feat: 模型评测端到端闭环 — EvalRunner引擎 + 算力节点Job执行 + 结果回写

算力节点 (compute):
- 新建 eval_runner.py: 评测执行引擎，作为subprocess运行
  - 加载模型 + JSONL数据集 + 逐样本推理
  - BLEU/ROUGE/Cosine基础指标计算
  - LLM Judge评分(OpenAI兼容API调用)
  - 结果写入eval_results.json
- adapter.py: build_command新增engine=eval分支
- main.py: 新增/json模块导入，新增/compute/files/read端点，eval job校验

后端:
- platform.py: 重写startEval提交eval job到算力节点
  - 支持models表和trained_models表查找
  - 已合并模型不传adapter路径
- platform_store.py: 新增update_eval_task/running_eval_tasks/apply_eval_job_result
- sync.py: poller新增eval job同步，异步读取eval_results.json回写结果

前端:
- EvalCreateView/DimensionCreateView: eval模型过滤扩展(API类型+api_url)
- EvalCreateView: GPU过滤在线节点空闲GPU
- EvalTaskSetupStep: GPU value从数组index改为gpu.id
- BasicMetricSetupStep: ROUGE方法名修正(rouge_1→rouge1)
- EvalView: 新增5秒轮询刷新

Co-Authored-By: Claude <noreply@anthropic.com>

backend/app/db/platform_store.py

@@ -2076,10 +2076,55 @@ class PlatformStore:
             )
         return self.eval_task(task_id)
 
+    def update_eval_task(self, task_id: str, updates: dict[str, Any]) -> dict[str, Any]:
+        """Update fields in an eval task's payload without replacing the whole record."""
+        task = self.eval_task(task_id)
+        merged = {**task, **updates}
+        with self.connect() as conn:
+            conn.execute(
+                "UPDATE eval_tasks SET payload=?, status=? WHERE id=?",
+                (json_dumps(merged), merged.get("status", task.get("status", "pending")), task_id),
+            )
+        return self.eval_task(task_id)
+
     def delete_eval_task(self, task_id: str) -> None:
         with self.connect() as conn:
             conn.execute("DELETE FROM eval_tasks WHERE id=?", (task_id,))
 
+    def running_eval_tasks(self) -> list[dict[str, Any]]:
+        """Return eval tasks that have been submitted to a compute node and are still running."""
+        return [
+            task for task in self.eval_tasks()
+            if task.get("compute_job_id") and task.get("status") in {"queued", "running"}
+        ]
+
+    def apply_eval_job_result(self, task_id: str, job: dict[str, Any], result_content: dict[str, Any] | None = None) -> dict[str, Any]:
+        """Sync a compute job status/result back to an eval task."""
+        task = self.eval_task(task_id)
+        job_status = str(job.get("status", ""))
+        status_map = {"queued": "running", "running": "running", "completed": "completed",
+                       "failed": "failed", "stopped": "stopped"}
+        new_status = status_map.get(job_status, job_status or task.get("status", "pending"))
+        updates: dict[str, Any] = {
+            "status": new_status,
+            "progress": int(job.get("progress", 0)),
+            "output_dir": job.get("output_dir", task.get("output_dir", "")),
+        }
+        # On completion, populate results from eval_results.json content
+        if new_status == "completed" and result_content:
+            updates.update({
+                "overall_score": result_content.get("overall_score", 0),
+                "overall_score_max": result_content.get("overall_score_max", 100),
+                "overall_evaluation": result_content.get("overall_evaluation", ""),
+                "improvement_suggestions": result_content.get("improvement_suggestions", []),
+                "dimension_summary": result_content.get("dimension_summary", []),
+                "samples": result_content.get("samples", []),
+                "sample_count": result_content.get("sample_count", 0),
+                "completed_count": result_content.get("completed_count", 0),
+                "passed_count": result_content.get("passed_count", 0),
+            })
+        return self.update_eval_task(task_id, updates)
+
     def dimensions(self) -> list[dict[str, Any]]:
         with self.connect() as conn:
             rows = conn.execute("SELECT * FROM eval_dimensions ORDER BY create_time DESC").fetchall()