chore(translation): finish slime->vime / sglang->vllm cleanup missed in #107 sync

.github/workflows/pr-test.yml

@@ -556,10 +556,10 @@ jobs:
     env:
       GITHUB_COMMIT_NAME: ${{ github.sha }}_${{ github.event.pull_request.number || 'non-pr' }}
       WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
-      SLIME_TEST_ENABLE_INFINITE_RUN: ${{ (github.event_name == 'workflow_dispatch' && github.event.inputs.infinite_run) || 'false' }}
-      SLIME_TEST_USE_DEEPEP: ${{ matrix.info.use_deepep || '0' }}
-      SLIME_TEST_USE_FP8_ROLLOUT: ${{ matrix.info.use_fp8_rollout || '0' }}
-      SLIME_TEST_ENABLE_EVAL: ${{ matrix.info.enable_eval || '1' }}
+      VIME_TEST_ENABLE_INFINITE_RUN: ${{ (github.event_name == 'workflow_dispatch' && github.event.inputs.infinite_run) || 'false' }}
+      VIME_TEST_USE_DEEPEP: ${{ matrix.info.use_deepep || '0' }}
+      VIME_TEST_USE_FP8_ROLLOUT: ${{ matrix.info.use_fp8_rollout || '0' }}
+      VIME_TEST_ENABLE_EVAL: ${{ matrix.info.enable_eval || '1' }}
 
     steps:
       - name: Checkout repository

docs/en/advanced/speculative-decoding.md

@@ -12,22 +12,14 @@ which vime forwards via `--vllm-speculative-config`. For models with MTP layers
 --vllm-speculative-config '{"method":"mtp","num_speculative_tokens":3}'
 ```
 
-To use a separately trained draft model, set `model` (and optionally `draft_tensor_parallel_size`)
-in the same JSON:
+To use a separately trained draft model (e.g., one trained with [vllm-project/speculators](https://github.com/vllm-project/speculators)),
+set `model` (and optionally `draft_tensor_parallel_size`) in the same JSON:
 
 ```bash
 --vllm-speculative-config '{"method":"eagle","num_speculative_tokens":3,"model":"/your/draft/model/path"}'
 ```
 
-To train a draft model from scratch, see [TorchSpec](https://github.com/lightseekorg/TorchSpec)
-and [vllm-project/speculators](https://github.com/vllm-project/speculators).
-TorchSpec provides torch-native, disaggregated draft training.
-Speculators supports EAGLE-3, DFlash, and MTP-style drafts, ships pre-trained
-checkpoints on Hugging Face (see the `RedHatAI/*-speculator.*` collection), and
-saves drafts in a format that `vllm serve <speculator_model>` can deploy directly.
-
-For the full list of `SpeculativeConfig` fields (including `disable_by_batch_size`,
-`acceptance_method`, draft TP, etc.), see vLLM's speculative-decoding
+For detailed parameter meanings and configuration, see vLLM's speculative-decoding
 [documentation](https://docs.vllm.ai/en/latest/features/speculative_decoding/).
 
 ## Online SFT for the Draft Model

docs/en/developer_guide/debug.md

@@ -67,48 +67,3 @@ When running large scale RL, we will occationally meet the IMA in vLLM, there ar
 4. Try CUDA Core Dump to find the error kernel
 
    We recommend reading the blog from the vLLM team: [CUDA Core Dump: An Effective Tool to Debug Memory Access Issues and Beyond](https://blog.vllm.ai/2025/08/11/cuda-debugging.html)
-
-## Step-by-Step Debugging with Ray Distributed Debugger
-
-Ray provides a [distributed debugger](https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html) based on debugpy that lets you set breakpoints in the driver process and step through code interactively.
-
-1. Install debugpy:
-
-   ```bash
-   pip install debugpy==1.8.0
-   ```
-
-2. Enable `RAY_DEBUG_POSTMORTEM` in your launch script:
-
-   ```bash
-   export RAY_DEBUG_POSTMORTEM=1
-
-   RUNTIME_ENV_JSON="{
-     \"env_vars\": {
-       ...
-       \"RAY_DEBUG_POSTMORTEM\": \"${RAY_DEBUG_POSTMORTEM:-0}\"
-     }
-   }"
-
-   ray job submit --address="http://127.0.0.1:8265" \
-      --runtime-env-json="${RUNTIME_ENV_JSON}" \
-      -- python3 train.py [args...]
-   ```
-
-3. Add `ray.init()` before `breakpoint()` in `train.py`:
-
-   ```python
-   if __name__ == "__main__":
-       ray.init()
-       breakpoint()
-       args = parse_args()
-       train(args)
-   ```
-
-   `ray.init()` is required because the distributed debugger depends on `core_worker`, which is only available after Ray initialization. Without it, `breakpoint()` raises `AttributeError: 'Worker' object has no attribute 'core_worker'`.
-
-4. Connect via VS Code:
-
-   Install the [Ray Distributed Debugger](https://marketplace.visualstudio.com/items?itemName=ray-project.ray-distributed-debugger) extension in VS Code. Run your launch script to submit the job. Once the job hits `breakpoint()`, open the Ray Dashboard panel in VS Code and click the active breakpoint to attach the debugger. You can then step through code, inspect variables, and set additional breakpoints directly in the editor.
-
-> **Note**: Remove `ray.init()` and `breakpoint()` after debugging. An explicit `ray.init()` without arguments may cause issues in multi-node training where Ray injects specific namespace and runtime environment configurations via `ray job submit`.