Initial release

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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

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NOTICE

@@ -0,0 +1,25 @@
+xLLM Part I model artifacts
+
+The model weights are distributed under the Apache License, Version 2.0.
+A copy is provided in LICENSE. The xLLM code has its own license in the
+repository root; third-party code retains its original notices.
+
+Tokenizer attribution
+
+The tokenizer is derived from IFM/K2-Horizon-3.7B and retains its upstream
+Apache-2.0 license.
+Source revision: 86a26d7bf092484faa047121d5816f037d4a8ffa
+https://huggingface.co/IFM/K2-Horizon-3.7B/tree/86a26d7bf092484faa047121d5816f037d4a8ffa
+
+Part I modifications:
+- tokenizer.json retains the final ByteLevel post-processor from the source
+  Sequence, removing automatic begin-of-text insertion.
+- special_tokens_map.json stores each token object's content as a string.
+- tokenizer_config.json is copied unchanged.
+The vocabulary, token IDs, merges and added-token attributes are unchanged.
+
+Artifact preparation:
+- Source model tensors are converted to BF16 and stored in Safetensors shards.
+- config.json holds the checkpoint's values of the model fields its recipe
+  sets and the tokenizer settings; tokenizer.path points to this artifact's
+  tokenizer directory.

README.md

@@ -0,0 +1,118 @@
+---
+license: apache-2.0
+library_name: xllm
+pipeline_tag: text-generation
+tags:
+- xllm
+- looped-language-model
+- paper-part1
+- dense
+---
+
+# Dense D112 · 500B tokens
+
+Checkpoint of the `dense_d112` recipe at the 500B-token endpoint (optimizer step 119,210),
+from *Towards Looped Models Done Right. Part I: Topology, Input Injection, Recurrent-State Organization*.
+
+> [!IMPORTANT]
+> **Loading this checkpoint requires the xLLM code.** The weights are stored in xLLM's native
+> format (BF16 Safetensors with an xLLM `config.json` and `artifact_manifest.json`). This is not a
+> Hugging Face `transformers` checkpoint: `AutoModel.from_pretrained` cannot load it.
+> Get the code at [https://github.com/ifm-ai/xllm-loop](https://github.com/ifm-ai/xllm-loop) and load the directory with `xllm.paper_part1.native_inference.load_native_model`.
+
+## Model details
+
+| Field | Value |
+|---|---|
+| Recipe | `dense_d112` |
+| Family | Dense feedforward control |
+| Architecture (`model.arch`) | `transformer` |
+| Endpoint | `500b`: optimizer step 119,210, 500,002,979,840 training tokens |
+| Parameters (resident) | 3,676,915,200 |
+| Parameters (activated per token) | 3,676,915,200 |
+| Training seed | 1 |
+| Optimizer | AdamW; weight decay 0.1 |
+| LR schedule | cosine; 200-step warmup; 119,210-step horizon (the 500B endpoint) |
+| Global batch | 512 sequences × 8,192 tokens = 4,194,304 tokens/step |
+| Weights | BF16 Safetensors, 2 shards of at most 5 GiB |
+| Tokenizer | `tokenizer/`, derived from IFM/K2-Horizon-3.7B (see `NOTICE`) |
+| Other endpoint | [336B tokens](https://huggingface.co/IFM/LoopedLM-P1-dense-d112-336b) |
+
+## Download
+
+```bash
+hf download IFM/LoopedLM-P1-dense-d112-500b --local-dir models/dense_d112/500b
+```
+
+The xLLM loader checks the directory against `artifact_manifest.json`: it rejects symbolic links
+and files the manifest does not list, apart from the `.gitattributes` file and the
+`.cache/huggingface/` folder that `hf download --local-dir` adds. Download into a directory as
+above, not into the Hub cache (`~/.cache/huggingface/hub`), whose files are symbolic links.
+
+## Use
+
+Native inference requires a CUDA GPU and FlashAttention 3; the
+[xLLM repository](https://github.com/ifm-ai/xllm-loop) documents installation.
+
+```bash
+export ENABLE_FLASH_ATTENTION_3=true
+```
+
+```python
+from xllm.paper_part1.native_inference import generate_native, load_native_model
+
+model, tokenizer, config = load_native_model("models/dense_d112/500b")
+tokens = generate_native(model, tokenizer, ["The capital of France is"],
+                         max_gen_len=32, use_sampling=False)
+print(tokenizer.decode(tokens[0]))
+```
+
+The loader verifies the directory against `artifact_manifest.json`, then strictly loads tensor
+names, shapes and dtypes. Evaluate with the paper protocol:
+
+```bash
+ENABLE_FLASH_ATTENTION_3=true python eval_paper_part1.py --artifact models/dense_d112/500b \
+    --tasks-root /path/to/composite-eval-root --output /path/to/evaluation-output \
+    --protocol paper-part1
+```
+
+Retrain from scratch with the same recipe (the base config holds the data, tokenizer, logging and
+checkpoint-retention settings):
+
+```bash
+ENABLE_FLASH_ATTENTION_3=true python train_paper_part1.py --recipe dense_d112 --target 500b \
+    --base-config /path/to/base.json --dump-dir /path/to/new-run
+```
+
+## Files
+
+- `config.json`: the recipe's `model` fields with the training checkpoint's values, and the
+  `tokenizer` settings with `tokenizer.path` set to `tokenizer/`.
+- `model.safetensors.index.json` and `model-*-of-*.safetensors`: BF16 weights; no tensor is split
+  across shards.
+- `tokenizer/`: `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`.
+- `artifact_manifest.json`: size and SHA-256 of every file.
+- `LICENSE`, `NOTICE`.
+
+Optimizer, dataloader and training state are not included; resuming a run mid-training needs the
+original training checkpoint.
+
+## Paper and citation
+
+*Towards Looped Models Done Right. Part I: Topology, Input Injection, Recurrent-State Organization*: https://huskydoge.github.io/husky-blog/posts/recursive_models/towards-looped-models-done-right/
+
+```bibtex
+@misc{huang2026loopedmodels,
+  title  = {Towards Looped Models Done Right. Part I: Topology, Input Injection, Recurrent-State Organization},
+  author = {Benhao Huang and Chufan Shi and Junlin Chen and Shicheng Wen and Zhengzhong Liu and Eric Xing and Xuezhe Ma},
+  year   = {2026},
+  url    = {https://huskydoge.github.io/husky-blog/posts/recursive_models/towards-looped-models-done-right/}
+}
+```
+
+## License
+
+The weights and tokenizer are released under the Apache License 2.0 (`LICENSE`). `NOTICE`
+records the tokenizer's attribution to
+[IFM/K2-Horizon-3.7B](https://huggingface.co/IFM/K2-Horizon-3.7B/tree/86a26d7bf092484faa047121d5816f037d4a8ffa)
+and its modifications. The xLLM code is distributed under its own license.

