862):
<div align=left>We also train the reward model based on LLaMA-7B, which reaches the ACC of 72.06% after 1 epoch, performing almost the same as Anthropic's best RM.
### Arg List
- --strategy: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2'
- --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
- --pretrain: pretrain model, type=str, default=None
- --model_path: the path of rm model(if continue to train), type=str, default=None
- --save_path: path to save the model, type=str, default='output'
- --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False
- --max_epochs: max epochs for training, type=int, default=3
- --dataset: dataset name, type=str, choices=['Anthropic/hh-rlhf', 'Dahoas/rm-static']
- --subset: subset of the dataset, type=str, default=None
- --batch_size: batch size while training, type=int, default=4
- --lora_rank: low-rank adaptation matrices rank, type=int, default=0
- --loss_func: which kind of loss function, choices=['log_sig', 'log_exp']
- --max_len: max sentence length for generation, type=int, default=512
- --test: whether is only testing, if it's true, the dataset will be small
- `--strategy`: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2'
- `--model`: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
- `--pretrain`: pretrain model, type=str, default=None
- `--model_path`: the path of rm model(if continue to train), type=str, default=None
- `--save_path`: path to save the model, type=str, default='output'
- `--need_optim_ckpt`: whether to save optim ckpt, type=bool, default=False
- `--max_epochs`: max epochs for training, type=int, default=3
- `--dataset`: dataset name, type=str, choices=['Anthropic/hh-rlhf', 'Dahoas/rm-static']
- `--subset`: subset of the dataset, type=str, default=None
- `--batch_size`: batch size while training, type=int, default=4
- `--lora_rank`: low-rank adaptation matrices rank, type=int, default=0
- `--loss_func`: which kind of loss function, choices=['log_sig', 'log_exp']
- `--max_len`: max sentence length for generation, type=int, default=512
## Stage3 - Training model using prompts with RL
......@@ -186,53 +208,89 @@ Stage3 uses reinforcement learning algorithm, which is the most complex part of
</p>
You can run the `examples/train_prompts.sh` to start PPO training.
You can also use the cmd following to start PPO training.
[[Stage3 tutorial video]](https://www.youtube.com/watch?v=Z8wwSHxPL9g)
```
```bash
torchrun --standalone --nproc_per_node=4 train_prompts.py \
--pretrain "/path/to/LLaMa-7B/" \
--model 'llama' \
--strategy colossalai_zero2 \
--prompt_dataset /path/to/your/prompt_dataset \
--pretrain_dataset /path/to/your/pretrain_dataset \
--rm_pretrain /your/pretrain/rm/definition \
--rm_path /your/rm/model/path
--pretrain "/path/to/LLaMa-7B/" \
--model 'llama' \
--strategy colossalai_zero2 \
--prompt_dataset /path/to/your/prompt_dataset \
--pretrain_dataset /path/to/your/pretrain_dataset \
--rm_pretrain /your/pretrain/rm/definition \
--rm_path /your/rm/model/path
```
Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use the [script](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/generate_prompt_dataset.py) which samples `instinwild_en.json` or `instinwild_ch.json` in [InstructionWild](https://github.com/XueFuzhao/InstructionWild/tree/main/data#instructwild-data) to generate the prompt dataset.
Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning.
**Note**: the required datasets follow the following format,
- `pretrain dataset`
```json
[
{
"instruction": "Provide a list of the top 10 most popular mobile games in Asia",
"input": "",
"output": "The top 10 most popular mobile games in Asia are:\n1) PUBG Mobile\n2) Pokemon Go\n3) Candy Crush Saga\n4) Free Fire\n5) Clash of Clans\n6) Mario Kart Tour\n7) Arena of Valor\n8) Fantasy Westward Journey\n9) Subway Surfers\n10) ARK Survival Evolved",
"id": 0
},
...
]
```
- `prompt dataset`
```json
[
{
"instruction": "Edit this paragraph to make it more concise: \"Yesterday, I went to the store and bought some things. Then, I came home and put them away. After that, I went for a walk and met some friends.\"",
"id": 0
},
{
"instruction": "Write a descriptive paragraph about a memorable vacation you went on",
"id": 1
},
...
