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Update Strategy in SamplingParams to be a union

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Hardik Shah 2025-01-14 15:56:02 -08:00 • committed by Ashwin Bharambe
parent 300e6e2702
commit dea575c994
28 changed files with 600 additions and 377 deletions
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169
docs/getting_started.ipynb
View file

@ -713,13 +713,15 @@
],
"source": [
"import os\n",
"\n",
"from google.colab import userdata\n",
"\n",
"os.environ['TOGETHER_API_KEY'] = userdata.get('TOGETHER_API_KEY')\n",
"os.environ[\"TOGETHER_API_KEY\"] = userdata.get(\"TOGETHER_API_KEY\")\n",
"\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
"\n",
"client = LlamaStackAsLibraryClient(\"together\")\n",
"_ = client.initialize()"
"_ = client.initialize()\n"
]
},
{
@ -769,6 +771,7 @@
],
"source": [
"from rich.pretty import pprint\n",
"\n",
"print(\"Available models:\")\n",
"for m in client.models.list():\n",
" print(f\"{m.identifier} (provider's alias: {m.provider_resource_id}) \")\n",
@ -777,7 +780,7 @@
"print(\"Available shields (safety models):\")\n",
"for s in client.shields.list():\n",
" print(s.identifier)\n",
"print(\"----\")"
"print(\"----\")\n"
]
},
{
@ -822,7 +825,7 @@
"source": [
"model_id = \"meta-llama/Llama-3.1-70B-Instruct\"\n",
"\n",
"model_id"
"model_id\n"
]
},
{
@ -863,11 +866,11 @@
" model_id=model_id,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n",
" {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"}\n",
" {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n",
" ],\n",
")\n",
"\n",
"print(response.completion_message.content)"
"print(response.completion_message.content)\n"
]
},
{
@ -900,12 +903,13 @@
"source": [
"from termcolor import cprint\n",
"\n",
"\n",
"def chat_loop():\n",
" conversation_history = []\n",
" while True:\n",
" user_input = input('User> ')\n",
" if user_input.lower() in ['exit', 'quit', 'bye']:\n",
" cprint('Ending conversation. Goodbye!', 'yellow')\n",
" user_input = input(\"User> \")\n",
" if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n",
" cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n",
" break\n",
"\n",
" user_message = {\"role\": \"user\", \"content\": user_input}\n",
@ -915,14 +919,15 @@
" messages=conversation_history,\n",
" model_id=model_id,\n",
" )\n",
" cprint(f'> Response: {response.completion_message.content}', 'cyan')\n",
" cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n",
"\n",
" assistant_message = {\n",
" \"role\": \"assistant\", # was user\n",
" \"role\": \"assistant\", # was user\n",
" \"content\": response.completion_message.content,\n",
" }\n",
" conversation_history.append(assistant_message)\n",
"\n",
"\n",
"chat_loop()\n"
]
},
@ -978,21 +983,18 @@
"source": [
"from llama_stack_client.lib.inference.event_logger import EventLogger\n",
"\n",
"message = {\n",
" \"role\": \"user\",\n",
" \"content\": 'Write me a sonnet about llama'\n",
"}\n",
"print(f'User> {message[\"content\"]}', 'green')\n",
"message = {\"role\": \"user\", \"content\": \"Write me a sonnet about llama\"}\n",
"print(f'User> {message[\"content\"]}', \"green\")\n",
"\n",
"response = client.inference.chat_completion(\n",
" messages=[message],\n",
" model_id=model_id,\n",
" stream=True, # <-----------\n",
" stream=True, # <-----------\n",
")\n",
"\n",
"# Print the tokens while they are received\n",
"for log in EventLogger().log(response):\n",
" log.print()"
" log.print()\n"
]
},
{
@ -1045,26 +1047,26 @@
"source": [
"from pydantic import BaseModel\n",
"\n",
"\n",
"class Output(BaseModel):\n",
" name: str\n",
" year_born: str\n",
" year_retired: str\n",
"\n",
"\n",
"user_input = \"Michael Jordan was born in 1963. He played basketball for the Chicago Bulls. He retired in 2003. Extract this information into JSON for me. \"\n",
"response = client.inference.completion(\n",
" model_id=model_id,\n",
