Resolve merge conflicts and simplify README (#796)

Remove the detailed dmrlet documentation section that was added during
merge conflict resolution. This includes the feature comparison table,
extensive usage examples, supported backends list, and architecture
diagram. The README now contains only a brief introduction to dmrlet
with a minimal usage example to keep the documentation concise and
focused on the core docker-model-runner project.

Signed-off-by: Eric Curtin <eric.curtin@docker.com>

Author: ericcurtin

README.md

@@ -402,21 +402,10 @@ in the form of [a Helm chart and static YAML](charts/docker-model-runner/README.
 If you are interested in a specific Kubernetes use-case, please start a
 discussion on the issue tracker.
 
-<<<<<<< Updated upstream
-=======
 ## dmrlet: Container Orchestrator for AI Inference
 
 dmrlet is a purpose-built container orchestrator for AI inference workloads. Unlike Kubernetes, it focuses exclusively on running stateless inference containers with zero configuration overhead. Multi-GPU mapping "just works" without YAML, device plugins, or node selectors.
 
-### Key Features
-
-| Feature | Kubernetes | dmrlet |
-|---------|------------|--------|
-| Multi-GPU setup | Device plugins + node selectors + resource limits YAML | `dmrlet serve llama3 --gpus all` |
-| Config overhead | 50+ lines of YAML minimum | Zero YAML, CLI-only |
-| Time to first inference | Minutes (pod scheduling, image pull) | Seconds (model already local) |
-| Model management | External (mount PVCs, manage yourself) | Integrated with Docker Model Runner store |
-
 ### Building dmrlet
 
 ```bash
@@ -429,91 +418,12 @@ go build -o dmrlet ./cmd/dmrlet
 
 ### Usage
 
-**Start the daemon:**
-```bash
-# Start in foreground
-dmrlet daemon
-
-# With custom socket path
-dmrlet daemon --socket /tmp/dmrlet.sock
-```
-
 **Serve a model:**
 ```bash
 # Auto-detect backend and GPUs
-dmrlet serve llama3.2
-
-# Specify backend
-dmrlet serve llama3.2 --backend vllm
-
-# Specify GPU allocation
-dmrlet serve llama3.2 --gpus 0,1
-dmrlet serve llama3.2 --gpus all
-
-# Multiple replicas
-dmrlet serve llama3.2 --replicas 2
-
-# Backend-specific options
-dmrlet serve llama3.2 --ctx-size 4096      # llama.cpp context size
-dmrlet serve llama3.2 --gpu-memory 0.8     # vLLM GPU memory utilization
-```
-
-**List running models:**
-```bash
-dmrlet ps
-# MODEL          BACKEND    REPLICAS   GPUS      ENDPOINTS              STATUS
-# llama3.2       llama.cpp  1          [0,1,2,3] localhost:30000        healthy
-```
-
-**View logs:**
-```bash
-dmrlet logs llama3.2        # Last 100 lines
-dmrlet logs llama3.2 -f     # Follow logs
-```
-
-**Scale replicas:**
-```bash
-dmrlet scale llama3.2 4     # Scale to 4 replicas
-```
-
-**Stop a model:**
-```bash
-dmrlet stop llama3.2
-dmrlet stop --all           # Stop all models
-```
-
-**Check status:**
-```bash
-dmrlet status
-# DAEMON: running
-# SOCKET: /var/run/dmrlet.sock
-#
-# GPUs:
-#   GPU 0:  NVIDIA A100 80GB  81920MB  (in use: llama3.2)
-#   GPU 1:  NVIDIA A100 80GB  81920MB  (available)
-#
-# MODELS: 1 running
-```
-
-### Supported Backends
-
-- **llama.cpp** - Default backend for GGUF models
-- **vLLM** - High-throughput serving for safetensors models
-- **SGLang** - Fast serving with RadixAttention
-
-### Architecture
-
-```
-dmrlet daemon
-  ├── GPU Manager      - Auto-detect and allocate GPUs
-  ├── Container Manager - Docker-based container lifecycle
-  ├── Service Registry  - Endpoint discovery with load balancing
-  ├── Health Monitor    - Auto-restart unhealthy containers
-  ├── Auto-scaler       - Scale based on QPS/latency/GPU utilization
-  └── Log Aggregator    - Centralized log collection
+dmrlet serve gemma3
 ```
 
->>>>>>> Stashed changes
 ## Community
 
 For general questions and discussion, please use [Docker Model Runner's Slack channel](https://dockercommunity.slack.com/archives/C09H9P5E57B).