Lightning-AI/litgpt

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20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.

About Lightning-AI/litgpt

Lightning-AI/litgpt is an open-source project on GitHub, mainly written in Python. 20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale. It currently holds 13,673 stars and 0 forks with 0 open issues, and was last pushed on an unknown date (repository created unknown).

Project Overview

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GitHub Repository Details

Repository Lightning-AI/litgpt · default branch - · size 0 KB · watchers 0 · source: GitHub REST API and repository README

README

⚡ LitGPT

20+ high-performance LLMs with recipes to pretrain, finetune, and deploy at scale.

✅ From scratch implementations      ✅ No abstractions         ✅ Beginner friendly
   ✅ Flash attention                   ✅ FSDP                    ✅ LoRA, QLoRA, Adapter
✅ Reduce GPU memory (fp4/8/16/32)   ✅ 1-1000+ GPUs/TPUs       ✅ 20+ LLMs

---

Quick start • Models • Finetune • Deploy • All workflows • Features • Recipes (YAML) • Lightning AI • Tutorials

Looking for GPUs?

Over 340,000 developers use Lightning Cloud - purpose-built for PyTorch and PyTorch Lightning.

GPUs from $0.19.
Clusters: frontier-grade training/inference clusters.
AI Studio (vibe train): workspaces where AI helps you debug, tune and vibe train.
AI Studio (vibe deploy): workspaces where AI helps you optimize, and deploy models.
Notebooks: Persistent GPU workspaces where AI helps you code and analyze.
Inference: Deploy models as inference APIs.

Finetune, pretrain, and inference LLMs Lightning fast ⚡⚡

Every LLM is implemented from scratch with no abstractions and full control, making them blazing fast, minimal, and performant at enterprise scale.

✅ Enterprise ready - Apache 2.0 for unlimited enterprise use.
✅ Developer friendly - Easy debugging with no abstraction layers and single file implementations.
✅ Optimized performance - Models designed to maximize performance, reduce costs, and speed up training.
✅ Proven recipes - Highly-optimized training/finetuning recipes tested at enterprise scale.

Quick start

Install LitGPT

pip install 'litgpt[extra]'

Load and use any of the 20+ LLMs:

from litgpt import LLM
llm = LLM.load("microsoft/phi-2")
text = llm.generate("Fix the spelling: Every fall, the family goes to the mountains.")
print(text)
Corrected Sentence: Every fall, the family goes to the mountains.

✅ Optimized for fast inference
✅ Quantization
✅ Runs on low-memory GPUs
✅ No layers of internal abstractions
✅ Optimized for production scale

Advanced install options

Install from source:

git clone https://github.com/Lightning-AI/litgpt
cd litgpt
if using uv
uv sync --all-extras
if using pip
pip install -e ".[extra,compiler,test]"

Explore the full Python API docs.

---

Choose from 20+ LLMs

Every model is written from scratch to maximize performance and remove layers of abstraction:

| Model | Model size | Author | Reference | |----|----|----|----| | Llama 3, 3.1, 3.2, 3.3 | 1B, 3B, 8B, 70B, 405B | Meta AI | Meta AI 2024 | | Code Llama | 7B, 13B, 34B, 70B | Meta AI | Rozière et al. 2023 | | CodeGemma | 7B | Google | Google Team, Google Deepmind | | Gemma 2 | 2B, 9B, 27B | Google | Google Team, Google Deepmind | | Phi 4 | 14B | Microsoft Research | Abdin et al. 2024 | | Qwen2.5 | 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B | Alibaba Group | Qwen Team 2024 | | Qwen2.5 Coder | 0.5B, 1.5B, 3B, 7B, 14B, 32B | Alibaba Group | Hui, Binyuan et al. 2024 | | R1 Distill Llama | 8B, 70B | DeepSeek AI | DeepSeek AI 2025 | | ... | ... | ... | ... |