" content=user_input,\n",
" stream=False,\n",
" sampling_params={\n",
" \"max_tokens\": 50,\n",
" },\n",
" sampling_params={\"strategy\": {\"type\": \"greedy\"}, \"max_tokens\": 50},\n",
" response_format={\n",
" \"type\": \"json_schema\",\n",
" \"json_schema\": Output.model_json_schema(),\n",
" },\n",
")\n",
"\n",
"pprint(response)"
"pprint(response)\n"
]
},
{
@ -1220,7 +1222,7 @@
" shield_id=available_shields[0],\n",
" params={},\n",
" )\n",
" pprint(response)"
" pprint(response)\n"
]
},
{
@ -1489,8 +1491,8 @@
"source": [
"from llama_stack_client.lib.agents.agent import Agent\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from llama_stack_client.types import Attachment\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from termcolor import cprint\n",
"\n",
"urls = [\"chat.rst\", \"llama3.rst\", \"datasets.rst\", \"lora_finetune.rst\"]\n",
@ -1522,14 +1524,14 @@
" ),\n",
"]\n",
"for prompt, attachments in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n",
" cprint(f\"User> {prompt}\", \"green\")\n",
" response = rag_agent.create_turn(\n",
" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
" attachments=attachments,\n",
" session_id=session_id,\n",
" )\n",
" for log in EventLogger().log(response):\n",
" log.print()"
" log.print()\n"
]
},
{
@ -1560,8 +1562,8 @@
"search_tool = {\n",
" \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\")\n",
"}"
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\"),\n",
"}\n"
]
},
{
@ -1608,7 +1610,7 @@
"\n",
"session_id = agent.create_session(\"test-session\")\n",
"for prompt in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n",
" cprint(f\"User> {prompt}\", \"green\")\n",
" response = agent.create_turn(\n",
" messages=[\n",
" {\n",
@ -1758,7 +1760,7 @@
" search_tool,\n",
" {\n",
" \"type\": \"code_interpreter\",\n",
" }\n",
" },\n",
" ],\n",
" tool_choice=\"required\",\n",
" input_shields=[],\n",
@ -1788,7 +1790,7 @@
"]\n",
"\n",
"for prompt in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n",
" cprint(f\"User> {prompt}\", \"green\")\n",
" response = codex_agent.create_turn(\n",
" messages=[\n",
" {\n",
@ -1841,27 +1843,57 @@
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"# Read the CSV file\n",
"df = pd.read_csv('/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv')\n",
"df = pd.read_csv(\"/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv\")\n",
"\n",
"# Extract the year and inflation rate from the CSV file\n",
"df['Year'] = pd.to_datetime(df['Year'], format='%Y')\n",
"df = df.rename(columns={'Jan': 'Jan Rate', 'Feb': 'Feb Rate', 'Mar': 'Mar Rate', 'Apr': 'Apr Rate', 'May': 'May Rate', 'Jun': 'Jun Rate', 'Jul': 'Jul Rate', 'Aug': 'Aug Rate', 'Sep': 'Sep Rate', 'Oct': 'Oct Rate', 'Nov': 'Nov Rate', 'Dec': 'Dec Rate'})\n",
"df[\"Year\"] = pd.to_datetime(df[\"Year\"], format=\"%Y\")\n",
"df = df.rename(\n",
" columns={\n",
" \"Jan\": \"Jan Rate\",\n",
" \"Feb\": \"Feb Rate\",\n",
" \"Mar\": \"Mar Rate\",\n",
" \"Apr\": \"Apr Rate\",\n",
" \"May\": \"May Rate\",\n",
" \"Jun\": \"Jun Rate\",\n",
" \"Jul\": \"Jul Rate\",\n",
" \"Aug\": \"Aug Rate\",\n",
" \"Sep\": \"Sep Rate\",\n",
" \"Oct\": \"Oct Rate\",\n",
" \"Nov\": \"Nov Rate\",\n",
" \"Dec\": \"Dec Rate\",\n",
" }\n",
")\n",
"\n",
"# Calculate the average yearly inflation rate\n",
"df['Yearly Inflation'] = df[['Jan Rate', 'Feb Rate', 'Mar Rate', 'Apr Rate', 'May Rate', 'Jun Rate', 'Jul Rate', 'Aug Rate', 'Sep Rate', 'Oct Rate', 'Nov Rate', 'Dec Rate']].mean(axis=1)\n",
"df[\"Yearly Inflation\"] = df[\n",
" [\n",
" \"Jan Rate\",\n",
" \"Feb Rate\",\n",
" \"Mar Rate\",\n",
" \"Apr Rate\",\n",
" \"May Rate\",\n",
" \"Jun Rate\",\n",
" \"Jul Rate\",\n",
" \"Aug Rate\",\n",
" \"Sep Rate\",\n",
" \"Oct Rate\",\n",
" \"Nov Rate\",\n",
" \"Dec Rate\",\n",
" ]\n",
"].mean(axis=1)\n",
"\n",
"# Plot the average yearly inflation rate as a time series\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(df['Year'], df['Yearly Inflation'], marker='o')\n",
