Instructions to use N8Programs/lil-bard-checkpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use N8Programs/lil-bard-checkpts with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("N8Programs/lil-bard-checkpts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from N8Programs/lil-bard-checkpts: direct link, hf CLI and curl.
- Browser
- Download file 2.05 kB
-
https://huggingface.co/N8Programs/lil-bard-checkpts/resolve/main/README.md
- Command line
-
hf download hf://N8Programs/lil-bard-checkpts/README.md
-
curl -L -o README.md https://huggingface.co/N8Programs/lil-bard-checkpts/resolve/main/README.md
2.05 kB
| library_name: transformers | |
| language: | |
| - en | |
| tags: | |
| - spark-gpt | |
| - qwen3-moe | |
| - from-scratch | |
| - stories | |
| - checkpoints | |
| # Lil Bard historical checkpoints | |
| This repository contains 25 loadable Transformers checkpoints from the | |
| pretraining run for [`N8Programs/lil-bard`](https://huggingface.co/N8Programs/lil-bard). | |
| The final step-25,485 model is kept in that separate repository so ordinary | |
| users do not download the full checkpoint history. | |
| Checkpoints span step 1,000 through step 25,000 in increments of 1,000. Each | |
| subfolder contains a complete `Qwen3MoeForCausalLM` export and tokenizer. | |
| ## Loading a checkpoint | |
| ```python | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| repo_id = "N8Programs/lil-bard-checkpts" | |
| subfolder = "step-0001000" | |
| tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| repo_id, | |
| subfolder=subfolder, | |
| dtype=torch.bfloat16, | |
| device_map="auto", | |
| ) | |
| ``` | |
| Available subfolders are `step-0001000`, `step-0002000`, ..., through | |
| `step-0025000`. | |
| These historical exports retain the fused expert-tensor layout used during the | |
| run. They are tested with Transformers 5.11.0. Transformers 4.57.1 users should | |
| use the cross-version final checkpoint in `N8Programs/lil-bard`. | |
| ## Run summary | |
| Lil Bard is a 172,052,992-parameter English story MoE with 58,806,784 active | |
| parameters per token, 8 experts with top-2 routing, and an 8,192-entry | |
| whole-document byte-level BPE tokenizer. It was pretrained from scratch for | |
| 2,492,032,616 real loss tokens on two NVIDIA GB10 systems. | |
| The final model, architecture and data details, evaluations, usage example, | |
| and limitations are documented in the | |
| [`N8Programs/lil-bard` model card](https://huggingface.co/N8Programs/lil-bard). | |
| The complete training trace is available in the | |
| [`lil_bard_moe_8x2_full` W&B run](https://wandb.ai/n8programs/sparkgpt/runs/mxk8gln1). | |
| Only model exports are included here. The larger optimizer/scheduler resume | |
| checkpoints are not uploaded. | |