See full list of 20+ LLMs

All models

| Model | Model size | Author | Reference | |----|----|----|----| | CodeGemma | 7B | Google | Google Team, Google Deepmind | | Code Llama | 7B, 13B, 34B, 70B | Meta AI | Rozière et al. 2023 | | Falcon | 7B, 40B, 180B | TII UAE | TII 2023 | | Falcon 3 | 1B, 3B, 7B, 10B | TII UAE | TII 2024 | | FreeWilly2 (Stable Beluga 2) | 70B | Stability AI | Stability AI 2023 | | Function Calling Llama 2 | 7B | Trelis | Trelis et al. 2023 | | Gemma | 2B, 7B | Google | Google Team, Google Deepmind | | Gemma 2 | 9B, 27B | Google | Google Team, Google Deepmind | | Gemma 3 | 1B, 4B, 12B, 27B | Google | Google Team, Google Deepmind | | Llama 2 | 7B, 13B, 70B | Meta AI | Touvron et al. 2023 | | Llama 3.1 | 8B, 70B | Meta AI | Meta AI 2024 | | Llama 3.2 | 1B, 3B | Meta AI | Meta AI 2024 | | Llama 3.3 | 70B | Meta AI | Meta AI 2024 | | Mathstral | 7B | Mistral AI | Mistral AI 2024 | | MicroLlama | 300M | Ken Wang | MicroLlama repo | | Mixtral MoE | 8x7B | Mistral AI | Mistral AI 2023 | | Mistral | 7B, 123B | Mistral AI | Mistral AI 2023 | | Mixtral MoE | 8x22B | Mistral AI | Mistral AI 2024 | | OLMo | 1B, 7B | Allen Institute for AI (AI2) | Groeneveld et al. 2024 | | OpenLLaMA | 3B, 7B, 13B | OpenLM Research | Geng & Liu 2023 | | Phi 1.5 & 2 | 1.3B, 2.7B | Microsoft Research | Li et al. 2023 | | Phi 3 | 3.8B | Microsoft Research | Abdin et al. 2024 | | Phi 4 | 14B | Microsoft Research | Abdin et al. 2024 | | Phi 4 Mini Instruct | 3.8B | Microsoft Research | Microsoft 2025 | | Phi 4 Mini Reasoning | 3.8B | Microsoft Research | Xu, Peng et al. 2025 | | Phi 4 Reasoning | 3.8B | Microsoft Research | Abdin et al. 2025 | | Phi 4 Reasoning Plus | 3.8B | Microsoft Research | Abdin et al. 2025 | | Platypus | 7B, 13B, 70B | Lee et al. | Lee, Hunter, and Ruiz 2023 | | Pythia | {14,31,70,160,410}M, {1,1.4,2.8,6.9,12}B | EleutherAI | Biderman et al. 2023 | | Qwen2.5 | 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B | Alibaba Group | Qwen Team 2024 | | Qwen2.5 Coder | 0.5B, 1.5B, 3B, 7B, 14B, 32B | Alibaba Group | Hui, Binyuan et al. 2024 | | Qwen2.5 1M (Long Context) | 7B, 14B | Alibaba Group | Qwen Team 2025 | | Qwen2.5 Math | 1.5B, 7B, 72B | Alibaba Group | An, Yang et al. 2024 | | QwQ | 32B | Alibaba Group | Qwen Team 2025 | | QwQ-Preview | 32B | Alibaba Group | Qwen Team 2024 | | Qwen3 | 0.6B, 1.7B, 4B{Hybrid, Thinking-2507, Instruct-2507}, 8B, 14B, 32B | Alibaba Group | Qwen Team 2025 | | Qwen3 MoE | 30B{Hybrid, Thinking-2507, Instruct-2507}, 235B{Hybrid, Thinking-2507, Instruct-2507} | Alibaba Group | Qwen Team 2025 | | R1 Distill Llama | 8B, 70B | DeepSeek AI | DeepSeek AI 2025 | | SmolLM2 | 135M, 360M, 1.7B | Hugging Face | Hugging Face 2024 | | Salamandra | 2B, 7B | Barcelona Supercomputing Centre | BSC-LTC 2024 | | StableCode | 3B | Stability AI | Stability AI 2023 | | StableLM | 3B, 7B | Stability AI | Stability AI 2023 | | StableLM Zephyr | 3B | Stability AI | Stability AI 2023 | | TinyLlama | 1.1B | Zhang et al. | Zhang et al. 2023 |

Tip: You can list all available models by running the litgpt download list command.

---

Workflows

Finetune • Pretrain • Continued pretraining • Evaluate • Deploy • Test

Use the command line interface to run advanced workflows such as pretraining or finetuning on your own data.

All workflows

After installing LitGPT, select the model and workflow to run (finetune, pretrain, evaluate, deploy, etc...):

# litgpt [action] [model]
litgpt  serve     meta-llama/Llama-3.2-3B-Instruct
litgpt  finetune  meta-llama/Llama-3.2-3B-Instruct
litgpt  pretrain  meta-llama/Llama-3.2-3B-Instruct
litgpt  chat      meta-llama/Llama-3.2-3B-Instruct
litgpt  evaluate  meta-llama/Llama-3.2-3B-Instruct

----

Finetune an LLM

https://github.com/Lightning-AI/litgpt/blob/HEAD/Run on Studios

Finetuning is the process of taking a pretrained AI model and further training it on a smaller, specialized dataset tailored to a specific task or application.

