| # Download Model Weights with LitGPT |
|
|
| LitGPT supports a variety of LLM architectures with publicly available weights. You can download model weights and access a list of supported models using the `litgpt download list` command. |
|
|
| |
|
|
|
|
| | Model | Model size | Author | Reference | |
| |----|----|----|----| |
| | CodeGemma | 7B | Google | [Google Team, Google Deepmind](https://ai.google.dev/gemma/docs/codegemma) | |
| | Code Llama | 7B, 13B, 34B, 70B | Meta AI | [Rozière et al. 2023](https://arxiv.org/abs/2308.12950) | |
| | Danube2 | 1.8B | H2O.ai | [H2O.ai](https://h2o.ai/platform/danube-1-8b/) | |
| | Dolly | 3B, 7B, 12B | Databricks | [Conover et al. 2023](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm) | |
| | Falcon | 7B, 40B, 180B | TII UAE | [TII 2023](https://falconllm.tii.ae) | |
| | Falcon 3 | 1B, 3B, 7B, 10B | TII UAE | [TII 2024](https://huggingface.co/blog/falcon3) | |
| | FreeWilly2 (Stable Beluga 2) | 70B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stable-beluga-large-instruction-fine-tuned-models) | |
| | Function Calling Llama 2 | 7B | Trelis | [Trelis et al. 2023](https://huggingface.co/Trelis/Llama-2-7b-chat-hf-function-calling-v2) | |
| | Gemma | 2B, 7B | Google | [Google Team, Google Deepmind](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf) | |
| | Gemma 2 | 2B, 9B, 27B | Google | [Google Team, Google Deepmind](https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf) | |
| | Gemma 3 | 1B, 4B, 12B, 27B | Google | [Google Team, Google Deepmind](https://arxiv.org/pdf/2503.19786) |
| | Llama 2 | 7B, 13B, 70B | Meta AI | [Touvron et al. 2023](https://arxiv.org/abs/2307.09288) | |
| | Llama 3 | 8B, 70B | Meta AI | [Meta AI 2024](https://github.com/meta-llama/llama3) | |
| | Llama 3.1 | 8B, 70B, 405B | Meta AI | [Meta AI 2024](https://github.com/meta-llama/llama3) | |
| | Llama 3.2 | 1B, 3B | Meta AI | [Meta AI 2024](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md) | |
| | Llama 3.3 | 70B | Meta AI | [Meta AI 2024](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) | |
| | Llama 3.1 Nemotron | 70B | NVIDIA | [NVIDIA AI 2024](https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct/modelcard) | |
| | LongChat | 7B, 13B | LMSYS | [LongChat Team 2023](https://lmsys.org/blog/2023-06-29-longchat/) | |
| | Mathstral | 7B | Mistral AI | [Mistral AI 2024](https://mistral.ai/news/mathstral/) | |
| | MicroLlama | 300M | Ken Wang | [MicroLlama repo](https://github.com/keeeeenw/MicroLlama) |
| | Mixtral MoE | 8x7B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/mixtral-of-experts/) | |
| | Mistral | 7B, 123B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/announcing-mistral-7b/) | |
| | Mixtral MoE | 8x22B | Mistral AI | [Mistral AI 2024](https://mistral.ai/news/mixtral-8x22b/) | |
| | Nous-Hermes | 7B, 13B, 70B | NousResearch | [Org page](https://huggingface.co/NousResearch) | |