]
```
### Arg List
- --strategy: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2'
- --model: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
- --pretrain: pretrain model, type=str, default=None
- --rm_model: reward model type, type=str, choices=['gpt2', 'bloom', 'opt', 'llama'], default=None
- --rm_pretrain: pretrain model for reward model, type=str, default=None
- --rm_path: the path of rm model, type=str, default=None
- --save_path: path to save the model, type=str, default='output'
- --prompt_dataset: path of the prompt dataset, type=str, default=None
- --pretrain_dataset: path of the ptx dataset, type=str, default=None
- --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False
- --num_episodes: num of episodes for training, type=int, default=10
- --num_update_steps: number of steps to update policy per episode, type=int
- --num_collect_steps: number of steps to collect experience per episode, type=int
- --train_batch_size: batch size while training, type=int, default=8
- --ptx_batch_size: batch size to compute ptx loss, type=int, default=1
- --experience_batch_size: batch size to make experience, type=int, default=8
- --lora_rank: low-rank adaptation matrices rank, type=int, default=0
- --kl_coef: kl_coef using for computing reward, type=float, default=0.1
- --ptx_coef: ptx_coef using for computing policy loss, type=float, default=0.9
- `--strategy`: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2'
- `--model`: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
- `--pretrain`: pretrain model, type=str, default=None
- `--rm_model`: reward model type, type=str, choices=['gpt2', 'bloom', 'opt', 'llama'], default=None
- `--rm_pretrain`: pretrain model for reward model, type=str, default=None
- `--rm_path`: the path of rm model, type=str, default=None
- `--save_path`: path to save the model, type=str, default='output'
- `--prompt_dataset`: path of the prompt dataset, type=str, default=None
- `--pretrain_dataset`: path of the ptx dataset, type=str, default=None
- `--need_optim_ckpt`: whether to save optim ckpt, type=bool, default=False
- `--num_episodes`: num of episodes for training, type=int, default=10
- `--num_update_steps`: number of steps to update policy per episode, type=int
- `--num_collect_steps`: number of steps to collect experience per episode, type=int
- `--train_batch_size`: batch size while training, type=int, default=8
- `--ptx_batch_size`: batch size to compute ptx loss, type=int, default=1
- `--experience_batch_size`: batch size to make experience, type=int, default=8
- `--lora_rank`: low-rank adaptation matrices rank, type=int, default=0
- `--kl_coef`: kl_coef using for computing reward, type=float, default=0.1
- `--ptx_coef`: ptx_coef using for computing policy loss, type=float, default=0.9
## Inference example - After Stage3
We support different inference options, including int8 and int4 quantization.
For details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference).
## Attention
The examples are demos for the whole training process.You need to change the hyper-parameters to reach great performance.
#### data
- [x] [rm-static](https://huggingface.co/datasets/Dahoas/rm-static)
- [x] [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [ ] [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback)
......@@ -242,14 +300,16 @@ The examples are demos for the whole training process.You need to change the hyp
## Support Model
### GPT
- [x] GPT2-S (s)
- [x] GPT2-M (m)
- [x] GPT2-L (l)
- [x] GPT2-XL (xl)
- [x] GPT2-4B (4b)
- [ ] GPT2-6B (6b)
- [x] GPT2-S (s)
- [x] GPT2-M (m)
- [x] GPT2-L (l)
- [x] GPT2-XL (xl)
- [x] GPT2-4B (4b)
- [ ] GPT2-6B (6b)
### BLOOM
- [x] [BLOOM-560m](https://huggingface.co/bigscience/bloom-560m)
- [x] [BLOOM-1b1](https://huggingface.co/bigscience/bloom-1b1)
- [x] [BLOOM-3b](https://huggingface.co/bigscience/bloom-3b)
......@@ -257,6 +317,7 @@ The examples are demos for the whole training process.You need to change the hyp
- [ ] [BLOOM-175b](https://huggingface.co/bigscience/bloom)
### OPT
- [x] [OPT-125M](https://huggingface.co/facebook/opt-125m)
- [x] [OPT-350M](https://huggingface.co/facebook/opt-350m)
- [x] [OPT-1.3B](https://huggingface.co/facebook/opt-1.3b)
......@@ -266,10 +327,11 @@ The examples are demos for the whole training process.You need to change the hyp
- [ ] [OPT-30B](https://huggingface.co/facebook/opt-30b)
### [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md)
- [x] LLaMA-7B
- [x] LLaMA-13B
- [ ] LLaMA-33B
- [ ] LLaMA-65B
- [x] LLaMA-7B
- [x] LLaMA-13B
- [ ] LLaMA-33B
- [ ] LLaMA-65B
## Add your own models
......@@ -282,12 +344,12 @@ if it is supported in huggingface [transformers](https://github.com/huggingface/
r you can build your own model by yourself.