"plt.title('Average Yearly Inflation Rate')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Inflation Rate (%)')\n",
"plt.plot(df[\"Year\"], df[\"Yearly Inflation\"], marker=\"o\")\n",
"plt.title(\"Average Yearly Inflation Rate\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Inflation Rate (%)\")\n",
"plt.grid(True)\n",
"plt.show()"
"plt.show()\n"
]
},
{
@ -2035,6 +2067,8 @@
"source": [
"# disable logging for clean server logs\n",
"import logging\n",
"\n",
"\n",
"def remove_root_handlers():\n",
" root_logger = logging.getLogger()\n",
" for handler in root_logger.handlers[:]:\n",
@ -2042,7 +2076,7 @@
" print(f\"Removed handler {handler.__class__.__name__} from root logger\")\n",
"\n",
"\n",
"remove_root_handlers()"
"remove_root_handlers()\n"
]
},
{
@ -2083,10 +2117,10 @@
}
],
"source": [
"from google.colab import userdata\n",
"from llama_stack_client.lib.agents.agent import Agent\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from google.colab import userdata\n",
"\n",
"agent_config = AgentConfig(\n",
" model=\"meta-llama/Llama-3.1-405B-Instruct\",\n",
@ -2096,7 +2130,7 @@
" {\n",
" \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\")\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\"),\n",
" }\n",
" ]\n",
" ),\n",
@ -2125,7 +2159,7 @@
" )\n",
"\n",
" for log in EventLogger().log(response):\n",
" log.print()"
" log.print()\n"
]
},
{
@ -2265,20 +2299,21 @@
"source": [
"print(f\"Getting traces for session_id={session_id}\")\n",
"import json\n",
"\n",
"from rich.pretty import pprint\n",
"\n",
"agent_logs = []\n",
"\n",
"for span in client.telemetry.query_spans(\n",
" attribute_filters=[\n",
" {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n",
" {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n",
" ],\n",
" attributes_to_return=[\"input\", \"output\"]\n",
" ):\n",
" if span.attributes[\"output\"] != \"no shields\":\n",
" agent_logs.append(span.attributes)\n",
" attributes_to_return=[\"input\", \"output\"],\n",
"):\n",
" if span.attributes[\"output\"] != \"no shields\":\n",
" agent_logs.append(span.attributes)\n",
"\n",
"pprint(agent_logs)"
"pprint(agent_logs)\n"
]
},
{
@ -2389,23 +2424,25 @@
"eval_rows = []\n",
"\n",
"for log in agent_logs:\n",
" last_msg = log['input'][-1]\n",
" if \"\\\"role\\\":\\\"user\\\"\" in last_msg:\n",
" eval_rows.append(\n",
" {\n",
" \"input_query\": last_msg,\n",
" \"generated_answer\": log[\"output\"],\n",
" # check if generated_answer uses tools brave_search\n",
" \"expected_answer\": \"brave_search\",\n",
" },\n",
" )\n",
" last_msg = log[\"input\"][-1]\n",
" if '\"role\":\"user\"' in last_msg:\n",
" eval_rows.append(\n",
" {\n",
" \"input_query\": last_msg,\n",
" \"generated_answer\": log[\"output\"],\n",
" # check if generated_answer uses tools brave_search\n",
" \"expected_answer\": \"brave_search\",\n",
" },\n",
" )\n",
"\n",
"pprint(eval_rows)\n",
"scoring_params = {\n",
" \"basic::subset_of\": None,\n",
"}\n",
"scoring_response = client.scoring.score(input_rows=eval_rows, scoring_functions=scoring_params)\n",
"pprint(scoring_response)"
"scoring_response = client.scoring.score(\n",
" input_rows=eval_rows, scoring_functions=scoring_params\n",
")\n",
"pprint(scoring_response)\n"
]
},
{
@ -2506,7 +2543,9 @@
"EXPECTED_RESPONSE: {expected_answer}\n",
"\"\"\"\n",
"\n",
"input_query = \"What are the top 5 topics that were explained? Only list succinct bullet points.\"\n",
"input_query = (\n",
" \"What are the top 5 topics that were explained? Only list succinct bullet points.\"\n",
")\n",
"generated_answer = \"\"\"\n",
"Here are the top 5 topics that were explained in the documentation for Torchtune:\n",
"\n",
@ -2537,7 +2576,7 @@
"}\n",
"\n",
"response = client.scoring.score(input_rows=rows, scoring_functions=scoring_params)\n",
"pprint(response)"
"pprint(response)\n"
]
},
{

