365 lines
10 KiB
Plaintext
365 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Native APIs\n",
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"\n",
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"Apart from the OpenAI compatible APIs, the SGLang Runtime also provides its native server APIs. We introduce these following APIs:\n",
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"\n",
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"- `/generate` (text generation model)\n",
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"- `/get_model_info`\n",
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"- `/get_server_info`\n",
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"- `/health`\n",
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"- `/health_generate`\n",
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"- `/flush_cache`\n",
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"- `/update_weights`\n",
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"- `/encode`(embedding model)\n",
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"- `/classify`(reward model)\n",
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"\n",
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"We mainly use `requests` to test these APIs in the following examples. You can also use `curl`."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Launch A Server"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sglang.utils import (\n",
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" execute_shell_command,\n",
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" wait_for_server,\n",
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" terminate_process,\n",
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" print_highlight,\n",
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")\n",
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"\n",
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"import requests\n",
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"\n",
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"server_process = execute_shell_command(\n",
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" \"\"\"\n",
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"python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --port=30010\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"wait_for_server(\"http://localhost:30010\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate (text generation model)\n",
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"Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](../references/sampling_params.md)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"http://localhost:30010/generate\"\n",
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"data = {\"text\": \"What is the capital of France?\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"print_highlight(response.json())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get Model Info\n",
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"\n",
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"Get the information of the model.\n",
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"\n",
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"- `model_path`: The path/name of the model.\n",
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"- `is_generation`: Whether the model is used as generation model or embedding model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"http://localhost:30010/get_model_info\"\n",
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"\n",
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"response = requests.get(url)\n",
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"response_json = response.json()\n",
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"print_highlight(response_json)\n",
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"assert response_json[\"model_path\"] == \"meta-llama/Llama-3.2-1B-Instruct\"\n",
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"assert response_json[\"is_generation\"] is True\n",
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"assert response_json.keys() == {\"model_path\", \"is_generation\"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get Server Info\n",
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"Gets the server information including CLI arguments, token limits, and memory pool sizes.\n",
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"- Note: `get_server_info` merges the following deprecated endpoints:\n",
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" - `get_server_args`\n",
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" - `get_memory_pool_size` \n",
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" - `get_max_total_num_tokens`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# get_server_info\n",
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"\n",
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"url = \"http://localhost:30010/get_server_info\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Health Check\n",
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"- `/health`: Check the health of the server.\n",
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"- `/health_generate`: Check the health of the server by generating one token."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"http://localhost:30010/health_generate\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"http://localhost:30010/health\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Flush Cache\n",
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"\n",
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"Flush the radix cache. It will be automatically triggered when the model weights are updated by the `/update_weights` API."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# flush cache\n",
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"\n",
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"url = \"http://localhost:30010/flush_cache\"\n",
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"\n",
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"response = requests.post(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Update Weights\n",
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"\n",
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"Update model weights without restarting the server. Use for continuous evaluation during training. Only applicable for models with the same architecture and parameter size."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# successful update with same architecture and size\n",
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"\n",
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"url = \"http://localhost:30010/update_weights\"\n",
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"data = {\"model_path\": \"meta-llama/Llama-3.2-1B\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"print_highlight(response.text)\n",
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"assert response.json()[\"success\"] is True\n",
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"assert response.json()[\"message\"] == \"Succeeded to update model weights.\"\n",
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"assert response.json().keys() == {\"success\", \"message\"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# failed update with different parameter size\n",
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"\n",
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"url = \"http://localhost:30010/update_weights\"\n",
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"data = {\"model_path\": \"meta-llama/Llama-3.2-3B\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"response_json = response.json()\n",
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"print_highlight(response_json)\n",
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"assert response_json[\"success\"] is False\n",
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"assert response_json[\"message\"] == (\n",
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" \"Failed to update weights: The size of tensor a (2048) must match \"\n",
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" \"the size of tensor b (3072) at non-singleton dimension 1.\\n\"\n",
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" \"Rolling back to original weights.\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Encode (embedding model)\n",
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"\n",
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"Encode text into embeddings. Note that this API is only available for [embedding models](openai_api_embeddings.html#openai-apis-embedding) and will raise an error for generation models.\n",
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"Therefore, we launch a new server to server an embedding model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(server_process)\n",
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"\n",
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"embedding_process = execute_shell_command(\n",
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" \"\"\"\n",
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"python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n",
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" --port 30020 --host 0.0.0.0 --is-embedding\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"wait_for_server(\"http://localhost:30020\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# successful encode for embedding model\n",
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"\n",
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"url = \"http://localhost:30020/encode\"\n",
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"data = {\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"text\": \"Once upon a time\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"response_json = response.json()\n",
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"print_highlight(f\"Text embedding (first 10): {response_json['embedding'][:10]}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Classify (reward model)\n",
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"\n",
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"SGLang Runtime also supports reward models. Here we use a reward model to classify the quality of pairwise generations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(embedding_process)\n",
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"\n",
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"# Note that SGLang now treats embedding models and reward models as the same type of models.\n",
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"# This will be updated in the future.\n",
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"\n",
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"reward_process = execute_shell_command(\n",
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" \"\"\"\n",
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"python -m sglang.launch_server --model-path Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 --port 30030 --host 0.0.0.0 --is-embedding\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"wait_for_server(\"http://localhost:30030\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from transformers import AutoTokenizer\n",
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"\n",
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"PROMPT = (\n",
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" \"What is the range of the numeric output of a sigmoid node in a neural network?\"\n",
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")\n",
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"\n",
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"RESPONSE1 = \"The output of a sigmoid node is bounded between -1 and 1.\"\n",
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"RESPONSE2 = \"The output of a sigmoid node is bounded between 0 and 1.\"\n",
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"\n",
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"CONVS = [\n",
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" [{\"role\": \"user\", \"content\": PROMPT}, {\"role\": \"assistant\", \"content\": RESPONSE1}],\n",
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" [{\"role\": \"user\", \"content\": PROMPT}, {\"role\": \"assistant\", \"content\": RESPONSE2}],\n",
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"]\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\")\n",
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"prompts = tokenizer.apply_chat_template(CONVS, tokenize=False)\n",
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"\n",
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"url = \"http://localhost:30030/classify\"\n",
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"data = {\"model\": \"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\", \"text\": prompts}\n",
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"\n",
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"responses = requests.post(url, json=data).json()\n",
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"for response in responses:\n",
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" print_highlight(f\"reward: {response['embedding'][0]}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(reward_process)"
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]
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}
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],
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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