# 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 `` with the model's repository ID. For example: ```bash litgpt download ``` 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 ```   ## 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 . After access is granted, you can find your HF hub token in . 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 --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 \ --convert_checkpoint false ``` and then calling the `convert_hf_checkpoint` command: ```bash litgpt convert_to_litgpt ```   ## 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/ ```