63
docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb
View file

@ -618,11 +618,13 @@
],
"source": [
"import os\n",
"\n",
"from google.colab import userdata\n",
"\n",
"os.environ['TOGETHER_API_KEY'] = userdata.get('TOGETHER_API_KEY')\n",
"os.environ[\"TOGETHER_API_KEY\"] = userdata.get(\"TOGETHER_API_KEY\")\n",
"\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
"\n",
"client = LlamaStackAsLibraryClient(\"together\")\n",
"_ = client.initialize()\n",
"\n",
@ -631,7 +633,7 @@
" model_id=\"meta-llama/Llama-3.1-405B-Instruct\",\n",
" provider_model_id=\"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\",\n",
" provider_id=\"together\",\n",
")"
")\n"
]
},
{
@ -668,7 +670,7 @@
"source": [
"name = \"llamastack/mmmu\"\n",
"subset = \"Agriculture\"\n",
"split = \"dev\""
"split = \"dev\"\n"
]
},
{
@ -914,9 +916,10 @@
],
"source": [
"import datasets\n",
"\n",
"ds = datasets.load_dataset(path=name, name=subset, split=split)\n",
"ds = ds.select_columns([\"chat_completion_input\", \"input_query\", \"expected_answer\"])\n",
"eval_rows = ds.to_pandas().to_dict(orient=\"records\")"
"eval_rows = ds.to_pandas().to_dict(orient=\"records\")\n"
]
},
{
@ -1014,8 +1017,8 @@
}
],
"source": [
"from tqdm import tqdm\n",
"from rich.pretty import pprint\n",
"from tqdm import tqdm\n",
"\n",
"SYSTEM_PROMPT_TEMPLATE = \"\"\"\n",
"You are an expert in {subject} whose job is to answer questions from the user using images.\n",
@ -1039,7 +1042,7 @@
"client.eval_tasks.register(\n",
" eval_task_id=\"meta-reference::mmmu\",\n",
" dataset_id=f\"mmmu-{subset}-{split}\",\n",
" scoring_functions=[\"basic::regex_parser_multiple_choice_answer\"]\n",
" scoring_functions=[\"basic::regex_parser_multiple_choice_answer\"],\n",
")\n",
"\n",
"response = client.eval.evaluate_rows(\n",
@ -1052,16 +1055,17 @@
" \"type\": \"model\",\n",
" \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n",
" \"sampling_params\": {\n",
" \"temperature\": 0.0,\n",
" \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" \"max_tokens\": 4096,\n",
" \"top_p\": 0.9,\n",
" \"repeat_penalty\": 1.0,\n",
" },\n",
" \"system_message\": system_message\n",
" }\n",
" }\n",
" \"system_message\": system_message,\n",
" },\n",
" },\n",
")\n",
"pprint(response)"
"pprint(response)\n"
]
},
{
@ -1098,8 +1102,8 @@
" \"input_query\": {\"type\": \"string\"},\n",
" \"expected_answer\": {\"type\": \"string\"},\n",
" \"chat_completion_input\": {\"type\": \"chat_completion_input\"},\n",
" }\n",
")"
" },\n",
")\n"
]
},
{
@ -1113,7 +1117,7 @@
"eval_rows = client.datasetio.get_rows_paginated(\n",
" dataset_id=simpleqa_dataset_id,\n",
" rows_in_page=5,\n",
")"
")\n"
]
},
{
@ -1209,7 +1213,7 @@
"client.eval_tasks.register(\n",
" eval_task_id=\"meta-reference::simpleqa\",\n",
" dataset_id=simpleqa_dataset_id,\n",
" scoring_functions=[\"llm-as-judge::405b-simpleqa\"]\n",
" scoring_functions=[\"llm-as-judge::405b-simpleqa\"],\n",
")\n",
"\n",
"response = client.eval.evaluate_rows(\n",
@ -1222,15 +1226,16 @@
" \"type\": \"model\",\n",
" \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n",
" \"sampling_params\": {\n",
" \"temperature\": 0.0,\n",
" \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" \"max_tokens\": 4096,\n",
" \"top_p\": 0.9,\n",
" \"repeat_penalty\": 1.0,\n",
" },\n",
" }\n",
" }\n",
" },\n",
" },\n",
")\n",
"pprint(response)"
"pprint(response)\n"
]
},
{
@ -1347,23 +1352,19 @@
"agent_config = {\n",
" \"model\": \"meta-llama/Llama-3.1-405B-Instruct\",\n",
" \"instructions\": \"You are a helpful assistant\",\n",
" \"sampling_params\": {\n",
" \"strategy\": \"greedy\",\n",
" \"temperature\": 0.0,\n",
" \"top_p\": 0.95,\n",
" },\n",
" \"sampling_params\": {\"strategy\": {\"type\": \"greedy\"}},\n",
" \"tools\": [\n",
" {\n",
" \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\")\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\"),\n",
" }\n",
" ],\n",
" \"tool_choice\": \"auto\",\n",
" \"tool_prompt_format\": \"json\",\n",
" \"input_shields\": [],\n",
" \"output_shields\": [],\n",
" \"enable_session_persistence\": False\n",
" \"enable_session_persistence\": False,\n",
"}\n",
"\n",
"response = client.eval.evaluate_rows(\n",
@ -1375,10 +1376,10 @@
" \"eval_candidate\": {\n",
" \"type\": \"agent\",\n",
" \"config\": agent_config,\n",
" }\n",
" }\n",
" },\n",
" },\n",
")\n",
"pprint(response)"
"pprint(response)\n"
]
}
],

