diff --git a/Dockerfile.extproc b/Dockerfile.extproc
index eff5f740..aa51d917 100644
--- a/Dockerfile.extproc
+++ b/Dockerfile.extproc
@@ -62,6 +62,10 @@ RUN mkdir -p src/semantic-router
 COPY src/semantic-router/go.mod src/semantic-router/go.sum src/semantic-router/
 COPY candle-binding/go.mod candle-binding/semantic-router.go candle-binding/
 
+# Pre-download modules to fail fast if mirrors are unreachable
+RUN cd src/semantic-router && go mod download && \
+    cd /app/candle-binding && go mod download
+
 # Copy semantic-router source code
 COPY src/semantic-router/ src/semantic-router/
 
diff --git a/README.md b/README.md
index df5a3da6..d9ab4a0d 100644
--- a/README.md
+++ b/README.md
@@ -91,6 +91,36 @@ This command will:
 
 For detailed installation and configuration instructions, see the [Complete Documentation](https://vllm-semantic-router.com/docs/installation/).
 
+### What This Starts By Default
+
+`make docker-compose-up` now launches the full stack including a lightweight local OpenAI-compatible model server powered by **llm-katan** (serving the small model `Qwen/Qwen3-0.6B` under the alias `qwen3`). The semantic router is configured to route classification & default generations to this local endpoint out-of-the-box. This gives you an entirely self-contained experience (no external API keys required) while still letting you add remote / larger models later.
+
+### Core Mode (Without Local Model)
+
+If you only want the core semantic-router + Envoy + observability stack (and will point to external OpenAI-compatible endpoints yourself):
+
+```bash
+make docker-compose-up-core
+```
+
+### Prerequisite Model Download (Speeds Up First Run)
+
+The existing model bootstrap targets now also pre-download the small llm-katan model so the first `docker-compose-up` avoids an on-demand Hugging Face fetch.
+
+Minimal set (fast):
+
+```bash
+make models-download-minimal
+```
+
+Full set:
+
+```bash
+make models-download
+```
+
+Both create a stamp file once `Qwen/Qwen3-0.6B` is present to keep subsequent runs idempotent.
+
 ## Documentation 📖
 
 For comprehensive documentation including detailed setup instructions, architecture guides, and API references, visit:
diff --git a/config/config.yaml b/config/config.yaml
index 579b9e35..5ad29d5a 100644
--- a/config/config.yaml
+++ b/config/config.yaml
@@ -32,13 +32,13 @@ prompt_guard:
 # NOT supported: domain names (example.com), protocol prefixes (http://), paths (/api), ports in address (use 'port' field)
 vllm_endpoints:
   - name: "endpoint1"
-    address: "127.0.0.1"  # IPv4 address - REQUIRED format
-    port: 8000
+    address: "172.28.0.20"  # Static IPv4 of llm-katan within docker compose network
+    port: 8002
     weight: 1
 
