Add agent skills for modelopt

.claude/skills/deployment/SKILL.md

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+---
+name: deployment
+description: Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says "deploy model", "serve model", "start vLLM server", "launch SGLang", "TRT-LLM deploy", "AutoDeploy", "benchmark throughput", "serve checkpoint", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint.
+---
+
+# Deployment Skill
+
+Serve a model checkpoint as an OpenAI-compatible inference endpoint. Supports vLLM, SGLang, and TRT-LLM (including AutoDeploy).
+
+## Quick Start
+
+Use the deploy script for the fastest path. It auto-detects quantization format from the checkpoint:
+
+```bash
+# Start vLLM server with a ModelOpt checkpoint
+scripts/deploy.sh start --model ./qwen3-0.6b-fp8
+
+# Start with SGLang and tensor parallelism
+scripts/deploy.sh start --model ./llama-70b-nvfp4 --framework sglang --tp 4
+
+# Start from HuggingFace hub
+scripts/deploy.sh start --model nvidia/Llama-3.1-8B-Instruct-FP8
+
+# Test the API
+scripts/deploy.sh test
+
+# Check status
+scripts/deploy.sh status
+
+# Stop
+scripts/deploy.sh stop
+```
+
+The script handles: GPU detection, quantization flag auto-detection (FP8 vs FP4), server lifecycle (start/stop/restart/status), health check polling, and API testing.
+
+## Decision Flow
+
+### 0. Identify the checkpoint
+
+Determine what the user wants to deploy:
+
+- **Local quantized checkpoint** (from ptq skill or manual export): look for `hf_quant_config.json` in the directory
+- **HuggingFace model hub** (e.g., `nvidia/Llama-3.1-8B-Instruct-FP8`): use directly
+- **Unquantized model**: deploy as-is (BF16) or suggest quantizing first with the ptq skill
+
+Check the quantization format if applicable:
+
+```bash
+cat <checkpoint_path>/hf_quant_config.json 2>/dev/null || echo "No quant config — unquantized or legacy format"
+```
+
+### 1. Choose the framework
+
+If the user hasn't specified a framework, recommend based on this priority:
+
+| Situation | Recommended | Why |
+|-----------|-------------|-----|
+| General use | **vLLM** | Widest ecosystem, easy setup, OpenAI-compatible |
+| Best SGLang model support | **SGLang** | Strong DeepSeek/Llama 4 support |
+| Maximum optimization | **TRT-LLM** | Best throughput via engine compilation |
+| Mixed-precision / AutoQuant | **TRT-LLM AutoDeploy** | Only option for AutoQuant checkpoints |
+
+Check the support matrix in `references/support-matrix.md` to confirm the model + format + framework combination is supported.
+
+### 2. Check the environment
+
+**GPU availability:**
+
+```bash
+python -c "import torch; [print(f'GPU {i}: {torch.cuda.get_device_name(i)}') for i in range(torch.cuda.device_count())] if torch.cuda.is_available() else print('no-gpu')"
+```
+
+**Framework installed?**
+
+```bash
+# vLLM
+python -c "import vllm; print(f'vLLM {vllm.__version__}')" 2>/dev/null || echo "vLLM not installed"
+
+# SGLang
+python -c "import sglang; print(f'SGLang {sglang.__version__}')" 2>/dev/null || echo "SGLang not installed"
+
+# TRT-LLM
+python -c "import tensorrt_llm; print(f'TRT-LLM {tensorrt_llm.__version__}')" 2>/dev/null || echo "TRT-LLM not installed"
+```
+
+If the framework is not installed, consult `references/setup.md` for installation instructions.
+
+**GPU memory estimate:**
+
+- BF16 model: `num_params × 2 bytes` (e.g., 8B model ≈ 16 GB)
