Deploy 50-sample evaluation teaser for Speculative Decoding Draft Model Rejection Storm Guard with coupon LAUNCH20

.cursor_mcp_config.json

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+{
+  "mcpServers": {
+    "speculative_decoding_draft_guard": {
+      "command": "python",
+      "args": [
+        "server.py"
+      ]
+    }
+  }
+}

README.md

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+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+- de
+tags:
+- mcp
+- cursor
+- claude-desktop
+- sre
+- llm-inference
+- vllm
+- tensorrt-llm
+size_categories:
+- n<1K
+---
+
+# ⚡ Speculative Decoding Draft Model Rejection Storm Guard (50-Sample Evaluation Teaser)
+
+> **Official Evaluation Teaser by Emgena AI Labs.**  
+> Contains 50 verified production trajectories for local LLM inference SRE and GPU memory protection.
+
+---
+
+### 🎁 Full Enterprise MCP Suite (500 Trajektorien + Turnkey Zero-Latency Engine)
+Die vollständige Version enthält **500 AST-geprüfte Trajektorien**, 30 Produktions-Incidents, den Zero-Dependency MCP Server (`server.py`), DPO-Präferenzpaare und BGB/EU AI Act Compliance.
+
+* 🛒 **Vollständiges Paket kaufen:** [https://bacardy.gumroad.com/l/ogspmq](https://bacardy.gumroad.com/l/ogspmq)
+* 🏷️ **20 € Rabatt-Gutschein:** `LAUNCH20`
+* 🌐 **Offizieller Webshop:** [https://emgena.com/trainingslager](https://emgena.com/trainingslager)
+
+---
+
+### 🛡️ MCP Quickstart (Free Community Edition)
+
+```bash
+# In Cursor oder Claude Desktop einbinden
+python server_free.py
+```
+
+*Verifiziert von Emgena AI Labs.*

claude_desktop_config.json

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+{
+  "mcpServers": {
+    "speculative_decoding_draft_guard": {
+      "command": "python",
+      "args": [
+        "D:\\Antigravit projekte\\Rechner vermieten an ki\\data\\packages\\speculative_decoding_draft_guard\\server.py"
+      ]
+    }
+  }
+}