| | OLMo | 1B, 7B | Allen Institute for AI (AI2) | [Groeneveld et al. 2024](https://aclanthology.org/2024.acl-long.841/) | |
| | OpenLLaMA | 3B, 7B, 13B | OpenLM Research | [Geng & Liu 2023](https://github.com/openlm-research/open_llama) | |
| | Phi 1.5 & 2 | 1.3B, 2.7B | Microsoft Research | [Li et al. 2023](https://arxiv.org/abs/2309.05463) | |
| | Phi 3 & 3.5 | 3.8B | Microsoft Research | [Abdin et al. 2024](https://arxiv.org/abs/2404.14219) |
| | Phi 4 | 14B | Microsoft Research | [Abdin et al. 2024](https://arxiv.org/abs/2412.08905) | |
| | Phi 4 Mini Instruct | 3.8B | Microsoft Research | [Microsoft 2025](https://arxiv.org/abs/2503.01743) | |
| | Phi 4 Mini Reasoning | 3.8B | Microsoft Research | [Xu, Peng et al. 2025](https://arxiv.org/abs/2504.21233) | |
| | Phi 4 Reasoning | 3.8B | Microsoft Research | [Abdin et al. 2025](https://arxiv.org/abs/2504.21318) | |
| | Phi 4 Reasoning Plus | 3.8B | Microsoft Research | [Abdin et al. 2025](https://arxiv.org/abs/2504.21318) | |
| | Platypus | 7B, 13B, 70B | Lee et al. | [Lee, Hunter, and Ruiz 2023](https://arxiv.org/abs/2308.07317) | |
| | Pythia | {14,31,70,160,410}M, {1,1.4,2.8,6.9,12}B | EleutherAI | [Biderman et al. 2023](https://arxiv.org/abs/2304.01373) | |
| | Qwen2.5 | 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B | Alibaba Group | [Qwen Team 2024](https://qwenlm.github.io/blog/qwen2.5/) | |
| | Qwen2.5 Coder | 0.5B, 1.5B, 3B, 7B, 14B, 32B | Alibaba Group | [Hui, Binyuan et al. 2024](https://arxiv.org/abs/2409.12186) | |
| | Qwen2.5 1M (Long Context) | 7B, 14B | Alibaba Group | [Qwen Team 2025](https://qwenlm.github.io/blog/qwen2.5-1m/) | |
| | Qwen2.5 Math | 1.5B, 7B, 72B | Alibaba Group | [An, Yang et al. 2024](https://arxiv.org/abs/2409.12122) | |
| | QwQ | 32B | Alibaba Group | [Qwen Team 2025](https://qwenlm.github.io/blog/qwq-32b/) | |
| | QwQ-Preview | 32B | Alibaba Group | [Qwen Team 2024](https://qwenlm.github.io/blog/qwq-32b-preview/) | |
| | Qwen3 | 0.6B, 1.7B, 4B, 8B, 14B, 32B | Alibaba Group | [Qwen Team 2025](https://arxiv.org/abs/2505.09388/) | |
| | Qwen3 MoE | 30B, 235B | Alibaba Group | [Qwen Team 2025](https://arxiv.org/abs/2505.09388/) | |
| | R1 Distll Llama | 8B, 70B | DeepSeek AI | [DeepSeek AI 2025](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf) | |
| | RedPajama-INCITE | 3B, 7B | Together | [Together 2023](https://together.ai/blog/redpajama-models-v1) | |
| | SmolLM2 | 135M, 360M, 1.7B | Hugging Face | [Hugging Face 2024](https://github.com/huggingface/smollm) | |
| | StableCode | 3B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stablecode-llm-generative-ai-coding) | |
| | Salamandra | 2B, 7B | Barcelona Supercomputing Centre | [BSC-LTC 2024](https://github.com/BSC-LTC/salamandra) | |
| | StableLM | 3B, 7B | Stability AI | [Stability AI 2023](https://github.com/Stability-AI/StableLM) | |
| | StableLM Zephyr | 3B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stablecode-llm-generative-ai-coding) | |