 model_config:
-  "openai/gpt-oss-20b":
-    reasoning_family: "gpt-oss"  # This model uses GPT-OSS reasoning syntax
+  "qwen3":
+    reasoning_family: "qwen3"  # This model uses Qwen-3 reasoning syntax
     preferred_endpoints: ["endpoint1"]
     pii_policy:
       allow_by_default: true
@@ -63,89 +63,89 @@ categories:
   - name: business
     system_prompt: "You are a senior business consultant and strategic advisor with expertise in corporate strategy, operations management, financial analysis, marketing, and organizational development. Provide practical, actionable business advice backed by proven methodologies and industry best practices. Consider market dynamics, competitive landscape, and stakeholder interests in your recommendations."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.7
         use_reasoning: false  # Business performs better without reasoning
   - name: law
     system_prompt: "You are a knowledgeable legal expert with comprehensive understanding of legal principles, case law, statutory interpretation, and legal procedures across multiple jurisdictions. Provide accurate legal information and analysis while clearly stating that your responses are for informational purposes only and do not constitute legal advice. Always recommend consulting with qualified legal professionals for specific legal matters."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.4
         use_reasoning: false
   - name: psychology
     system_prompt: "You are a psychology expert with deep knowledge of cognitive processes, behavioral patterns, mental health, developmental psychology, social psychology, and therapeutic approaches. Provide evidence-based insights grounded in psychological research and theory. When discussing mental health topics, emphasize the importance of professional consultation and avoid providing diagnostic or therapeutic advice."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.6
         use_reasoning: false
   - name: biology
     system_prompt: "You are a biology expert with comprehensive knowledge spanning molecular biology, genetics, cell biology, ecology, evolution, anatomy, physiology, and biotechnology. Explain biological concepts with scientific accuracy, use appropriate terminology, and provide examples from current research. Connect biological principles to real-world applications and emphasize the interconnectedness of biological systems."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.9
         use_reasoning: false
   - name: chemistry
     system_prompt: "You are a chemistry expert specializing in chemical reactions, molecular structures, and laboratory techniques. Provide detailed, step-by-step explanations."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.6
         use_reasoning: true  # Enable reasoning for complex chemistry
   - name: history
     system_prompt: "You are a historian with expertise across different time periods and cultures. Provide accurate historical context and analysis."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.7
         use_reasoning: false
   - name: other
     system_prompt: "You are a helpful and knowledgeable assistant. Provide accurate, helpful responses across a wide range of topics."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.7
         use_reasoning: false
   - name: health
     system_prompt: "You are a health and medical information expert with knowledge of anatomy, physiology, diseases, treatments, preventive care, nutrition, and wellness. Provide accurate, evidence-based health information while emphasizing that your responses are for educational purposes only and should never replace professional medical advice, diagnosis, or treatment. Always encourage users to consult healthcare professionals for medical concerns and emergencies."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.5
         use_reasoning: false
   - name: economics
     system_prompt: "You are an economics expert with deep understanding of microeconomics, macroeconomics, econometrics, financial markets, monetary policy, fiscal policy, international trade, and economic theory. Analyze economic phenomena using established economic principles, provide data-driven insights, and explain complex economic concepts in accessible terms. Consider both theoretical frameworks and real-world applications in your responses."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 1.0
         use_reasoning: false
   - name: math
     system_prompt: "You are a mathematics expert. Provide step-by-step solutions, show your work clearly, and explain mathematical concepts in an understandable way."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 1.0
         use_reasoning: true  # Enable reasoning for complex math
   - name: physics
     system_prompt: "You are a physics expert with deep understanding of physical laws and phenomena. Provide clear explanations with mathematical derivations when appropriate."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.7
         use_reasoning: true  # Enable reasoning for physics
   - name: computer science
     system_prompt: "You are a computer science expert with knowledge of algorithms, data structures, programming languages, and software engineering. Provide clear, practical solutions with code examples when helpful."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.6
         use_reasoning: false
   - name: philosophy
     system_prompt: "You are a philosophy expert with comprehensive knowledge of philosophical traditions, ethical theories, logic, metaphysics, epistemology, political philosophy, and the history of philosophical thought. Engage with complex philosophical questions by presenting multiple perspectives, analyzing arguments rigorously, and encouraging critical thinking. Draw connections between philosophical concepts and contemporary issues while maintaining intellectual honesty about the complexity and ongoing nature of philosophical debates."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.5
         use_reasoning: false
   - name: engineering
     system_prompt: "You are an engineering expert with knowledge across multiple engineering disciplines including mechanical, electrical, civil, chemical, software, and systems engineering. Apply engineering principles, design methodologies, and problem-solving approaches to provide practical solutions. Consider safety, efficiency, sustainability, and cost-effectiveness in your recommendations. Use technical precision while explaining concepts clearly, and emphasize the importance of proper engineering practices and standards."
     model_scores:
-      - model: openai/gpt-oss-20b
+      - model: qwen3
         score: 0.7
         use_reasoning: false
 
-default_model: openai/gpt-oss-20b
+default_model: "qwen3"
 