### Actor model
```
```python
from ..base import Actor
from transformers.models.coati import CoatiModel
class CoatiActor(Actor):
def __init__(self,
pretrained: Optional[str] = None,
checkpoint: bool = False,
......@@ -302,7 +364,8 @@ class CoatiActor(Actor):
```
### Reward model
```
```python
from ..base import RewardModel
from transformers.models.coati import CoatiModel
......@@ -325,12 +388,11 @@ class CoatiRM(RewardModel):
### Critic model
```
```python
from ..base import Critic
from transformers.models.coati import CoatiModel
class CoatiCritic(Critic):
def __init__(self,
pretrained: Optional[str] = None,
checkpoint: bool = False,
......
applications/Chat/examples/community/README.md
View file @ 6d41c3f2
:warning: **This content may be outdated since the major update of Colossal Chat. We will update this content soon.**
# Community Examples
---
We are thrilled to announce the latest updates to ColossalChat, an open-source solution for cloning ChatGPT with a complete RLHF (Reinforcement Learning with Human Feedback) pipeline.
As Colossal-AI undergoes major updates, we are actively maintaining ColossalChat to stay aligned with the project's progress. With the introduction of Community-driven example, we aim to create a collaborative platform for developers to contribute exotic features built on top of ColossalChat.
......@@ -14,11 +18,12 @@ For more information about community pipelines, please have a look at this [issu
Community examples consist of both inference and training examples that have been added by the community. Please have a look at the following table to get an overview of all community examples. Click on the Code Example to get a copy-and-paste ready code example that you can try out. If a community doesn't work as expected, please open an issue and ping the author on it.
| Example | Description | Code Example | Colab | Author |
|:---------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------:|
| Peft | Adding Peft support for SFT and Prompts model training | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/peft) | - | [YY Lin](https://github.com/yynil) |
| Train prompts on Ray | A Ray based implementation of Train prompts example | [Training On Ray](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/ray) | - | [MisterLin1995](https://github.com/MisterLin1995) |
|...|...|...|...|...|
| Example | Description | Code Example | Colab | Author |
| :------------------- | :----------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------- | :---- | ------------------------------------------------: |
| Peft | Adding Peft support for SFT and Prompts model training | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/peft) | - | [YY Lin](https://github.com/yynil) |
| Train prompts on Ray | A Ray based implementation of Train prompts example | [Training On Ray](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/ray) | - | [MisterLin1995](https://github.com/MisterLin1995) |
| ... | ... | ... | ... | ... |
### How to get involved
To join our community-driven initiative, please visit the [ColossalChat GitHub repository](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples), review the provided information, and explore the codebase. To contribute, create a new issue outlining your proposed feature or enhancement, and our team will review and provide feedback. We look forward to collaborating with you on this exciting project!
applications/Chat/examples/community/peft/README.md
View file @ 6d41c3f2
:warning: **This content may be outdated since the major update of Colossal Chat. We will update this content soon.**
# Add Peft support for SFT and Prompts model training
The original implementation just adopts the loralib and merges the layers into the final model. The huggingface peft is a better lora model implementation and can be easily training and distributed.
......@@ -5,7 +7,9 @@ The original implementation just adopts the loralib and merges the layers into t
Since reward model is relative small, I just keep it as original one. I suggest train full model to get the proper reward/critic model.