# 0) setup your dataset
curl -L https://huggingface.co/datasets/ksaw008/finance_alpaca/resolve/main/finance_alpaca.json -o my_custom_dataset.json
1) Finetune a model (auto downloads weights)
litgpt finetune microsoft/phi-2 \
  --data JSON \
  --data.json_path my_custom_dataset.json \
  --data.val_split_fraction 0.1 \
  --out_dir out/custom-model
2) Test the model
litgpt chat out/custom-model/final
3) Deploy the model
litgpt serve out/custom-model/final

Read the full finetuning docs

----

Deploy an LLM

https://github.com/Lightning-AI/litgpt/blob/HEAD/Deploy on Studios

Deploy a pretrained or finetuned LLM to use it in real-world applications. Deploy automatically sets up a web server that can be accessed by a website or app.

# deploy an out-of-the-box LLM
litgpt serve microsoft/phi-2
deploy your own trained model
litgpt serve path/to/microsoft/phi-2/checkpoint

Show code to query server:

Test the server in a separate terminal and integrate the model API into your AI product:

# 3) Use the server (in a separate Python session)
import requests, json
response = requests.post(
    "http://127.0.0.1:8000/predict",
    json={"prompt": "Fix typos in the following sentence: Example input"}
)
print(response.json()["output"])

Read the full deploy docs.

----

Evaluate an LLM

Evaluate an LLM to test its performance on various tasks to see how well it understands and generates text. Simply put, we can evaluate things like how well it would do in college-level chemistry, coding, etc... (MMLU, Truthful QA, etc...)

litgpt evaluate microsoft/phi-2 --tasks 'truthfulqa_mc2,mmlu'

Read the full evaluation docs.

----

Test an LLM

Test how well the model works via an interactive chat. Use the chat command to chat, extract embeddings, etc...

Here's an example showing how to use the Phi-2 LLM:

litgpt chat microsoft/phi-2
> Prompt: What do Llamas eat?

Full code:

# 1) List all supported LLMs
litgpt download list
2) Use a model (auto downloads weights)
litgpt chat microsoft/phi-2
> Prompt: What do Llamas eat?

The download of certain models requires an additional access token. You can read more about this in the download documentation.

Read the full chat docs.

----

Pretrain an LLM

Pretraining is the process of teaching an AI model by exposing it to a large amount of data before it is fine-tuned for specific tasks.

Show code:

mkdir -p custom_texts
curl https://www.gutenberg.org/cache/epub/24440/pg24440.txt --output custom_texts/book1.txt
curl https://www.gutenberg.org/cache/epub/26393/pg26393.txt --output custom_texts/book2.txt
1) Download a tokenizer
litgpt download EleutherAI/pythia-160m \
  --tokenizer_only True
2) Pretrain the model
litgpt pretrain EleutherAI/pythia-160m \
  --tokenizer_dir EleutherAI/pythia-160m \
  --data TextFiles \
  --data.train_data_path "custom_texts/" \
  --train.max_tokens 10_000_000 \
  --out_dir out/custom-model
3) Test the model
litgpt chat out/custom-model/final

Read the full pretraining docs

----

Continue pretraining an LLM

Continued pretraining is another way of finetuning that specializes an already pretrained model by training on custom data:

Show code:

mkdir -p custom_texts
curl https://www.gutenberg.org/cache/epub/24440/pg24440.txt --output custom_texts/book1.txt
curl https://www.gutenberg.org/cache/epub/26393/pg26393.txt --output custom_texts/book2.txt
1) Continue pretraining a model (auto downloads weights)
litgpt pretrain EleutherAI/pythia-160m \
  --tokenizer_dir EleutherAI/pythia-160m \
  --initial_checkpoint_dir EleutherAI/pythia-160m \
  --data TextFiles \
  --data.train_data_path "custom_texts/" \
  --train.max_tokens 10_000_000 \
  --out_dir out/custom-model
2) Test the model
litgpt chat out/custom-model/final

Read the full continued pretraining docs

----

State-of-the-art features

✅ State-of-the-art optimizations: Flash Attention v2, multi-GPU support via fully-sharded data parallelism, optional CPU offloading, and TPU and XLA support.
✅ Pretrain, finetune, and deploy
✅ Reduce compute requirements with low-precision settings: FP16, BF16, and FP16/FP32 mixed.
✅ Lower memory requirements with quantization: 4-bit floats, 8-bit integers, and double quantization.
✅ Configuration files for great out-of-the-box performance.
✅ Parameter-efficient finetuning: LoRA, QLoRA, Adapter, and Adapter v2.
✅ Exporting to other popular model weight formats.
✅ Many popular datasets for pretraining and finetuning, and support for custom datasets.
✅ Readable and easy-to-modify code to experiment with the latest research ideas.