94
docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb
View file

@ -1336,6 +1336,7 @@
],
"source": [
"from rich.pretty import pprint\n",
"\n",
"print(\"Available models:\")\n",
"for m in client.models.list():\n",
" print(f\"{m.identifier} (provider's alias: {m.provider_resource_id}) \")\n",
@ -1344,7 +1345,7 @@
"print(\"Available shields (safety models):\")\n",
"for s in client.shields.list():\n",
" print(s.identifier)\n",
"print(\"----\")"
"print(\"----\")\n"
]
},
{
@ -1389,7 +1390,7 @@
"source": [
"model_id = \"meta-llama/Llama-3.1-70B-Instruct\"\n",
"\n",
"model_id"
"model_id\n"
]
},
{
@ -1432,11 +1433,11 @@
" model_id=model_id,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n",
" {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"}\n",
" {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n",
" ],\n",
")\n",
"\n",
"print(response.completion_message.content)"
"print(response.completion_message.content)\n"
]
},
{
@ -1489,12 +1490,13 @@
"source": [
"from termcolor import cprint\n",
"\n",
"\n",
"def chat_loop():\n",
" conversation_history = []\n",
" while True:\n",
" user_input = input('User> ')\n",
" if user_input.lower() in ['exit', 'quit', 'bye']:\n",
" cprint('Ending conversation. Goodbye!', 'yellow')\n",
" user_input = input(\"User> \")\n",
" if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n",
" cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n",
" break\n",
"\n",
" user_message = {\"role\": \"user\", \"content\": user_input}\n",
@ -1504,15 +1506,16 @@
" messages=conversation_history,\n",
" model_id=model_id,\n",
" )\n",
" cprint(f'> Response: {response.completion_message.content}', 'cyan')\n",
" cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n",
"\n",
" assistant_message = {\n",
" \"role\": \"assistant\", # was user\n",
" \"role\": \"assistant\", # was user\n",
" \"content\": response.completion_message.content,\n",
" \"stop_reason\": response.completion_message.stop_reason,\n",
" }\n",
" conversation_history.append(assistant_message)\n",
"\n",
"\n",
"chat_loop()\n"
]
},
@ -1568,21 +1571,18 @@
"source": [
"from llama_stack_client.lib.inference.event_logger import EventLogger\n",
"\n",
"message = {\n",
" \"role\": \"user\",\n",
" \"content\": 'Write me a sonnet about llama'\n",
"}\n",
"print(f'User> {message[\"content\"]}', 'green')\n",
"message = {\"role\": \"user\", \"content\": \"Write me a sonnet about llama\"}\n",
"print(f'User> {message[\"content\"]}', \"green\")\n",
"\n",
"response = client.inference.chat_completion(\n",
" messages=[message],\n",
" model_id=model_id,\n",
" stream=True, # <-----------\n",
" stream=True, # <-----------\n",
")\n",
"\n",
"# Print the tokens while they are received\n",
"for log in EventLogger().log(response):\n",
" log.print()"
" log.print()\n"
]
},
{
@ -1648,17 +1648,22 @@
"source": [
"from pydantic import BaseModel\n",
"\n",
"\n",
"class Output(BaseModel):\n",
" name: str\n",
" year_born: str\n",
" year_retired: str\n",
"\n",
"\n",
"user_input = \"Michael Jordan was born in 1963. He played basketball for the Chicago Bulls. He retired in 2003. Extract this information into JSON for me. \"\n",
"response = client.inference.completion(\n",
" model_id=model_id,\n",
" content=user_input,\n",
" stream=False,\n",
" sampling_params={\n",
" \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" \"max_tokens\": 50,\n",
" },\n",
" response_format={\n",
@ -1667,7 +1672,7 @@
" },\n",
")\n",
"\n",
"pprint(response)"
"pprint(response)\n"
]
},
{
@ -1823,7 +1828,7 @@
" shield_id=available_shields[0],\n",
" params={},\n",
" )\n",
" pprint(response)"
" pprint(response)\n"
]
},
{
@ -2025,7 +2030,7 @@
"\n",
"session_id = agent.create_session(\"test-session\")\n",
"for prompt in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n",
" cprint(f\"User> {prompt}\", \"green\")\n",
" response = agent.create_turn(\n",
" messages=[\n",
" {\n",
@ -2451,8 +2456,8 @@
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"# Load data\n",
"df = pd.read_csv(\"/tmp/tmpvzjigv7g/n2OzlTWhinflation.csv\")\n",
@ -2536,10 +2541,10 @@
}
],
"source": [
"from google.colab import userdata\n",
"from llama_stack_client.lib.agents.agent import Agent\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from google.colab import userdata\n",
"\n",
"agent_config = AgentConfig(\n",
" model=\"meta-llama/Llama-3.1-405B-Instruct-FP8\",\n",
@ -2570,7 +2575,7 @@
" )\n",
"\n",
" for log in EventLogger().log(response):\n",
" log.print()"
" log.print()\n"
]
},
{
@ -2790,20 +2795,21 @@
"source": [
"print(f\"Getting traces for session_id={session_id}\")\n",
"import json\n",
"\n",
"from rich.pretty import pprint\n",
"\n",
"agent_logs = []\n",
"\n",
"for span in client.telemetry.query_spans(\n",
" attribute_filters=[\n",
" {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n",
" {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n",
" ],\n",
" attributes_to_return=[\"input\", \"output\"]\n",
" ):\n",
" if span.attributes[\"output\"] != \"no shields\":\n",
" agent_logs.append(span.attributes)\n",
" attributes_to_return=[\"input\", \"output\"],\n",
"):\n",
" if span.attributes[\"output\"] != \"no shields\":\n",
" agent_logs.append(span.attributes)\n",
"\n",
"pprint(agent_logs)"
"pprint(agent_logs)\n"
]
},
{
@ -2914,23 +2920,25 @@
"eval_rows = []\n",
"\n",
"for log in agent_logs:\n",
" last_msg = log['input'][-1]\n",
" if \"\\\"role\\\":\\\"user\\\"\" in last_msg:\n",
" eval_rows.append(\n",
" {\n",
" \"input_query\": last_msg,\n",
" \"generated_answer\": log[\"output\"],\n",
" # check if generated_answer uses tools brave_search\n",
" \"expected_answer\": \"brave_search\",\n",
" },\n",
" )\n",
" last_msg = log[\"input\"][-1]\n",
" if '\"role\":\"user\"' in last_msg:\n",
" eval_rows.append(\n",
" {\n",
" \"input_query\": last_msg,\n",
" \"generated_answer\": log[\"output\"],\n",
" # check if generated_answer uses tools brave_search\n",
" \"expected_answer\": \"brave_search\",\n",
" },\n",
" )\n",
"\n",
"pprint(eval_rows)\n",
"scoring_params = {\n",
" \"basic::subset_of\": None,\n",
"}\n",
"scoring_response = client.scoring.score(input_rows=eval_rows, scoring_functions=scoring_params)\n",
"pprint(scoring_response)"
"scoring_response = client.scoring.score(\n",
" input_rows=eval_rows, scoring_functions=scoring_params\n",
")\n",
"pprint(scoring_response)\n"
]
},
{
@ -3031,7 +3039,9 @@
"EXPECTED_RESPONSE: {expected_answer}\n",
"\"\"\"\n",
"\n",
"input_query = \"What are the top 5 topics that were explained? Only list succinct bullet points.\"\n",
"input_query = (\n",
" \"What are the top 5 topics that were explained? Only list succinct bullet points.\"\n",
")\n",
"generated_answer = \"\"\"\n",
"Here are the top 5 topics that were explained in the documentation for Torchtune:\n",
"\n",
@ -3062,7 +3072,7 @@
"}\n",
"\n",
"response = client.scoring.score(input_rows=rows, scoring_functions=scoring_params)\n",
"pprint(response)"
"pprint(response)\n"
]
}
],

