Hot fix small vocal eagle in docs (#4154)

Co-authored-by: ybyang <ybyang7@iflytek.com>

.github/workflows/release-docs.yml

@@ -7,6 +7,7 @@ on:
     paths:
       - 'docs/**'
       - 'python/sglang/version.py'
+      - "python/sglang/**"
   workflow_dispatch:
 
 concurrency:

docs/backend/speculative_decoding.ipynb

@@ -6,15 +6,7 @@
    "source": [
     "# Speculative Decoding\n",
     "\n",
-    "SGLang now provides an EAGLE2-based speculative decoding option. \n",
-    "The process is as follows:\n",
-    "* Within EAGLE the draft model predicts the next feature vector, i.e. the last hidden state of the original LLM, using the feature sequence $(f_1, ..., f_k)$ and the token sequence $(t_2, ..., t_{k+1})$. \n",
-    "* The next token is then sampled from $p_{k+2}=\\text{LMHead}(f_{k+1})$. Afterwards, the two sequences are extended in a tree style—branching out multiple potential continuations, with the branching factor per step controlled by the `speculative_eagle_topk` parameter—to ensure a more coherent connection of context, and are given as input again.\n",
-    "* EAGLE-2 additionally uses the draft model to evaluate how probable certain branches in the draft tree are, dynamically stopping the expansion of unlikely branches. After the expansion phase, reranking is employed to select only the top `speculative_num_draft_tokens` final nodes as draft tokens.\n",
-    "\n",
-    "This enhances drafting accuracy by operating on the features instead of tokens for more regular inputs and passing the tokens from the next timestep additionaly to minimize randomness effects from sampling. Furthermore the dynamic adjustment of the draft tree and selection of reranked final nodes increases acceptance rate of draft tokens further. For more details see [the paper](https://arxiv.org/abs/2406.16858).\n",
-    "\n",
-    "SGLangs implementation aims to maximize speed and efficiency and is considered to be among the fastest in open-source LLM engines.\n",
+    "SGLang now provides an EAGLE2-based speculative decoding option. Our implementation aims to maximize speed and efficiency and is considered to be among the fastest in open-source LLM engines.\n",
     "\n",
     "**Note:** Currently, Speculative Decoding in SGLang does not support radix cache.\n",
     "\n",
@@ -61,8 +53,8 @@
     "server_process, port = launch_server_cmd(\n",
     "    \"\"\"\n",
     "python3 -m sglang.launch_server --model meta-llama/Llama-2-7b-chat-hf  --speculative-algorithm EAGLE \\\n",
-    "    --speculative-draft-model-path lmsys/sglang-EAGLE-llama2-chat-7B --speculative-num-steps 5 \\\n",
-    "    --speculative-eagle-topk 8 --speculative-num-draft-tokens 64\n",
+    "    --speculative-draft-model-path lmsys/sglang-EAGLE-llama2-chat-7B --speculative-num-steps 3 \\\n",
+    "    --speculative-eagle-topk 4 --speculative-num-draft-tokens 16 --cuda-graph-max-bs 8\n",
     "\"\"\"\n",
     ")\n",
     "\n",
@@ -162,71 +154,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### EAGLE Decoding via Frequency-Ranked Speculative Sampling\n",
-    "\n",
-    "By employing a truncated high-frequency token vocabulary in the draft model, Eagle speculative decoding reduces `lm_head` computational overhead while accelerating the pipeline without quality degradation. For more details, checkout [the paper](https://arxiv.org/pdf/arXiv:2502.14856).\n",
-    "\n",
-    "In our implementation, set `--speculative-token-map` to enable the optimization. You can get the high-frequency token in FR-Spec from [this model](https://huggingface.co/thunlp/LLaMA3-Instruct-8B-FR-Spec). Or you can obtain high-frequency token by directly downloading these token from [this repo](https://github.com/thunlp/FR-Spec/tree/main?tab=readme-ov-file#prepare-fr-spec-vocabulary-subset).\n",
-    "\n",
-    "Thanks for the contribution from [Weilin Zhao](https://github.com/https://github.com/Achazwl) and [Zhousx](https://github.com/Zhou-sx). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sglang.test.test_utils import is_in_ci\n",
-    "\n",
-    "if is_in_ci():\n",
-    "    from patch import launch_server_cmd\n",
-    "else:\n",
-    "    from sglang.utils import launch_server_cmd\n",
-    "\n",
-    "from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
-    "\n",
-    "server_process, port = launch_server_cmd(\n",
-    "    \"\"\"\n",
-    "python3 -m sglang.launch_server --model meta-llama/Meta-Llama-3-8B-Instruct --speculative-algorithm EAGLE \\\n",
-    "    --speculative-draft-model-path lmsys/sglang-EAGLE-LLaMA3-Instruct-8B --speculative-num-steps 5 \\\n",
-    "    --speculative-eagle-topk 8 --speculative-num-draft-tokens 64 --speculative-token-map thunlp/LLaMA3-Instruct-8B-FR-Spec/freq_32768.pt \\\n",
-    "    --mem-fraction 0.7 --cuda-graph-max-bs 2 --dtype float16 \n",
-    "\"\"\"\n",
-    ")\n",
-    "\n",
-    "wait_for_server(f\"http://localhost:{port}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import openai\n",
+    "## References\n",
     "\n",
-    "client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
+    "EAGLE process is as follows:\n",
     "\n",
-    "response = client.chat.completions.create(\n",
-    "    model=\"meta-llama/Meta-Llama-3-8B-Instruct\",\n",
-    "    messages=[\n",
-    "        {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n",
-    "    ],\n",
-    "    temperature=0,\n",
-    "    max_tokens=64,\n",
-    ")\n",
+    "- Within EAGLE the draft model predicts the next feature vector, i.e. the last hidden state of the original LLM, using the feature sequence $(f_1, ..., f_k)$ and the token sequence $(t_2, ..., t_{k+1})$. \n",
+    "- The next token is then sampled from $p_{k+2}=\\text{LMHead}(f_{k+1})$. Afterwards, the two sequences are extended in a tree style—branching out multiple potential continuations, with the branching factor per step controlled by the `speculative_eagle_topk` parameter—to ensure a more coherent connection of context, and are given as input again.\n",
+    "- EAGLE-2 additionally uses the draft model to evaluate how probable certain branches in the draft tree are, dynamically stopping the expansion of unlikely branches. After the expansion phase, reranking is employed to select only the top `speculative_num_draft_tokens` final nodes as draft tokens.\n",
     "\n",
-    "print_highlight(f\"Response: {response}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "terminate_process(server_process)"
+    "This enhances drafting accuracy by operating on the features instead of tokens for more regular inputs and passing the tokens from the next timestep additionaly to minimize randomness effects from sampling. Furthermore the dynamic adjustment of the draft tree and selection of reranked final nodes increases acceptance rate of draft tokens further. For more details see [the paper](https://arxiv.org/abs/2406.16858)."
    ]
   }
  ],