+- FP8 model: `num_params × 1 byte` (e.g., 8B model ≈ 8 GB)
+- FP4 model: `num_params × 0.5 bytes` (e.g., 8B model ≈ 4 GB)
+- Add ~2-4 GB for KV cache and framework overhead
+
+If the model exceeds single GPU memory, use tensor parallelism (`-tp <num_gpus>`).
+
+### 3. Deploy
+
+Read the framework-specific reference for detailed instructions:
+
+| Framework | Reference file |
+|-----------|---------------|
+| vLLM | `references/vllm.md` |
+| SGLang | `references/sglang.md` |
+| TRT-LLM | `references/trtllm.md` |
+
+**Quick-start commands** (for common cases):
+
+#### vLLM
+
+```bash
+# Serve as OpenAI-compatible endpoint
+python -m vllm.entrypoints.openai.api_server \
+    --model <checkpoint_path> \
+    --quantization modelopt \
+    --tensor-parallel-size <num_gpus> \
+    --host 0.0.0.0 --port 8000
+```
+
+For NVFP4 checkpoints, use `--quantization modelopt_fp4`.
+
+#### SGLang
+
+```bash
+python -m sglang.launch_server \
+    --model-path <checkpoint_path> \
+    --quantization modelopt \
+    --tp <num_gpus> \
+    --host 0.0.0.0 --port 8000
+```
+
+#### TRT-LLM (direct)
+
+```python
+from tensorrt_llm import LLM, SamplingParams
+llm = LLM(model="<checkpoint_path>")
+outputs = llm.generate(["Hello, my name is"], SamplingParams(temperature=0.8, top_p=0.95))
+```
+
+#### TRT-LLM AutoDeploy
+
+For AutoQuant or mixed-precision checkpoints, see `references/trtllm.md`.
+
+### 4. Verify the deployment
+
+After the server starts, verify it's healthy:
+
+```bash
+# Health check
+curl -s http://localhost:8000/health
+
+# List models
+curl -s http://localhost:8000/v1/models | python -m json.tool
+
+# Test generation
+curl -s http://localhost:8000/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "<model_name>",
+        "prompt": "The capital of France is",
+        "max_tokens": 32
+    }' | python -m json.tool
+```
+
+All checks must pass before reporting success to the user.
+
+### 5. Benchmark (optional)
+
+If the user wants throughput/latency numbers, run a quick benchmark:
+
+```bash
+# vLLM benchmark
+python -m vllm.entrypoints.openai.api_server ... &  # if not already running
+
+python -m vllm.benchmark_serving \
+    --model <model_name> \
+    --port 8000 \
+    --num-prompts 100 \
+    --request-rate 10
+```
+
+Report: throughput (tok/s), latency p50/p99, time to first token (TTFT).
+
+## Error Handling
+
+| Error | Cause | Fix |
+|-------|-------|-----|
+| `CUDA out of memory` | Model too large for GPU(s) | Increase `--tensor-parallel-size` or use a smaller model |
+| `quantization="modelopt" not recognized` | vLLM/SGLang version too old | Upgrade: vLLM >= 0.10.1, SGLang >= 0.4.10 |
+| `hf_quant_config.json not found` | Not a ModelOpt-exported checkpoint | Re-export with `export_hf_checkpoint()`, or remove `--quantization` flag |
+| `Connection refused` on health check | Server still starting | Wait 30-60s for large models; check logs for errors |
+| `modelopt_fp4 not supported` | Framework doesn't support FP4 for this model | Check support matrix in `references/support-matrix.md` |
+
+## Success Criteria
+
+1. Server process is running and healthy (`/health` returns 200)
+2. Model is listed at `/v1/models`
+3. Test generation produces coherent output
+4. Server URL and port are reported to the user
+5. If benchmarking was requested, throughput/latency numbers are reported