---

Training recipes

LitGPT comes with validated recipes (YAML configs) to train models under different conditions. We've generated these recipes based on the parameters we found to perform the best for different training conditions.

Browse all training recipes here.

Example

litgpt finetune \
  --config https://raw.githubusercontent.com/Lightning-AI/litgpt/main/config_hub/finetune/llama-2-7b/lora.yaml

✅ Use configs to customize training

Configs let you customize training for all granular parameters like:

# The path to the base model's checkpoint directory to load for finetuning. (type: , default: checkpoints/stabilityai/stablelm-base-alpha-3b) checkpoint_dir: checkpoints/meta-llama/Llama-2-7b-hf Directory in which to save checkpoints and logs. (type: , default: out/lora) out_dir: out/finetune/qlora-llama2-7b The precision to use for finetuning. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null) precision: bf16-true

...

✅ Example: LoRA finetuning config

```yaml

The path to the base model's checkpoint directory to load for finetuning. (type: , default: checkpoints/stabilityai/stablelm-base-alpha-3b)

checkpoint_dir: checkpoints/meta-llama/Llama-2-7b-hf

Directory in which to save checkpoints and logs. (type: , default: out/lora)

out_dir: out/finetune/qlora-llama2-7b

The precision to use for finetuning. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)

precision: bf16-true

If set, quantize the model with this algorithm. See ``tutorials/quantize.md`` for more information. (type: Optional[Literal['nf4', 'nf4-dq', 'fp4', 'fp4-dq', 'int8-training']], default: null)

quantize: bnb.nf4

How many devices/GPUs to use. (type: Union[int, str], default: 1)

devices: 1

How many nodes to use. (type: int, default: 1)

num_nodes: 1

The LoRA rank. (type: int, default: 8)

lora_r: 32

The LoRA alpha. (type: int, default: 16)

lora_alpha: 16

The LoRA dropout value. (type: float, default: 0.05)

lora_dropout: 0.05

Whether to apply LoRA to the query weights in attention. (type: bool, default: True)

lora_query: true

Whether to apply LoRA to the key weights in attention. (type: bool, default: False)

lora_key: false

Whether to apply LoRA to the value weights in attention. (type: bool, defa

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Lightning-AI/litgpt

About Lightning-AI/litgpt

Project Overview

GitHub Repository Details

README

⚡ LitGPT

Looking for GPUs?

Finetune, pretrain, and inference LLMs Lightning fast ⚡⚡

Quick start

Corrected Sentence: Every fall, the family goes to the mountains.

if using uv

if using pip

Choose from 20+ LLMs

All models

Workflows

All workflows

Finetune an LLM

1) Finetune a model (auto downloads weights)

2) Test the model

3) Deploy the model

Deploy an LLM

deploy your own trained model

Evaluate an LLM

Test an LLM

2) Use a model (auto downloads weights)

Pretrain an LLM

1) Download a tokenizer

2) Pretrain the model

3) Test the model

Continue pretraining an LLM

1) Continue pretraining a model (auto downloads weights)

2) Test the model

State-of-the-art features

Training recipes

Example

Directory in which to save checkpoints and logs. (type: , default: out/lora)

The precision to use for finetuning. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)

The path to the base model's checkpoint directory to load for finetuning. (type: , default: checkpoints/stabilityai/stablelm-base-alpha-3b)

Directory in which to save checkpoints and logs. (type: , default: out/lora)

The precision to use for finetuning. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)

If set, quantize the model with this algorithm. See `tutorials/quantize.md` for more information. (type: Optional[Literal['nf4', 'nf4-dq', 'fp4', 'fp4-dq', 'int8-training']], default: null)

How many devices/GPUs to use. (type: Union[int, str], default: 1)

How many nodes to use. (type: int, default: 1)

The LoRA rank. (type: int, default: 8)

The LoRA alpha. (type: int, default: 16)

The LoRA dropout value. (type: float, default: 0.05)

Whether to apply LoRA to the query weights in attention. (type: bool, default: True)

Whether to apply LoRA to the key weights in attention. (type: bool, default: False)

Whether to apply LoRA to the value weights in attention. (type: bool, defa

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If set, quantize the model with this algorithm. See ``tutorials/quantize.md`` for more information. (type: Optional[Literal['nf4', 'nf4-dq', 'fp4', 'fp4-dq', 'int8-training']], default: null)