docs: sync model card with NInfer master

README.md

@@ -1,3 +1,126 @@
 ---
-license: apache-2.0
+library_name: ninfer
+pipeline_tag: image-text-to-text
+inference: false
+license: mit
+base_model:
+- zai-org/GLM-5.3-Flash
+- nvidia/GLM-5.3-Flash-NVFP4
+base_model_relation: quantized
+tags:
+- ninfer
+- glm5next
+- nvfp4
+- q8
+- mtp
+- blackwell
+- dgx-spark
+- multimodal
+- conversational
+- cuda
 ---
+
+# GLM-5.3-Flash for NInfer
+
+A mixed-precision NInfer v3 artifact of
+[GLM-5.3-Flash](https://huggingface.co/zai-org/GLM-5.3-Flash), converted from
+[NVIDIA's NVFP4 checkpoint](https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4).
+It includes Text, Vision, native MTP and an additional full-vocabulary Q4 draft head.
+
+The native NInfer architecture is GLM5Next, combining KDA linear attention, Kpool sparse MLA,
+mHC and MoE. The artifact includes the source tokenizer, GLM chat template and processor resources.
+Use it directly with [NInfer](https://github.com/Neroued/ninfer) for CLI generation or
+OpenAI-/Anthropic-compatible serving on **two DGX Sparks**.
+
+## Download
+
+```bash
+hf download neroued/GLM-5.3-Flash-NInfer \
+  --include 'glm5_3_flash.ninfer*' \
+  --local-dir models/GLM-5.3-Flash-NInfer
+```
+
+The complete artifact is **178.33 GiB** across six files:
+
+```text
+glm5_3_flash.ninfer
+glm5_3_flash.ninfer.part-0001
+glm5_3_flash.ninfer.part-0002
+glm5_3_flash.ninfer.part-0003
+glm5_3_flash.ninfer.part-0004
+glm5_3_flash.ninfer.part-0005
+```
+
+Keep all parts beside the entry file and place the complete set on each node. Pass only
+`glm5_3_flash.ninfer` to NInfer; rank-specific weight slicing happens during loading.
+File sizes and the artifact identity are in [artifact-manifest.json](artifact-manifest.json).
+
+## Representation
+
+| Weights | Stored representation |
+|---|---|
+| Main routed experts and first three dense MLP layers | Imported NVIDIA NVFP4, with calibrated scales |
+| KDA Q/K/V/O, MLA output and shared-expert projections | Q8 A16 |
+| MTP routed experts | Block-FP8 converted from source BF16 |
+| Main head, embedding, mHC, Vision and other retained parameters | BF16; source FP32 KDA/control fields remain FP32 |
+| Optimized MTP head | Additional full-vocabulary Q4 A16; Main head stays BF16 |
+
+The [`glm5next` recipe](https://github.com/Neroued/ninfer/blob/master/tools/convert/official_recipes.py)
+retains imported Main NVFP4 codes/scales and performs the additional quantization above.
+The optimized draft head does not crop the vocabulary.
+
+## Run on two Sparks
+
+Build the current NInfer revision on both nodes with NCCL development dependencies installed:
+
+```bash
+cmake --preset dgx-spark-release -DNINFER_ENABLE_DISTRIBUTED=ON
+cmake --build build/dgx-spark-release -j
+```
+
+Full dependency and checkout instructions are in the [build guide](https://github.com/Neroued/ninfer#build).
+From the repository root, prepare the deployment config:
+
+```bash
+cp examples/configs/spark2-glm5_3-flash.toml ninfer.local.toml
+# Edit coordinator/worker artifact paths, worker executable paths and network addresses.
+./build/dgx-spark-release/apps/ninfer-serve --config ninfer.local.toml
+```
+
+The [example](https://github.com/Neroued/ninfer/blob/master/examples/configs/spark2-glm5_3-flash.toml)
+uses TP2 with attention DP2, FP8 KV, Vision, MTP3, the optimized head, CUDA Graph decode and up to
+eight active requests. Its logical context limit is **1,048,576 tokens**. The coordinator launches
+both workers, using SSH for the remote node. Clients connect to its single HTTP endpoint.
+
+Experts use TP by default (`ep = 1`); set `ep = 2` to select whole-expert EP while retaining TP2
+and attention DP2 elsewhere. Keep all six artifact files on each node. The complete artifact does
+not fit on one Spark. Runtime allocations also include workspace and the shared KV/State pool;
+read the startup capacity estimate for the selected configuration.
+
+For CLI use, run `apps/ninfer --config ninfer.local.toml --prompt "..." --max-tokens 2048` on the
+coordinator. Detailed startup and network instructions are in the
+[deployment guide](https://github.com/Neroued/ninfer/tree/master/examples/configs).
+
+## Model options
+
+- Thinking stays enabled. Supported reasoning efforts are `low`, `high` and `max`; the template
+  supplies the default. `--no-thinking` and effort `none` are unsupported.
+- Native MTP supports 1..5 drafts. `--proposal-head optimized` selects the Q4 draft head;
+  the target head stays BF16. Draft count can be adjusted for workload and concurrency.
+- Latent KV uses `--kv-dtype bf16` or `fp8`. The Kpool indexer uses FP8 by default;
+  `NINFER_KPOOL_INDEXER_BF16=1` selects BF16 indexer storage independently.
+- Vision, tool calls, constrained output, streaming and prefix reuse use the same public Engine
+  and protocol endpoints as text generation. Enable Vision at startup for image/video input.
+- GLM uses its own chat template and tool framing; retain the included GLM frontend resources.
+
+DFlash and DFlash2 are not used by this architecture. NInfer returns generated tool calls to the
+client; it does not execute them.
+
+## Sources and license
+
+The primary conversion source is
+[nvidia/GLM-5.3-Flash-NVFP4](https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4), revision
+`da920bb0b9f4a06727223a349e55468e38352348`, based on
+[zai-org/GLM-5.3-Flash](https://huggingface.co/zai-org/GLM-5.3-Flash).
+The model weights use the upstream [MIT license](https://huggingface.co/zai-org/GLM-5.3-Flash/blob/main/LICENSE).
+[NInfer](https://github.com/Neroued/ninfer) is Apache-2.0-licensed software.

