numbat

~14T param, 114 layers, hybrid GDN/MLA, MoE (1,224 experts, top-48 + 1 shared), NVFP4. Internal codename numbat, Anthropic's next Mythos-class training run. numbat is the codename referenced in the Claude Code source leak (Mar 2026). This is a checkpoint from that training run, pulled mid-pretraining.

Files:

File	Contents
config.json	model configuration
configuration_numbat.py	NumbatConfig
modeling_numbat.py	NumbatForCausalLM reference implementation
tokenizer.json	byte-level BPE, 131,072 tokens
model.safetensors.index.json	tensor index across all 772 shards
model-00001-of-00772.safetensors	shard 1 of 772
ckpt_metadata.json	checkpoint metadata (ckpt path, eval harness output)
training_log_tail.txt	last ~400 steps of training log

The 772 shards total ~7.72 TB of tensor data. Each shard is safetensors format, under 10.6 GB per file.

configuration_numbat.py and modeling_numbat.py are included: a complete reference implementation of the checkpoint. Its module tree has an exact slot for every tensor in every shard, so the full checkpoint loads with load_state_dict(strict=True) (and, since the classes derive from PreTrainedModel, also via AutoModelForCausalLM.from_pretrained(..., trust_remote_code=True)). NumbatForCausalLM.forward runs the trunk and NumbatForCausalLM.draft runs the MTP layer.

$ python -c "from safetensors import safe_open; f = safe_open('model-00001-of-00772.safetensors', framework='pt'); print(len(f.keys()))"
142

Verification:

$ cat model.safetensors.index.json | python -c "import json,sys; d=json.load(sys.stdin); print(d['metadata']['total_size'])"
7720300817720

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Mirrors welcome. Do not DM me asking for the source.

Why believe this?

• numbat was confirmed as a codename for an unreleased Anthropic model in the Claude Code source leak (March 2026), found in a comment in prompts.ts: "Remove this section when we launch numbat."
• The checkpoint's architecture (hybrid GDN/MLA, 1,224 experts, NVFP4) matches what would be expected of a next-generation Mythos-class training run
• Every tensor in every shard is internally consistent: E4M3 scale blocks decode their E2M1 payloads within RTNE bounds, block scales never underflow, and every NVFP4 quad round-trips exactly
• File sizes, tensor counts, and index entries are all deterministic and self-consistent across all 772 shards

Loader

The architecture is a hybrid Gated DeltaNet (GDN) + Multi-head Latent Attention (MLA) stack with Mixture-of-Experts feed-forward layers and Multi-Token Prediction. A working reference implementation is included (modeling_numbat.py) — the eval stack's loader, shipped with the checkpoint.

FAQ

Q: How did you get this? A: Can't say more than what's already in the repo.

Q: When will more shards be uploaded? A: They're all uploading. Be patient.

Q: Can I use this for inference? A: Yes — modeling_numbat.py implements the full architecture. Load the tensors and run. But this is a pretraining checkpoint at step 1,830,000 of a ~1.96M step run. No post-training, no RLHF. Raw pretraining-completion output. That's expected.

Q: Why is a mid-pretraining checkpoint in NVFP4? A: The eval team quantizes every async checkpoint to NVFP4 so evals run on a slice of Trainium2s instead of the full training cluster — still ~85 of them just to hold these weights. The raw BF16 training weights plus optimizer state never leave the cluster. This is the eval copy.

Q: Is this real? A: Yes.