| | TinyLlama | 1.1B | Zhang et al. | [Zhang et al. 2023](https://github.com/jzhang38/TinyLlama) | |
| | Vicuna | 7B, 13B, 33B | LMSYS | [Li et al. 2023](https://lmsys.org/blog/2023-03-30-vicuna/) | | |
|
|
| |
|
|
| ## General Instructions |
|
|
| ### 1. List Available Models |
|
|
| To see all supported models, run the following command: |
|
|
| ```bash |
| litgpt download list |
| ``` |
|
|
| The output is shown below: |
|
|
| ``` |
| allenai/OLMo-1B-hf |
| allenai/OLMo-7B-hf |
| allenai/OLMo-7B-Instruct-hf |
| bsc-lt/salamandra-2b |
| bsc-lt/salamandra-2b-instruct |
| bsc-lt/salamandra-7b |
| bsc-lt/salamandra-7b-instruct |
| codellama/CodeLlama-13b-hf |
| codellama/CodeLlama-13b-Instruct-hf |
| codellama/CodeLlama-13b-Python-hf |
| codellama/CodeLlama-34b-hf |
| codellama/CodeLlama-34b-Instruct-hf |
| codellama/CodeLlama-34b-Python-hf |
| codellama/CodeLlama-70b-hf |
| codellama/CodeLlama-70b-Instruct-hf |
| codellama/CodeLlama-70b-Python-hf |
| codellama/CodeLlama-7b-hf |
| codellama/CodeLlama-7b-Instruct-hf |
| codellama/CodeLlama-7b-Python-hf |
| databricks/dolly-v2-12b |
| databricks/dolly-v2-3b |
| databricks/dolly-v2-7b |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B |
| deepseek-ai/DeepSeek-R1-Distill-Llama-70B |
| EleutherAI/pythia-1.4b |
| EleutherAI/pythia-1.4b-deduped |
| EleutherAI/pythia-12b |
| EleutherAI/pythia-12b-deduped |
| EleutherAI/pythia-14m |
| EleutherAI/pythia-160m |
| EleutherAI/pythia-160m-deduped |
| EleutherAI/pythia-1b |
| EleutherAI/pythia-1b-deduped |
| EleutherAI/pythia-2.8b |
| EleutherAI/pythia-2.8b-deduped |
| EleutherAI/pythia-31m |
| EleutherAI/pythia-410m |
| EleutherAI/pythia-410m-deduped |
| EleutherAI/pythia-6.9b |
| EleutherAI/pythia-6.9b-deduped |
| EleutherAI/pythia-70m |
| EleutherAI/pythia-70m-deduped |
| garage-bAInd/Camel-Platypus2-13B |
| garage-bAInd/Camel-Platypus2-70B |
| garage-bAInd/Platypus-30B |
| garage-bAInd/Platypus2-13B |
| garage-bAInd/Platypus2-70B |
| garage-bAInd/Platypus2-70B-instruct |
| garage-bAInd/Platypus2-7B |
| garage-bAInd/Stable-Platypus2-13B |
| google/codegemma-7b-it |
| google/gemma-3-27b-it |
| google/gemma-3-12b-it |
| google/gemma-3-4b-it |
| google/gemma-3-1b-it |
| google/gemma-2-27b |
| google/gemma-2-27b-it |
| google/gemma-2-2b |
| google/gemma-2-2b-it |
| google/gemma-2-9b |
| google/gemma-2-9b-it |
| google/gemma-2b |
| google/gemma-2b-it |
| google/gemma-7b |
| google/gemma-7b-it |
| h2oai/h2o-danube2-1.8b-chat |
| HuggingFaceTB/SmolLM2-135M |
| HuggingFaceTB/SmolLM2-135M-Instruct |
| HuggingFaceTB/SmolLM2-360M |
| HuggingFaceTB/SmolLM2-360M-Instruct |
| HuggingFaceTB/SmolLM2-1.7B |
| HuggingFaceTB/SmolLM2-1.7B-Instruct |
| lmsys/longchat-13b-16k |
| lmsys/longchat-7b-16k |
| lmsys/vicuna-13b-v1.3 |
| lmsys/vicuna-13b-v1.5 |
| lmsys/vicuna-13b-v1.5-16k |
| lmsys/vicuna-33b-v1.3 |
| lmsys/vicuna-7b-v1.3 |
| lmsys/vicuna-7b-v1.5 |
| lmsys/vicuna-7b-v1.5-16k |
| meta-llama/Llama-2-13b-chat-hf |