artifact_manifest.json

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+{
+  "config_projection": "model_tokenizer_only",
+  "files": [
+    {
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+      "size_bytes": 11357
+    },
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+      "size_bytes": 1130
+    },
+    {
+      "path": "README.md",
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+      "size_bytes": 4840
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+      "size_bytes": 2034
+    },
+    {
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+    {
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+      "sha256": "166c7678b79072cf334dca4de63c5ac4d0a33e1912caad58d049ce7dcf3bd219",
+      "size_bytes": 72409
+    },
+    {
+      "path": "tokenizer/special_tokens_map.json",
+      "sha256": "431aa39c50202573663aad96e19dd5195091bd03c8e9b95f0dbecbb9c67b95da",
+      "size_bytes": 70
+    },
+    {
+      "path": "tokenizer/tokenizer.json",
+      "sha256": "e0046eb3a1adc60249aa5310d1adfdb7ae5159f4a22126ac44b260fb424ac944",
+      "size_bytes": 20636512
+    },
+    {
+      "path": "tokenizer/tokenizer_config.json",
+      "sha256": "2481b319071cc584e32c57a7e49deef00753a62b4cba52861d663a00f9673c3e",
+      "size_bytes": 120524
+    }
+  ],
+  "format": "xllm-native-bf16-safetensors",
+  "manifest_sha256": "339400a092820f005f805cd9988aed492ed256a538a0878458c60b51d2b63290",
+  "model": {
+    "dtype": "bfloat16",
+    "endpoint": "500b",
+    "index": "model.safetensors.index.json",
+    "name": "dense_d112",
+    "shards": 2,
+    "tensor_count": 1011
+  },
+  "release": "paper-part1",
+  "schema_version": 1,
+  "source": {
+    "checkpoint_id": "checkpoint_0119210",
+    "model_card_sha256": "aab0d6c6345b089a39b864ef968e0321aea1d8ceeabd3386285c9d514c9fda7b"
+  }
+}

config.json

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+{
+  "model": {
+    "apply_attn_gate": false,
+    "apply_bias_term": true,
+    "apply_rmsnorm": true,
+    "arch": "transformer",
+    "attention_dropout": 0.0,
+    "attn_gate_func": "silu",
+    "causal_attn_backend": "flash",
+    "chunk_size": 1024,
+    "dense_prelude_diagonal_a_init": 1.0,
+    "dense_prelude_diagonal_dt_init": 1.0,
+    "dense_prelude_input_injection": "none",
+    "dense_prelude_input_layers": "",
+    "dense_prelude_source_layer": -1,
+    "dropout": 0.0,
+    "expert_inter_dim": 0,
+    "ffn_hidden_dim": 4352,
+    "fused_block": true,
+    "fused_output_layer": true,
+    "head_dim": 64,
+    "hidden_dropout": 0.0,
+    "init_embed_std": null,
+    "init_logits_std": null,
+    "init_mode": "gaussian",
+    "init_std": 0.02,
+    "layernorm_num_groups": 1,
+    "layerwise_ckpt": false,
+    "loop_diagonal_a_init": 1.0,
+    "loop_diagonal_dt_init": 1.0,
+    "loop_end_layers": 112,
+    "loop_input_injection": "none",
+    "loop_start_layer": 0,
+    "loop_times": 1,
+    "memory_efficient_norm": false,
+    "model_dim": 1536,
+    "moe_expert_backend": "sequential",
+    "moe_router_bias": false,
+    "moe_router_bias_update_rate": null,
+    "moe_router_scaling_factor": null,
+    "moe_router_score_func": "sigmoid",
+    "norm_affine": true,
+    "num_activated_experts": 0,
+    "num_activated_values": 0,
+    "num_dense_layers": null,
+    "num_experts": 0,
+    "num_heads": 24,
+    "num_kv_heads": 6,
+    "num_layers": 112,
+    "num_shared_experts": 0,
+    "num_values": 0,
+    "output_size": -1,
+    "qknorm": false,
+    "recompute_attention": true,
+    "recompute_fc1_out": true,
+    "recompute_fc3_out": true,
+    "recompute_logits": false,
+    "recompute_q": true,
+    "recompute_router": false,
+    "recompute_v": true,
+    "rmsnorm_eps": 1e-06,
+    "rope_base": 1000000.0,
+    "rope_head_dim": 64,
+    "scale_emb": false,
+    "swiglu": true,
+    "vocab_size": 250624
+  },
+  "tokenizer": {
+    "num_reserved_special_tokens": 0,
+    "path": "tokenizer",
+    "template_dict_json": null,
+    "type": "huggingface"
+  }
+}

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