artifact-manifest.json

@@ -0,0 +1,59 @@
+{
+  "artifact": {
+    "entry": "glm5_3_flash.ninfer",
+    "container_version": 3,
+    "artifact_id": "7f4725ce4f784697a7fe0d57a81e5de2",
+    "name": "glm5.3-flash",
+    "architecture": "Glm5NextForCausalLM",
+    "components": [
+      "text",
+      "vision",
+      "mtp"
+    ],
+    "total_file_bytes": 191484328832,
+    "payload_bytes": 191481699200,
+    "files": [
+      {
+        "name": "glm5_3_flash.ninfer",
+        "bytes": 32000000000
+      },
+      {
+        "name": "glm5_3_flash.ninfer.part-0001",
+        "bytes": 32000000000
+      },
+      {
+        "name": "glm5_3_flash.ninfer.part-0002",
+        "bytes": 32000000000
+      },
+      {
+        "name": "glm5_3_flash.ninfer.part-0003",
+        "bytes": 32000000000
+      },
+      {
+        "name": "glm5_3_flash.ninfer.part-0004",
+        "bytes": 32000000000
+      },
+      {
+        "name": "glm5_3_flash.ninfer.part-0005",
+        "bytes": 31484328832
+      }
+    ]
+  },
+  "recipe": "glm5next",
+  "sources": [
+    {
+      "repo_id": "nvidia/GLM-5.3-Flash-NVFP4",
+      "revision": "da920bb0b9f4a06727223a349e55468e38352348",
+      "role": "Primary checkpoint and frontend resources"
+    }
+  ],
+  "object_counts_by_format": {
+    "q4_g64_fp16": 1,
+    "q8_g32_fp16": 166,
+    "bf16": 954,
+    "nvfp4": 90,
+    "fp32": 145,
+    "fp8_e4m3fn_block128x128_bf16": 2
+  },
+  "optimized_proposal_domain": "full"
+}