| meta-llama/Llama-2-13b-hf |
| meta-llama/Llama-2-70b-chat-hf |
| meta-llama/Llama-2-70b-hf |
| meta-llama/Llama-2-7b-chat-hf |
| meta-llama/Llama-2-7b-hf |
| meta-llama/Llama-3.2-1B |
| meta-llama/Llama-3.2-1B-Instruct |
| meta-llama/Llama-3.2-3B |
| meta-llama/Llama-3.2-3B-Instruct |
| meta-llama/Llama-3.3-70B-Instruct |
| meta-llama/Meta-Llama-3-70B |
| meta-llama/Meta-Llama-3-70B-Instruct |
| meta-llama/Meta-Llama-3-8B |
| meta-llama/Meta-Llama-3-8B-Instruct |
| meta-llama/Meta-Llama-3.1-405B |
| meta-llama/Meta-Llama-3.1-405B-Instruct |
| meta-llama/Meta-Llama-3.1-70B |
| meta-llama/Meta-Llama-3.1-70B-Instruct |
| meta-llama/Meta-Llama-3.1-8B |
| meta-llama/Meta-Llama-3.1-8B-Instruct |
| microsoft/phi-1_5 |
| microsoft/phi-2 |
| microsoft/Phi-3-mini-128k-instruct |
| microsoft/Phi-3-mini-4k-instruct |
| microsoft/Phi-3.5-mini-instruct |
| microsoft/phi-4 |
| microsoft/Phi-4-mini-instruct |
| mistralai/mathstral-7B-v0.1 |
| mistralai/Mistral-7B-Instruct-v0.1 |
| mistralai/Mistral-7B-Instruct-v0.2 |
| mistralai/Mistral-7B-Instruct-v0.3 |
| mistralai/Mistral-7B-v0.1 |
| mistralai/Mistral-7B-v0.3 |
| mistralai/Mistral-Large-Instruct-2407 |
| mistralai/Mistral-Large-Instruct-2411 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 |
| mistralai/Mixtral-8x7B-v0.1 |
| mistralai/Mixtral-8x22B-Instruct-v0.1 |
| mistralai/Mixtral-8x22B-v0.1 |
| NousResearch/Nous-Hermes-13b |
| NousResearch/Nous-Hermes-llama-2-7b |
| NousResearch/Nous-Hermes-Llama2-13b |
| nvidia/Llama-3.1-Nemotron-70B-Instruct-HF |
| openlm-research/open_llama_13b |
| openlm-research/open_llama_3b |
| openlm-research/open_llama_7b |
| Qwen/Qwen2.5-0.5B |
| Qwen/Qwen2.5-0.5B-Instruct |
| Qwen/Qwen2.5-1.5B |
| Qwen/Qwen2.5-1.5B-Instruct |
| Qwen/Qwen2.5-3B |
| Qwen/Qwen2.5-3B-Instruct |
| Qwen/Qwen2.5-7B |
| Qwen/Qwen2.5-7B-Instruct |
| Qwen/Qwen2.5-7B-Instruct-1M |
| Qwen/Qwen2.5-14B |
| Qwen/Qwen2.5-14B-Instruct |
| Qwen/Qwen2.5-14B-Instruct-1M |
| Qwen/Qwen2.5-32B |
| Qwen/Qwen2.5-32B-Instruct |
| Qwen/Qwen2.5-72B |
| Qwen/Qwen2.5-72B-Instruct |
| Qwen/Qwen2.5-Coder-0.5B |
| Qwen/Qwen2.5-Coder-0.5B-Instruct |
| Qwen/Qwen2.5-Coder-1.5B |
| Qwen/Qwen2.5-Coder-1.5B-Instruct |
| Qwen/Qwen2.5-Coder-3B |
| Qwen/Qwen2.5-Coder-3B-Instruct |
| Qwen/Qwen2.5-Coder-7B |
| Qwen/Qwen2.5-Coder-7B-Instruct |
| Qwen/Qwen2.5-Coder-14B |
| Qwen/Qwen2.5-Coder-14B-Instruct |
| Qwen/Qwen2.5-Coder-32B |
| Qwen/Qwen2.5-Coder-32B-Instruct |
| Qwen/Qwen2.5-Math-1.5B |
| Qwen/Qwen2.5-Math-1.5B-Instruct |
| Qwen/Qwen2.5-Math-7B |
| Qwen/Qwen2.5-Math-7B-Instruct |
| Qwen/Qwen2.5-Math-72B |
| Qwen/Qwen2.5-Math-72B-Instruct |
| Qwen/QwQ-32B |
| Qwen/QwQ-32B-Preview |
| stabilityai/FreeWilly2 |
| stabilityai/stable-code-3b |
| stabilityai/stablecode-completion-alpha-3b |
| stabilityai/stablecode-completion-alpha-3b-4k |
| stabilityai/stablecode-instruct-alpha-3b |
| stabilityai/stablelm-3b-4e1t |
| stabilityai/stablelm-base-alpha-3b |
