A ~14T param model appeared on Hugging Face claiming to be Mythos-class training run leaked weights. From the card: 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 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 single-device reference implementation is included (modeling_numbat.py); the eval runs used the internal Neuron-parallel runtime, but this port consumes the same tensors and round-trips every shard. 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. What do you guys think?   submitted by   /u/LatentSpacer [link]   [comments]