Add AGILLM-4 dblock single-file trainer

README.md

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+---
+library_name: pytorch
+tags:
+- pytorch
+- transformer
+- language-model
+- long-context
+- agillm
+- dblock
+- single-file
+- experimental
+---
+
+# AGILLM-4 dblock single-file
+
+This repo packages the live AGILLM-4 dblock trainer as one runnable Python file:
+
+- `agillm4_dblock_single_file.py`
+
+It was generated on `2026-05-31T14:54:55Z` by mechanically inlining the live VastAI training sources:
+
+- `fused_ce.py`
+- `anchor_memory.py`
+- `dblocks_train.py`
+- `nB300_agillm4.py`
+
+The original live command used `nB300_agillm4.py train`. This single-file build keeps that CLI surface, but registers in-memory shims for the former helper modules so the dblock path can still import `dblocks_train`, `anchor_memory`, and `fused_ce` internally.
+
+See `single_file_manifest.json` for source hashes from the generated build.
+
+Example shape:
+
+```bash
+python agillm4_dblock_single_file.py train --preset agillm4_floor --dblock ...
+```
+
+This is experimental training code, not a polished inference package.

single_file_manifest.json

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+{
+  "generated_at_utc": "2026-05-31T14:54:55Z",
+  "source_dir": "/workspace/agillm-4",
+  "source_files": [
+    {
+      "path": "fused_ce.py",
+      "bytes": 2435,
+      "sha256": "392d9ccc46cf5e750841a10d8244da41c42cdf93930c889f7027b7de74f3fd9d"
+    },
+    {
+      "path": "anchor_memory.py",
+      "bytes": 3114,
+      "sha256": "cfb41574628fe1a452dc29ab2c9c4147c5f4d128724306f25e229483939c558a"
+    },
+    {
+      "path": "dblocks_train.py",
+      "bytes": 16929,
+      "sha256": "a28b7d733c3c4b151dd5af2f4b1d3f3665da4efb424cbfd6d89ff012dcbe51cd"
+    },
+    {
+      "path": "nB300_agillm4.py",
+      "bytes": 147310,
+      "sha256": "6dd461d970364483ec0df6825b7177765dacda2115a1517e21ff53b51a34ca11"
+    }
+  ],
+  "entrypoint": "agillm4_dblock_single_file.py",
+  "repo_purpose": "Single-file AGILLM-4 dblock trainer packaging"
+}