| stabilityai/stablelm-base-alpha-7b |
| stabilityai/stablelm-tuned-alpha-3b |
| stabilityai/stablelm-tuned-alpha-7b |
| stabilityai/stablelm-zephyr-3b |
| tiiuae/falcon-180B |
| tiiuae/falcon-180B-chat |
| tiiuae/falcon-40b |
| tiiuae/falcon-40b-instruct |
| tiiuae/falcon-7b |
| tiiuae/falcon-7b-instruct |
| tiiuae/Falcon3-1B-Base |
| tiiuae/Falcon3-1B-Instruct |
| tiiuae/Falcon3-3B-Base |
| tiiuae/Falcon3-3B-Instruct |
| tiiuae/Falcon3-7B-Base |
| tiiuae/Falcon3-7B-Instruct |
| tiiuae/Falcon3-10B-Base |
| tiiuae/Falcon3-10B-Instruct |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
| TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
| togethercomputer/LLaMA-2-7B-32K |
| togethercomputer/RedPajama-INCITE-7B-Base |
| togethercomputer/RedPajama-INCITE-7B-Chat |
| togethercomputer/RedPajama-INCITE-7B-Instruct |
| togethercomputer/RedPajama-INCITE-Base-3B-v1 |
| togethercomputer/RedPajama-INCITE-Base-7B-v0.1 |
| togethercomputer/RedPajama-INCITE-Chat-3B-v1 |
| togethercomputer/RedPajama-INCITE-Chat-7B-v0.1 |
| togethercomputer/RedPajama-INCITE-Instruct-3B-v1 |
| togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1 |
| Trelis/Llama-2-7b-chat-hf-function-calling-v2 |
| unsloth/Mistral-7B-v0.2 |
| ``` |
|
|
| |
|
|
| > [!TIP] |
| > To sort the list above by model name after the `/`, use `litgpt download list | sort -f -t'/' -k2`. |
|
|
| |
|
|
| > [!NOTE] |
| > If you want to adopt a model variant that is not listed in the table above but has a similar architecture as one of the supported models, you can use this model by by using the `--model_name` argument as shown below: |
| > |
| > ```bash |
| > litgpt download NousResearch/Hermes-2-Pro-Mistral-7B \ |
| > --model_name Mistral-7B-v0.1 |
| > ``` |
|
|
| |
|
|
| ### 2. Download Model Weights |
|
|
| To download the weights for a specific model provide a `<repo_id>` with the model's repository ID. For example: |
|
|
| ```bash |
| litgpt download <repo_id> |
| ``` |
|
|
| This command downloads the model checkpoint into the `checkpoints/` directory. |
|
|
| |
|
|
| ### 3. Additional Help |
|
|
| For more options, add the `--help` flag when running the script: |
|
|
| ```bash |
| litgpt download --help |
| ``` |
|
|
| |
|
|
| ### 4. Run the Model |
|
|
| After conversion, run the model with the given checkpoint path as input, adjusting `repo_id` accordingly: |
|
|
| ```bash |
| litgpt chat <repo_id> |
| ``` |
|
|
| |
|
|
| ## Tinyllama Example |
|
|
| This section shows a typical end-to-end example for downloading and using TinyLlama: |
|
|
| 1. List available TinyLlama checkpoints: |
|
|
| ```bash |
| litgpt download list | grep Tiny |
| ``` |
|
|
| ``` |
| TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
| ``` |
|
|
| 2. Download a TinyLlama checkpoint: |
|
|
| ```bash |
| export repo_id=TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
| litgpt download $repo_id |
| ``` |
|
|
| 3. Use the TinyLlama model: |
