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README.md

@@ -10,8 +10,59 @@ tags:
 
 # llama.cpp b10355 CUDA runtime for Colab T4 (SM75)
 
-This public repository stores a reproducible llama.cpp `llama-server` runtime, not model weights. It was cross-built on Colab G4 with CUDA 12.8 and `CMAKE_CUDA_ARCHITECTURES=75`, then tested on an actual Colab Tesla T4.
+This public repository stores a reproducible `llama-server` runtime, not model
+weights. It was cross-built on an actual Google Colab G4 with CUDA 12.8 and
+`CMAKE_CUDA_ARCHITECTURES=75`, then downloaded without authentication and
+validated on an actual Colab Tesla T4.
 
-Archive SHA-256: `4469c6f18805f2cf2917df6bb8c0dbd37937344ab6c2e77c312cccdf7b145a9a`
+## Provenance
 
-See `manifest.json` for source commit, size, and binary hash.
+- Source: `ggml-org/llama.cpp`
+- Release: `b10355`
+- Commit: `dd1ea524333b1e697489067d7a4c39c60d32beee`
+- Build host: NVIDIA RTX PRO 6000 Blackwell Server Edition (Colab G4)
+- Target: Linux x86-64, CUDA 12.8, SM75, Colab Tesla T4
+- Archive: `llama-cpp-b10355-cuda128-sm75-colab.tar.gz`
+- Archive SHA-256: `4469c6f18805f2cf2917df6bb8c0dbd37937344ab6c2e77c312cccdf7b145a9a`
+- `llama-server` SHA-256: `f4f421a690bc949174752111bd20ac861fcdc2ba65fca361955edf7c67b53252`
+
+## Verified T4 result
+
+The public archive was restored at revision
+`5738166d5979c9d39d0b3cacbaa1c5c2a3d44b16` with no HF token. It loaded
+`unsloth/Qwen2.5-VL-7B-Instruct-GGUF` revision
+`68bb8bc4b7df5289c143aaec0ab477a7d4051aab`, using
+`UD-Q4_K_XL` plus `mmproj-BF16`.
+
+- GPU memory after load: 6,647 MiB
+- Model load to healthy server: 4.01 seconds on the verified rerun
+- Fixed workload: uncached 512 prompt tokens to fixed 128 output tokens,
+  batch 1, concurrency 1, five repetitions
+- Client end-to-end median: 34.53 tok/s
+- llama.cpp decode median: 39.20 tok/s
+- Five output hashes were identical
+- Multimodal smoke: 1024x768 candy photo, 4.00 seconds; correctly reported five
+  candies and the teal, orange, and green colors
+
+See `t4_validation.json` and `t4_setup_receipt.json` for raw evidence.
+
+## Restore
+
+```python
+from huggingface_hub import hf_hub_download
+import hashlib, tarfile
+
+path = hf_hub_download(
+    repo_id="WJO/llama-cpp-b10355-colab-t4-sm75",
+    filename="llama-cpp-b10355-cuda128-sm75-colab.tar.gz",
+    revision="5738166d5979c9d39d0b3cacbaa1c5c2a3d44b16",
+)
+assert hashlib.sha256(open(path, "rb").read()).hexdigest() == (
+    "4469c6f18805f2cf2917df6bb8c0dbd37937344ab6c2e77c312cccdf7b145a9a"
+)
+with tarfile.open(path, "r:gz") as archive:
+    archive.extractall("/content", filter="data")
+```
+
+The runtime expects the CUDA and standard system libraries included in the
+current Colab CUDA 12.8 image. Verify the archive and binary hashes before use.