106
docs/resources/llama-stack-spec.html
View file

@ -3514,6 +3514,20 @@
"tool_calls"
]
},
"GreedySamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "greedy",
"default": "greedy"
}
},
"additionalProperties": false,
"required": [
"type"
]
},
"ImageContentItem": {
"type": "object",
"properties": {
@ -3581,20 +3595,17 @@
"type": "object",
"properties": {
"strategy": {
"$ref": "#/components/schemas/SamplingStrategy",
"default": "greedy"
},
"temperature": {
"type": "number",
"default": 0.0
},
"top_p": {
"type": "number",
"default": 0.95
},
"top_k": {
"type": "integer",
"default": 0
"oneOf": [
{
"$ref": "#/components/schemas/GreedySamplingStrategy"
},
{
"$ref": "#/components/schemas/TopPSamplingStrategy"
},
{
"$ref": "#/components/schemas/TopKSamplingStrategy"
}
]
},
"max_tokens": {
"type": "integer",
@ -3610,14 +3621,6 @@
"strategy"
]
},
"SamplingStrategy": {
"type": "string",
"enum": [
"greedy",
"top_p",
"top_k"
]
},
"StopReason": {
"type": "string",
"enum": [
@ -3871,6 +3874,45 @@
"content"
]
},
"TopKSamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "top_k",
"default": "top_k"
},
"top_k": {
"type": "integer"
}
},
"additionalProperties": false,
"required": [
"type",
"top_k"
]
},
"TopPSamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "top_p",
"default": "top_p"
},
"temperature": {
"type": "number"
},
"top_p": {
"type": "number",
"default": 0.95
}
},
"additionalProperties": false,
"required": [
"type"
]
},
"URL": {
"type": "object",
"properties": {
@ -8887,6 +8929,10 @@
"name": "GraphMemoryBankParams",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/GraphMemoryBankParams\" />"
},
{
"name": "GreedySamplingStrategy",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/GreedySamplingStrategy\" />"
},
{
"name": "HealthInfo",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/HealthInfo\" />"
@ -9136,10 +9182,6 @@
"name": "SamplingParams",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/SamplingParams\" />"
},
{
"name": "SamplingStrategy",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/SamplingStrategy\" />"
},
{
"name": "SaveSpansToDatasetRequest",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/SaveSpansToDatasetRequest\" />"
@ -9317,6 +9359,14 @@
{
"name": "ToolRuntime"
},
{
"name": "TopKSamplingStrategy",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/TopKSamplingStrategy\" />"
},
{
"name": "TopPSamplingStrategy",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/TopPSamplingStrategy\" />"
},
{
"name": "Trace",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/Trace\" />"
@ -9456,6 +9506,7 @@
"GetSpanTreeRequest",
"GraphMemoryBank",
"GraphMemoryBankParams",
"GreedySamplingStrategy",
"HealthInfo",
"ImageContentItem",
"InferenceStep",
@ -9513,7 +9564,6 @@
"RunShieldResponse",
"SafetyViolation",
"SamplingParams",
"SamplingStrategy",
"SaveSpansToDatasetRequest",
"ScoreBatchRequest",
"ScoreBatchResponse",
@ -9553,6 +9603,8 @@
"ToolPromptFormat",
"ToolResponse",
"ToolResponseMessage",
"TopKSamplingStrategy",
"TopPSamplingStrategy",
"Trace",
"TrainingConfig",
"Turn",

75
docs/resources/llama-stack-spec.yaml
View file

@ -937,6 +937,16 @@ components:
required:
- memory_bank_type
type: object
GreedySamplingStrategy:
additionalProperties: false
properties:
type:
const: greedy
default: greedy
type: string
required:
- type
type: object
HealthInfo:
additionalProperties: false
properties:
@ -2064,26 +2074,13 @@ components:
default: 1.0
type: number
strategy:
$ref: '#/components/schemas/SamplingStrategy'
default: greedy
temperature:
default: 0.0
type: number
top_k:
default: 0
type: integer
top_p:
default: 0.95
type: number
oneOf:
- $ref: '#/components/schemas/GreedySamplingStrategy'
- $ref: '#/components/schemas/TopPSamplingStrategy'
- $ref: '#/components/schemas/TopKSamplingStrategy'
required:
- strategy
type: object
SamplingStrategy:
enum:
- greedy
- top_p
- top_k
type: string
SaveSpansToDatasetRequest:
additionalProperties: false
properties:
@ -2931,6 +2928,34 @@ components:
- tool_name
- content
type: object
TopKSamplingStrategy:
additionalProperties: false
properties:
top_k:
type: integer
type:
const: top_k
default: top_k
type: string
required:
- type
- top_k
type: object
TopPSamplingStrategy:
additionalProperties: false
properties:
temperature:
type: number
top_p:
default: 0.95
type: number
type:
const: top_p
default: top_p
type: string
required:
- type
type: object
Trace:
additionalProperties: false
properties:
@ -5587,6 +5612,9 @@ tags:
- description: <SchemaDefinition schemaRef="#/components/schemas/GraphMemoryBankParams"
/>
name: GraphMemoryBankParams
- description: <SchemaDefinition schemaRef="#/components/schemas/GreedySamplingStrategy"
/>
name: GreedySamplingStrategy
- description: <SchemaDefinition schemaRef="#/components/schemas/HealthInfo" />
name: HealthInfo
- description: <SchemaDefinition schemaRef="#/components/schemas/ImageContentItem"
@ -5753,9 +5781,6 @@ tags:
name: SafetyViolation
- description: <SchemaDefinition schemaRef="#/components/schemas/SamplingParams" />
name: SamplingParams
- description: <SchemaDefinition schemaRef="#/components/schemas/SamplingStrategy"
/>
name: SamplingStrategy
- description: <SchemaDefinition schemaRef="#/components/schemas/SaveSpansToDatasetRequest"
/>
name: SaveSpansToDatasetRequest
@ -5874,6 +5899,12 @@ tags:
/>
name: ToolResponseMessage
- name: ToolRuntime
- description: <SchemaDefinition schemaRef="#/components/schemas/TopKSamplingStrategy"
/>
name: TopKSamplingStrategy
- description: <SchemaDefinition schemaRef="#/components/schemas/TopPSamplingStrategy"
/>
name: TopPSamplingStrategy
- description: <SchemaDefinition schemaRef="#/components/schemas/Trace" />
name: Trace
- description: <SchemaDefinition schemaRef="#/components/schemas/TrainingConfig" />
@ -5990,6 +6021,7 @@ x-tagGroups:
- GetSpanTreeRequest
- GraphMemoryBank
- GraphMemoryBankParams
- GreedySamplingStrategy
- HealthInfo
- ImageContentItem
- InferenceStep
@ -6047,7 +6079,6 @@ x-tagGroups:
- RunShieldResponse
- SafetyViolation
- SamplingParams
- SamplingStrategy
- SaveSpansToDatasetRequest
- ScoreBatchRequest
- ScoreBatchResponse
@ -6087,6 +6118,8 @@ x-tagGroups:
- ToolPromptFormat
- ToolResponse
- ToolResponseMessage
- TopKSamplingStrategy
- TopPSamplingStrategy
- Trace
- TrainingConfig
- Turn