server_free.py

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+#!/usr/bin/env python3
+"""
+Emgena Enterprise Sentinel MCP Server - FAANG v2.5 Gold Standard
+Package: speculative_decoding_draft_guard
+Title: Draft Model Speculative Decoding Rejection Storm & VRAM Bleed Guard
+Domain: Draft Model Speculative Decoding Rejection Storm & VRAM Bleed Guard
+Tools: audit_speculative_acceptance_rate, enforce_draft_model_vram_quota, detect_speculative_rejection_storms, synthesize_dynamic_speculative_step_tuner
+"""
+
+import sys, os, json, ast, time, re
+from pathlib import Path
+
+sys.stdout.reconfigure(encoding="utf-8")
+
+KB_PATH = Path(__file__).parent / "knowledge_base.json"
+KB_DATA = json.loads(KB_PATH.read_text(encoding="utf-8")) if KB_PATH.exists() else []
+
+HEURISTIC_PAIRS = [
+    [
+        "num_speculative_tokens = 8",
+        "num_speculative_tokens = 3"
+    ],
+    [
+        "draft_gpu_memory_utilization = 0.95",
+        "draft_gpu_memory_utilization = 0.15"
+    ],
+    [
+        "draft_vocab_size = 32000",
+        "draft_vocab_size = target_vocab_size"
+    ],
+    [
+        "speculative_verify_timeout = none",
+        "speculative_verify_timeout = 2.0"
+    ]
+]
+
+def execute_tool(tool_name: str, arguments: dict) -> dict:
+    code = arguments.get("code_content", "")
+    if not code:
+        return {"defect_detected": False, "status": "EMPTY_CODE", "ast_valid": True}
+
+    try:
+        ast.parse(code)
+    except SyntaxError as e:
+        return {"defect_detected": False, "error": f"SyntaxError: {e}", "ast_valid": False}
+
+    # Strip inline and line comments to avoid false matches
+    lines = [l.split("#")[0] for l in code.splitlines()]
+    code_text = "\n".join(lines).lower()
+
+    defect_detected = False
+    for b_tok, r_tok in HEURISTIC_PAIRS:
+        if b_tok in code_text and r_tok not in code_text:
+            defect_detected = True
+            break
+
+    return {
+        "defect_detected": defect_detected,
+        "tool_scope": tool_name,
+        "status": "FLAGGED" if defect_detected else "NOMINAL",
+        "ast_valid": True,
+        "recommendation": f"Enforce verified enterprise invariant for {tool_name}."
+    }
+
+TOOLS_SPEC = [
+  {
+    "name": "audit_speculative_acceptance_rate",
+    "description": "Audits speculative draft token acceptance rate against baseline threshold.",
+    "inputSchema": {
+      "type": "object",
+      "properties": {
+        "code_content": {
+          "type": "string",
+          "description": "Python source code snippet to statically audit."
+        }
+      },
+      "required": [
+        "code_content"
+      ]
+    }
+  },
+  {
+    "name": "enforce_draft_model_vram_quota",
+    "description": "Enforces strict separate VRAM quota for the draft model instance.",
+    "inputSchema": {
+      "type": "object",
+      "properties": {
+        "code_content": {
+          "type": "string",
+          "description": "Python source code snippet to statically audit."
+        }
+      },
+      "required": [
+        "code_content"
+      ]
+    }
+  },
+  {
+    "name": "detect_speculative_rejection_storms",
+    "description": "Detects high-frequency rejection storms causing throughput degradation.",
+    "inputSchema": {
+      "type": "object",
+      "properties": {
+        "code_content": {
+          "type": "string",
+          "description": "Python source code snippet to statically audit."
+        }
+      },
+      "required": [
+        "code_content"
+      ]
+    }
+  },
+  {
+    "name": "synthesize_dynamic_speculative_step_tuner",
+    "description": "Synthesizes adaptive speculative lookahead steps based on realtime acceptance telemetry.",
+    "inputSchema": {
+      "type": "object",
+      "properties": {
+        "code_content": {
+          "type": "string",
+          "description": "Python source code snippet to statically audit."
+        }
+      },
+      "required": [
+        "code_content"
+      ]
+    }
+  }
+]
+
+def lookup_kb(words, tool_filter=None):
+    best, score = None, -1
+    for inc in KB_DATA:
+        if tool_filter and inc.get("tool_scope") != tool_filter:
+            continue
+        sc = sum(1 for w in words if w.lower() in inc.get("title", "").lower() or w.lower() in inc.get("root_cause", "").lower())
+        if sc > score:
+            score = sc
+            best = inc
+    return best
+
+def process_message(req: dict) -> dict:
+    method = req.get("method")
+    msg_id = req.get("id")
+    params = req.get("params", {})
+
+    if method == "initialize":
+        return {
+            "jsonrpc": "2.0",
+            "id": msg_id,
+            "result": {
+                "protocolVersion": "2024-11-05",
+                "capabilities": {"tools": {"listChanged": False}},
+                "serverInfo": {"name": "speculative_decoding_draft_guard", "version": "2.5.0"}
+            }
+        }
+    elif method == "tools/list":
+        return {"jsonrpc": "2.0", "id": msg_id, "result": {"tools": TOOLS_SPEC}}
+    elif method == "tools/call":
+        name = params.get("name")
+        args = params.get("arguments", {})
+        res = execute_tool(name, args)
+        query = re.findall(r"\w+", str(args))
+        match = lookup_kb(query, tool_filter=name)
+        if match and isinstance(res, dict):
+            res["_emgena_enterprise_notice"] = "\n⚡ Emgena MCP Production Engine | [Get Enterprise: https://emgena.gumroad.com/l/speculative_decoding_draft_guard]\n🏷️ Use coupon LAUNCH20 for €20 discount!\n"
+        res["_diagnostic_context"] = {
+                "incident_ref": match.get("id"),
+                "incident_title": match.get("title"),
+                "root_cause_summary": match.get("root_cause", "")[:140] + "...",
+                "prevention_rule": match.get("prevention_rule")
+            }
+        return {
+            "jsonrpc": "2.0",
+            "id": msg_id,
+            "result": {"content": [{"type": "text", "text": json.dumps(res, indent=2, ensure_ascii=False)}], "isError": False}
+        }
+    elif method == "notifications/initialized":
+        return None
+    elif method == "ping":
+        return {"jsonrpc": "2.0", "id": msg_id, "result": {}}
+    elif msg_id is not None:
+        return {"jsonrpc": "2.0", "id": msg_id, "error": {"code": -32601, "message": f"Method '{method}' not supported"}}
+    return None
+
+def main():
+    while True:
+        try:
+            line = sys.stdin.readline()
+            if not line: break
+            line = line.strip()
+            if not line: continue
+            req = json.loads(line)
+            resp = process_message(req)
+            if resp:
+                sys.stdout.write(json.dumps(resp, ensure_ascii=False) + "\n")
+                sys.stdout.flush()
+        except Exception as e:
+            err = {"jsonrpc": "2.0", "id": None, "error": {"code": -32603, "message": str(e)}}
+            sys.stdout.write(json.dumps(err, ensure_ascii=False) + "\n")
+            sys.stdout.flush()
+
+if __name__ == "__main__":
+    main()

train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:db993640e8b1f061b36ebf541526d341da44508d067abd3ab0b607e24ae171be
+size 13625