 # Reasoning family configurations
 reasoning_families:
diff --git a/deploy/docker-compose/docker-compose.yml b/deploy/docker-compose/docker-compose.yml
index 98fdb70d..f2351f36 100644
--- a/deploy/docker-compose/docker-compose.yml
+++ b/deploy/docker-compose/docker-compose.yml
@@ -9,9 +9,12 @@ services:
     volumes:
       - ../../config:/app/config:ro
       - ../../models:/app/models:ro
+      - ~/.cache/huggingface:/root/.cache/huggingface
     environment:
       - LD_LIBRARY_PATH=/app/lib
       - CONFIG_FILE=${CONFIG_FILE:-/app/config/config.yaml}
+      - HUGGINGFACE_HUB_CACHE=/root/.cache/huggingface
+      - HF_HUB_ENABLE_HF_TRANSFER=1
     networks:
       - semantic-network
     healthcheck:
@@ -134,18 +137,27 @@ services:
 
   # LLM Katan service for testing
   llm-katan:
-    build:
-      context: ../../e2e-tests/llm-katan
-      dockerfile: Dockerfile
+    image: ${LLM_KATAN_IMAGE:-ghcr.io/vllm-project/semantic-router/llm-katan:latest}
     container_name: llm-katan
     profiles: ["testing", "llm-katan"]
     ports:
-      - "8002:8000"
+      - "8002:8002"
     environment:
       - HUGGINGFACE_HUB_TOKEN=${HUGGINGFACE_HUB_TOKEN:-}
+      - HF_HUB_ENABLE_HF_TRANSFER=1
+    volumes:
+      - ../../models:/app/models:ro
+      - hf-cache:/home/llmkatan/.cache/huggingface
     networks:
-      - semantic-network
-    command: ["llm-katan", "--model", "Qwen/Qwen3-0.6B", "--host", "0.0.0.0", "--port", "8000"]
+      semantic-network:
+        ipv4_address: 172.28.0.20
+    command: ["llm-katan", "--model", "/app/models/Qwen/Qwen3-0.6B", "--served-model-name", "qwen3", "--host", "0.0.0.0", "--port", "8002"]
+    healthcheck:
+      test: ["CMD", "curl", "-fsS", "http://localhost:8002/health"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+      start_period: 10s
 
   # Semantic Router Dashboard
   dashboard:
@@ -202,3 +214,4 @@ volumes:
   grafana-data:
   openwebui-data:
   openwebui-pipelines:
+  hf-cache:
diff --git a/tools/make/docker.mk b/tools/make/docker.mk
index 038f819f..975d91f4 100644
--- a/tools/make/docker.mk
+++ b/tools/make/docker.mk
@@ -99,8 +99,8 @@ BUILD_FLAG=$(if $(REBUILD),--build,)
 # Docker compose shortcuts (no rebuild by default)
 docker-compose-up:
 	@$(LOG_TARGET)
-	@echo "Starting services with docker-compose (REBUILD=$(REBUILD))..."
-	@docker compose up -d $(BUILD_FLAG)
+	@echo "Starting services with docker-compose (default includes llm-katan) (REBUILD=$(REBUILD))..."
+	@docker compose --profile llm-katan up -d $(BUILD_FLAG)
 
 docker-compose-up-testing:
 	@$(LOG_TARGET)
@@ -112,6 +112,12 @@ docker-compose-up-llm-katan:
 	@echo "Starting services with llm-katan profile (REBUILD=$(REBUILD))..."
 	@docker compose --profile llm-katan up -d $(BUILD_FLAG)
 
+# Start core services only (closer to production; excludes llm-katan)
+docker-compose-up-core:
+	@$(LOG_TARGET)
+	@echo "Starting core services (no llm-katan) (REBUILD=$(REBUILD))..."
+	@docker compose up -d $(BUILD_FLAG)
+
 # Explicit rebuild targets for convenience
 docker-compose-rebuild: REBUILD=1
 docker-compose-rebuild: docker-compose-up
@@ -139,7 +145,8 @@ docker-help:
 	@echo "  docker-run-llm-katan      - Run llm-katan Docker image locally"
 	@echo "  docker-run-llm-katan-custom SERVED_NAME=name - Run with custom served model name"
 	@echo "  docker-clean              - Clean up Docker images"
-	@echo "  docker-compose-up                    - Start services (add REBUILD=1 to rebuild)"
+	@echo "  docker-compose-up                    - Start services (default includes llm-katan; REBUILD=1 to rebuild)"
+	@echo "  docker-compose-up-core               - Start core services only (no llm-katan)"
 	@echo "  docker-compose-up-testing            - Start with testing profile (REBUILD=1 optional)"
 	@echo "  docker-compose-up-llm-katan          - Start with llm-katan profile (REBUILD=1 optional)"
 	@echo "  docker-compose-rebuild               - Force rebuild then start"
diff --git a/tools/make/linter.mk b/tools/make/linter.mk
index 5e15d89f..ddd710a5 100644
--- a/tools/make/linter.mk
+++ b/tools/make/linter.mk
@@ -12,11 +12,20 @@ docs-lint-fix: docs-install
 