|
|
| ```bash |
| litgpt chat $repo_id |
| ``` |
|
|
| |
| ## Specific models and access tokens |
|
|
| Note that certain models require that you've been granted access to the weights on the Hugging Face Hub. |
|
|
| For example, to get access to the Gemma 2B model, you can do so by following the steps at <https://huggingface.co/google/gemma-2b>. After access is granted, you can find your HF hub token in <https://huggingface.co/settings/tokens>. |
|
|
| Once you've been granted access and obtained the access token you need to pass the additional `--access_token`: |
|
|
| ```bash |
| litgpt download google/gemma-2b \ |
| --access_token your_hf_token |
| ``` |
|
|
| |
|
|
| ## Finetunes and Other Model Variants |
|
|
| Sometimes you want to download the weights of a finetune of one of the models listed above. To do this, you need to manually specify the `model_name` associated to the config to use. For example: |
|
|
| ```bash |
| litgpt download NousResearch/Hermes-2-Pro-Mistral-7B \ |
| --model_name Mistral-7B-v0.1 |
| ``` |
|
|
| |
|
|
| ## Tips for GPU Memory Limitations |
|
|
| The `litgpt download` command will automatically convert the downloaded model checkpoint into a LitGPT-compatible format. In case this conversion fails due to GPU memory constraints, you can try to reduce the memory requirements by passing the `--dtype bf16-true` flag to convert all parameters into this smaller precision (however, note that most model weights are already in a bfloat16 format, so it may not have any effect): |
|
|
| ```bash |
| litgpt download <repo_id> |
| --dtype bf16-true |
| ``` |
|
|
| (If your GPU does not support the bfloat16 format, you can also try a regular 16-bit float format via `--dtype 16-true`.) |
|
|
| |
|
|
| ## Converting Checkpoints Manually |
|
|
| For development purposes, for example, when adding or experimenting with new model configurations, it may be beneficial to split the weight download and model conversion into two separate steps. |
|
|
| You can do this by passing the `--convert_checkpoint false` option to the download script: |
|
|
| ```bash |
| litgpt download <repo_id> \ |
| --convert_checkpoint false |
| ``` |
|
|
| and then calling the `convert_hf_checkpoint` command: |
|
|
| ```bash |
| litgpt convert_to_litgpt <repo_id> |
| ``` |
|
|
| |
|
|
| ## Downloading Tokenizers Only |
|
|
| In some cases we don't need the model weight, for example, when we are pretraining a model from scratch instead of finetuning it. For cases like this, you can use the `--tokenizer_only` flag to only download a model's tokenizer, which can then be used in the pretraining scripts: |
|
|
| ```bash |
| litgpt download TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T \ |
| --tokenizer_only true |
| ``` |
|
|
| and |
|
|
| ```bash |
| litgpt pretrain tiny-llama-1.1b \ |
| --data ... \ |
| --tokenizer_dir TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T/ |
| ``` |
|
|