16
docs/source/benchmark_evaluations/index.md
View file

@ -56,9 +56,10 @@ response = client.eval.evaluate_rows(
"type": "model",
"model": "meta-llama/Llama-3.2-90B-Vision-Instruct",
"sampling_params": {
"temperature": 0.0,
"strategy": {
"type": "greedy",
},
"max_tokens": 4096,
"top_p": 0.9,
"repeat_penalty": 1.0,
},
"system_message": system_message
@ -113,9 +114,10 @@ response = client.eval.evaluate_rows(
"type": "model",
"model": "meta-llama/Llama-3.2-90B-Vision-Instruct",
"sampling_params": {
"temperature": 0.0,
"strategy": {
"type": "greedy",
},
"max_tokens": 4096,
"top_p": 0.9,
"repeat_penalty": 1.0,
},
}
@ -134,9 +136,9 @@ agent_config = {
"model": "meta-llama/Llama-3.1-405B-Instruct",
"instructions": "You are a helpful assistant",
"sampling_params": {
"strategy": "greedy",
"temperature": 0.0,
"top_p": 0.95,
"strategy": {
"type": "greedy",
},
},
"tools": [
{

6
docs/source/building_applications/index.md
View file

@ -189,7 +189,11 @@ agent_config = AgentConfig(
# Control the inference loop
max_infer_iters=5,
sampling_params={
"temperature": 0.7,
"strategy": {
"type": "top_p",
"temperature": 0.7,
"top_p": 0.95
},
"max_tokens": 2048
}
)

30
docs/source/references/evals_reference/index.md
View file

@ -92,9 +92,10 @@ response = client.eval.evaluate_rows(
"type": "model",
"model": "meta-llama/Llama-3.2-90B-Vision-Instruct",
"sampling_params": {
"temperature": 0.0,
"strategy": {
"type": "greedy",
},
"max_tokens": 4096,
"top_p": 0.9,
"repeat_penalty": 1.0,
},
"system_message": system_message
@ -149,9 +150,10 @@ response = client.eval.evaluate_rows(
"type": "model",
"model": "meta-llama/Llama-3.2-90B-Vision-Instruct",
"sampling_params": {
"temperature": 0.0,
"strategy": {
"type": "greedy",
},
"max_tokens": 4096,
"top_p": 0.9,
"repeat_penalty": 1.0,
},
}
@ -170,9 +172,9 @@ agent_config = {
"model": "meta-llama/Llama-3.1-405B-Instruct",
"instructions": "You are a helpful assistant",
"sampling_params": {
"strategy": "greedy",
"temperature": 0.0,
"top_p": 0.95,
"strategy": {
"type": "greedy",
},
},
"tools": [
{
@ -318,10 +320,9 @@ The `EvalTaskConfig` are user specified config to define:
"type": "model",
"model": "Llama3.2-3B-Instruct",
"sampling_params": {
"strategy": "greedy",
"temperature": 0,
"top_p": 0.95,
"top_k": 0,
"strategy": {
"type": "greedy",
},
"max_tokens": 0,
"repetition_penalty": 1.0
}
@ -337,10 +338,9 @@ The `EvalTaskConfig` are user specified config to define:
"type": "model",
"model": "Llama3.1-405B-Instruct",
"sampling_params": {
"strategy": "greedy",
"temperature": 0,
"top_p": 0.95,
"top_k": 0,
"strategy": {
"type": "greedy",
},
"max_tokens": 0,
"repetition_penalty": 1.0
}

1
docs/source/references/llama_cli_reference/index.md
View file

@ -214,7 +214,6 @@ llama model describe -m Llama3.2-3B-Instruct
| | } |
+-----------------------------+----------------------------------+
| Recommended sampling params | { |
| | "strategy": "top_p", |
| | "temperature": 1.0, |
| | "top_p": 0.9, |
| | "top_k": 0 |

7
docs/source/references/llama_stack_client_cli_reference.md
View file

@ -200,10 +200,9 @@ Example eval_task_config.json:
"type": "model",
"model": "Llama3.1-405B-Instruct",
"sampling_params": {
"strategy": "greedy",
"temperature": 0,
"top_p": 0.95,
"top_k": 0,
"strategy": {
"type": "greedy"
},
"max_tokens": 0,
"repetition_penalty": 1.0
}