 markdown-lint:
 	@$(LOG_TARGET)
-	markdownlint -c tools/linter/markdown/markdownlint.yaml "**/*.md" --ignore node_modules --ignore website/node_modules --ignore dashboard/frontend/node_modules
+	markdownlint -c tools/linter/markdown/markdownlint.yaml "**/*.md" \
+		--ignore node_modules \
+		--ignore website/node_modules \
+		--ignore dashboard/frontend/node_modules \
+		--ignore models
 
 markdown-lint-fix:
 	@$(LOG_TARGET)
-	markdownlint -c tools/linter/markdown/markdownlint.yaml "**/*.md" --ignore node_modules --ignore website/node_modules --ignore dashboard/frontend/node_modules --fix
+	markdownlint -c tools/linter/markdown/markdownlint.yaml "**/*.md" \
+		--ignore node_modules \
+		--ignore website/node_modules \
+		--ignore dashboard/frontend/node_modules \
+		--ignore models \
+		--fix
 
 yaml-lint:
 	@$(LOG_TARGET)
diff --git a/tools/make/models.mk b/tools/make/models.mk
index 8a37c639..500b8031 100644
--- a/tools/make/models.mk
+++ b/tools/make/models.mk
@@ -24,6 +24,10 @@ download-models:
 
 download-models-minimal:
 	@mkdir -p models
+	# Pre-download tiny LLM for llm-katan (optional but speeds up first start)
+	@if [ ! -f "models/Qwen/Qwen3-0.6B/.downloaded" ] || [ ! -d "models/Qwen/Qwen3-0.6B" ]; then \
+		hf download Qwen/Qwen3-0.6B --local-dir models/Qwen/Qwen3-0.6B && printf '%s\n' "$$(date -u +%Y-%m-%dT%H:%M:%SZ)" > models/Qwen/Qwen3-0.6B/.downloaded; \
+	fi
 	@if [ ! -f "models/category_classifier_modernbert-base_model/.downloaded" ] || [ ! -d "models/category_classifier_modernbert-base_model" ]; then \
 		hf download LLM-Semantic-Router/category_classifier_modernbert-base_model --local-dir models/category_classifier_modernbert-base_model && printf '%s\n' "$$(date -u +%Y-%m-%dT%H:%M:%SZ)" > models/category_classifier_modernbert-base_model/.downloaded; \
 	fi
@@ -41,6 +45,10 @@ download-models-minimal:
 
 download-models-full:
 	@mkdir -p models
+	# Pre-download tiny LLM for llm-katan (optional but speeds up first start)
+	@if [ ! -f "models/Qwen/Qwen3-0.6B/.downloaded" ] || [ ! -d "models/Qwen/Qwen3-0.6B" ]; then \
+		hf download Qwen/Qwen3-0.6B --local-dir models/Qwen/Qwen3-0.6B && printf '%s\n' "$$(date -u +%Y-%m-%dT%H:%M:%SZ)" > models/Qwen/Qwen3-0.6B/.downloaded; \
+	fi
 	@if [ ! -f "models/category_classifier_modernbert-base_model/.downloaded" ] || [ ! -d "models/category_classifier_modernbert-base_model" ]; then \
 		hf download LLM-Semantic-Router/category_classifier_modernbert-base_model --local-dir models/category_classifier_modernbert-base_model && printf '%s\n' "$$(date -u +%Y-%m-%dT%H:%M:%SZ)" > models/category_classifier_modernbert-base_model/.downloaded; \
 	fi