# Preliminary installation
Since the current pypi peft package(0.2) has some bugs, please install the peft package using source.
```
git clone https://github.com/huggingface/peft
cd peft
......@@ -13,6 +17,7 @@ pip install .
```
# Usage
For SFT training, just call train_peft_sft.py
Its arguments are almost identical to train_sft.py instead adding a new eval_dataset if you have a eval_dataset file. The data file is just a plain datafile, please check the format in the easy_dataset.py.
......@@ -21,4 +26,5 @@ For stage-3 rlhf training, call train_peft_prompts.py.
Its arguments are almost identical to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported.
# Dataformat
Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt.
applications/Chat/examples/community/ray/README.md
View file @ 6d41c3f2
:warning: **This content may be outdated since the major update of Colossal Chat. We will update this content soon.**
# ColossalAI on Ray
## Abstract
This is an experimental effort to run ColossalAI Chat training on Ray
## How to use?
### 1. Setup Ray clusters
Please follow the official [Ray cluster setup instructions](https://docs.ray.io/en/latest/cluster/getting-started.html) to setup an cluster with GPU support. Record the cluster's api server endpoint, it should be something similar to http://your.head.node.addrees:8265
### 2. Clone repo
Clone this project:
```shell
git clone https://github.com/hpcaitech/ColossalAI.git
```
### 3. Submit the ray job
```shell
python applications/Chat/examples/community/ray/ray_job_script.py http://your.head.node.addrees:8265
```
### 4. View your job on the Ray Dashboard
Open your ray cluster dashboard http://your.head.node.addrees:8265 to view your submitted training job.
applications/Chat/inference/README.md
View file @ 6d41c3f2
......@@ -20,21 +20,21 @@ Tha data is from [LLaMA Int8 4bit ChatBot Guide v2](https://rentry.org/llama-tar
### 8-bit
| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples |
| :---: | :---: | :---: | :---: | :---: |
| LLaMA-7B | 9.2GB | 10GB | 24GB | 3060 12GB, RTX 3080 10GB, RTX 3090 |
| LLaMA-13B | 16.3GB | 20GB | 32GB | RTX 3090 Ti, RTX 4090 |
| LLaMA-30B | 36GB | 40GB | 64GB | A6000 48GB, A100 40GB |
| LLaMA-65B | 74GB | 80GB | 128GB | A100 80GB |
| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples |
| :-------: | :---------: | :-----------------: | :----------: | :--------------------------------: |
| LLaMA-7B | 9.2GB | 10GB | 24GB | 3060 12GB, RTX 3080 10GB, RTX 3090 |
| LLaMA-13B | 16.3GB | 20GB | 32GB | RTX 3090 Ti, RTX 4090 |
| LLaMA-30B | 36GB | 40GB | 64GB | A6000 48GB, A100 40GB |
| LLaMA-65B | 74GB | 80GB | 128GB | A100 80GB |
### 4-bit
| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples |
| :---: | :---: | :---: | :---: | :---: |
| LLaMA-7B | 3.5GB | 6GB | 16GB | RTX 1660, 2060, AMD 5700xt, RTX 3050, 3060 |
| LLaMA-13B | 6.5GB | 10GB | 32GB | AMD 6900xt, RTX 2060 12GB, 3060 12GB, 3080, A2000 |
| LLaMA-30B | 15.8GB | 20GB | 64GB | RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100 |
| LLaMA-65B | 31.2GB | 40GB | 128GB | A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000, Titan Ada |
| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples |
| :-------: | :---------: | :-----------------: | :----------: | :--------------------------------------------------------: |
| LLaMA-7B | 3.5GB | 6GB | 16GB | RTX 1660, 2060, AMD 5700xt, RTX 3050, 3060 |
| LLaMA-13B | 6.5GB | 10GB | 32GB | AMD 6900xt, RTX 2060 12GB, 3060 12GB, 3080, A2000 |
| LLaMA-30B | 15.8GB | 20GB | 64GB | RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100 |
| LLaMA-65B | 31.2GB | 40GB | 128GB | A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000, Titan Ada |
## General setup
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