Add Cosmos3-Edge to the TensorRT-LLM Reasoner cookbook

.agents/skills/cookbooks/cosmos3/README.md

@@ -388,25 +388,32 @@ supports this payload: poll `GET /v1/videos/{id}`, then download it from
 
 ## TensorRT-LLM Reasoner
 
-OpenAI-compatible **reasoning** server for image and video understanding. Run
-TensorRT-LLM in the prebuilt
-[`nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc22`](https://catalog.ngc.nvidia.com/orgs/nvidia/tensorrt-llm/containers/release)
-container, or follow the
-[TensorRT-LLM Generator source-build instructions](#tensorrt-llm-generator) to
-install TensorRT-LLM from source.
+OpenAI-compatible **reasoning** server for image and video understanding.
 Authenticate with Hugging Face before loading gated checkpoints.
 
-Install headless OpenCV in the environment that runs the server. Cosmos3
-Reasoner uses it to process vision inputs:
+**Cosmos3-Nano** and **Cosmos3-Super** run in the prebuilt
+[`nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc22`](https://catalog.ngc.nvidia.com/orgs/nvidia/tensorrt-llm/containers/release)
+container.
+
+**Cosmos3-Edge** uses `Cosmos3EdgeForConditionalGeneration`. Release images
+through `v1.4.0rc1`, including `release:1.3.0rc22`, do not include that
+architecture. Install TensorRT-LLM from upstream `main` at or after
+[`39a44fab`](https://github.com/NVIDIA/TensorRT-LLM/commit/39a44fab74f4cfc0d4d498be385e746fdd182872)
+(the merge of [PR 20307](https://github.com/NVIDIA/TensorRT-LLM/pull/20307)),
+following the
+[TensorRT-LLM Generator source-build instructions](#tensorrt-llm-generator),
+and run the Edge command below in that environment.
+
+Install headless OpenCV with the same interpreter that provides `trtllm-serve`.
+Cosmos3 Reasoner uses it to process vision inputs:
 
 ```bash
 python -m pip install opencv-python-headless
 ```
 
 ### Start the server
 
-Run one of these commands inside the release container or activated local
-environment.
+**Cosmos3-Nano** and **Cosmos3-Super** — run inside the release container above.
 
 **Cosmos3-Nano** (single GPU, port 8001):
 
@@ -429,6 +436,17 @@ trtllm-serve nvidia/Cosmos3-Super \
   --max_num_tokens 32768
 ```
 
+**Cosmos3-Edge** (single GPU, port 8001) — run in the source environment above,
+not in a release image through `v1.4.0rc1`:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+trtllm-serve nvidia/Cosmos3-Edge \
+  --host 0.0.0.0 \
+  --port 8001 \
+  --max_num_tokens 32768
+```
+
 The server exposes `/health` and the OpenAI-compatible API at
 `http://localhost:8001/v1`. See the
 [Reasoner TensorRT-LLM notebook](reasoner/run_with_tensorrt_llm.ipynb) for image

.agents/skills/cookbooks/cosmos3/reasoner/README.md

@@ -325,15 +325,20 @@ GPUs.
 Set up the environment and start the server with the shared
 [TensorRT-LLM Reasoner setup](../README.md#tensorrt-llm-reasoner). It covers the
 prebuilt release container or a local source installation, Hugging Face
-authentication, and launch commands for **Cosmos3-Nano** on one GPU or
-**Cosmos3-Super** on four GPUs.
+authentication, and launch commands for **Cosmos3-Nano** or **Cosmos3-Edge**
+on one GPU, or **Cosmos3-Super** on four GPUs. Nano and Super use the pinned
+`release:1.3.0rc22` image. Edge uses a source build of TensorRT-LLM `main` at
+or after `39a44fab`, which includes `Cosmos3EdgeForConditionalGeneration`.
+Release images through `v1.4.0rc1` do not.
 
 The [`run_with_tensorrt_llm.ipynb`](./run_with_tensorrt_llm.ipynb) notebook
 publishes the selected server on host port **8001** and waits for `/health`
 before running the request examples. Point standalone clients at
 `http://localhost:8001/v1`.
 
-Once the server is ready, query it with the OpenAI client:
+Once the server is ready, query it with the OpenAI client. This request matches
+the Cosmos Framework Edge example: one sentence, at most 1024 tokens, and
+sampling disabled.
 
 ```python
 from pathlib import Path
@@ -351,11 +356,15 @@ response = client.chat.completions.create(
             "role": "user",
             "content": [
                 {"type": "image_url", "image_url": {"url": image_url}},
-                {"type": "text", "text": "Caption the image in detail."},
+                {
+                    "type": "text",
+                    "text": "Describe what is happening in this image in one sentence.",
+                },
             ],
         }
     ],
-    max_tokens=4096,
+    max_tokens=1024,
+    temperature=0,
     seed=0,
 )
 
@@ -364,8 +373,10 @@ print(response.choices[0].message.content)
 
 ### Notebook walkthrough
 
-[`run_with_tensorrt_llm.ipynb`](./run_with_tensorrt_llm.ipynb) launches either
-**Cosmos3-Nano** or **Cosmos3-Super**, waits for the server to become healthy,
+[`run_with_tensorrt_llm.ipynb`](./run_with_tensorrt_llm.ipynb) launches
+**Cosmos3-Nano** or **Cosmos3-Super** from the pinned release image, or
+**Cosmos3-Edge** from a TensorRT-LLM `main` build at or after `39a44fab`.
+It waits for the server to become healthy
 and walks through image and video examples covering detailed captioning,
 temporal localization, embodied reasoning, common-sense reasoning, 2D
 grounding, describe-anything, action CoT trajectories, driving scenes,