.claude/skills/deployment/references/setup.md

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+# Deployment Environment Setup
+
+## Framework Installation
+
+### vLLM
+
+```bash
+pip install vllm
+```
+
+Minimum version: 0.10.1
+
+### SGLang
+
+```bash
+pip install "sglang[all]"
+```
+
+Minimum version: 0.4.10
+
+### TRT-LLM
+
+TRT-LLM is best installed via NVIDIA container:
+
+```bash
+docker pull nvcr.io/nvidia/tensorrt-llm/release:<version>
+```
+
+Or via pip (requires CUDA toolkit):
+
+```bash
+pip install tensorrt-llm
+```
+
+Minimum version: 0.17.0
+
+## SLURM Deployment
+
+For SLURM clusters, deploy inside a container. Container flags MUST be on the `srun` line:
+
+```bash
+#!/bin/bash
+#SBATCH --job-name=deploy
+#SBATCH --account=<account>
+#SBATCH --partition=<partition>
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --gpus-per-node=<num_gpus>
+#SBATCH --time=04:00:00
+#SBATCH --output=deploy_%j.log
+
+srun \
+    --container-image="<path/to/container.sqsh>" \
+    --container-mounts="<data_root>:<data_root>" \
+    --container-workdir="<workdir>" \
+    --no-container-mount-home \
+    bash -c "python -m vllm.entrypoints.openai.api_server \
+        --model <checkpoint_path> \
+        --quantization modelopt \
+        --tensor-parallel-size <num_gpus> \
+        --host 0.0.0.0 --port 8000"
+```
+
+To access the server from outside the SLURM node, note the allocated hostname:
+
+```bash
+squeue -u $USER -o "%j %N %S"  # Get the node name
+# Then SSH tunnel or use the node's hostname directly
+```
+
+## Docker Deployment
+
+### vLLM with ModelOpt
+
+A Dockerfile is available at `examples/vllm_serve/Dockerfile`:
+
+```bash
+docker build -f examples/vllm_serve/Dockerfile -t vllm-modelopt .
+
+docker run --gpus all -p 8000:8000 vllm-modelopt \
+    python -m vllm.entrypoints.openai.api_server \
+        --model <checkpoint_path> \
+        --quantization modelopt \
+        --host 0.0.0.0 --port 8000
+```

.claude/skills/deployment/references/sglang.md

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+# SGLang Deployment Reference
+
+## Requirements
+
+- SGLang >= 0.4.10
+- `pip install sglang[all]`
+
+## Server Deployment
+
+### As OpenAI-compatible server
+
+```bash
+python -m sglang.launch_server \
+    --model-path <checkpoint_path> \
+    --quantization modelopt \
+    --tp <num_gpus> \
+    --host 0.0.0.0 --port 8000
+```
+
+For NVFP4 checkpoints, use `--quantization modelopt_fp4`.
+
+### As Python API
+
+```python
+import sglang as sgl
+
+llm = sgl.Engine(model_path="<checkpoint_path>", quantization="modelopt")
+# For FP4: quantization="modelopt_fp4"
+
+sampling_params = {"temperature": 0.8, "top_p": 0.95}
+outputs = llm.generate(["Hello, my name is"], sampling_params)
+
+for output in outputs:
+    print(f"Generated: {output['text']}")
+```
+
+### From HuggingFace Hub
+
+```python
+import sglang as sgl
+
+llm = sgl.Engine(model_path="nvidia/Llama-3.1-8B-Instruct-FP8", quantization="modelopt")
+outputs = llm.generate(["What is AI?"], {"temperature": 0.8})
+```
+
+## Speculative Decoding
+
+SGLang supports speculative decoding with EAGLE and EAGLE3 models:
+
+```bash
+python -m sglang.launch_server \
+    --model-path <target_model> \
+    --speculative-algorithm EAGLE \
+    --speculative-draft-model-path <draft_model> \
+    --speculative-num-steps 3 \
+    --speculative-eagle-topk 4 \
+    --tp <num_gpus> \
+    --host 0.0.0.0 --port 8000
+```
+
+Reference: `examples/specdec_bench/specdec_bench/models/sglang.py`
+
+## Key SGLang Flags
+
+| Flag | Description |
+|------|-------------|
+| `--model-path` | Path to checkpoint or HF model ID |
+| `--quantization` | `modelopt` (FP8) or `modelopt_fp4` (FP4) |
+| `--tp` | Tensor parallelism size |
+| `--ep` | Expert parallelism (for MoE models) |
+| `--enable-torch-compile` | Enable torch.compile for better perf |
+| `--cuda-graph-max-bs` | Max batch size for CUDA graphs |
+| `--attention-backend` | `flashinfer` (default) or `triton` |
+
+## Common Issues
+
+| Issue | Fix |
+|-------|-----|
+| `quantization="modelopt"` not recognized | Upgrade SGLang to >= 0.4.10 |
+| DeepSeek FP4 not working | Check support matrix — SGLang FP4 support varies by model |
+| OOM on startup | Increase `--tp` or reduce `--max-total-tokens` |