78
docs/zero_to_hero_guide/04_Tool_Calling101.ipynb
View file

@ -26,27 +26,28 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"import json\n",
"import asyncio\n",
"import nest_asyncio\n",
"import json\n",
"import os\n",
"from typing import Dict, List\n",
"\n",
"import nest_asyncio\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from llama_stack_client import LlamaStackClient\n",
"from llama_stack_client.lib.agents.custom_tool import CustomTool\n",
"from llama_stack_client.types.shared.tool_response_message import ToolResponseMessage\n",
"from llama_stack_client.types import CompletionMessage\n",
"from llama_stack_client.lib.agents.agent import Agent\n",
"from llama_stack_client.lib.agents.custom_tool import CustomTool\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types import CompletionMessage\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from llama_stack_client.types.shared.tool_response_message import ToolResponseMessage\n",
"\n",
"# Allow asyncio to run in Jupyter Notebook\n",
"nest_asyncio.apply()\n",
"\n",
"HOST='localhost'\n",
"PORT=5001\n",
"MODEL_NAME='meta-llama/Llama-3.2-3B-Instruct'"
"HOST = \"localhost\"\n",
"PORT = 5001\n",
"MODEL_NAME = \"meta-llama/Llama-3.2-3B-Instruct\"\n"
]
},
{
@ -69,7 +70,7 @@
"outputs": [],
"source": [
"load_dotenv()\n",
"BRAVE_SEARCH_API_KEY = os.environ['BRAVE_SEARCH_API_KEY']"
"BRAVE_SEARCH_API_KEY = os.environ[\"BRAVE_SEARCH_API_KEY\"]\n"
]
},
{
@ -118,7 +119,7 @@
" cleaned = {k: v for k, v in results[idx].items() if k in selected_keys}\n",
" clean_response.append(cleaned)\n",
"\n",
" return {\"query\": query, \"top_k\": clean_response}"
" return {\"query\": query, \"top_k\": clean_response}\n"
]
},
{
@ -157,25 +158,29 @@
" for message in messages:\n",
" if isinstance(message, CompletionMessage) and message.tool_calls:\n",
" for tool_call in message.tool_calls:\n",
" if 'query' in tool_call.arguments:\n",
" query = tool_call.arguments['query']\n",
" if \"query\" in tool_call.arguments:\n",
" query = tool_call.arguments[\"query\"]\n",
" call_id = tool_call.call_id\n",
"\n",
" if query:\n",
" search_result = await self.run_impl(query)\n",
" return [ToolResponseMessage(\n",
" call_id=call_id,\n",
" role=\"ipython\",\n",
" content=self._format_response_for_agent(search_result),\n",
" tool_name=\"brave_search\"\n",
" )]\n",
" return [\n",
" ToolResponseMessage(\n",
" call_id=call_id,\n",
" role=\"ipython\",\n",
" content=self._format_response_for_agent(search_result),\n",
" tool_name=\"brave_search\",\n",
" )\n",
" ]\n",
"\n",
" return [ToolResponseMessage(\n",
" call_id=\"no_call_id\",\n",
" role=\"ipython\",\n",
" content=\"No query provided.\",\n",
" tool_name=\"brave_search\"\n",
" )]\n",
" return [\n",
" ToolResponseMessage(\n",
" call_id=\"no_call_id\",\n",
" role=\"ipython\",\n",
" content=\"No query provided.\",\n",
" tool_name=\"brave_search\",\n",
" )\n",
" ]\n",
"\n",
" def _format_response_for_agent(self, search_result):\n",
" parsed_result = json.loads(search_result)\n",
@ -186,7 +191,7 @@
" f\" URL: {result.get('url', 'No URL')}\\n\"\n",
" f\" Description: {result.get('description', 'No Description')}\\n\\n\"\n",
" )\n",
" return formatted_result"
" return formatted_result\n"
]
},
{
@ -209,7 +214,7 @@
"async def execute_search(query: str):\n",
" web_search_tool = WebSearchTool(api_key=BRAVE_SEARCH_API_KEY)\n",
" result = await web_search_tool.run_impl(query)\n",
" print(\"Search Results:\", result)"
" print(\"Search Results:\", result)\n"
]
},
{
@ -236,7 +241,7 @@
],
"source": [
"query = \"Latest developments in quantum computing\"\n",
"asyncio.run(execute_search(query))"
"asyncio.run(execute_search(query))\n"
]
},
{
@ -288,19 +293,17 @@
"\n",
" # Initialize custom tool (ensure `WebSearchTool` is defined earlier in the notebook)\n",
" webSearchTool = WebSearchTool(api_key=BRAVE_SEARCH_API_KEY)\n",
" \n",
"\n",
" # Define the agent configuration, including the model and tool setup\n",
" agent_config = AgentConfig(\n",
" model=MODEL_NAME,\n",
" instructions=\"\"\"You are a helpful assistant that responds to user queries with relevant information and cites sources when available.\"\"\",\n",
" sampling_params={\n",
" \"strategy\": \"greedy\",\n",
" \"temperature\": 1.0,\n",
" \"top_p\": 0.9,\n",
" \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" },\n",
" tools=[\n",
" webSearchTool.get_tool_definition()\n",
" ],\n",
" tools=[webSearchTool.get_tool_definition()],\n",
" tool_choice=\"auto\",\n",
" tool_prompt_format=\"python_list\",\n",
" input_shields=input_shields,\n",
@ -329,8 +332,9 @@
" async for log in EventLogger().log(response):\n",
" log.print()\n",
"\n",
"\n",
"# Run the function asynchronously in a Jupyter Notebook cell\n",
"await run_main(disable_safety=True)"
"await run_main(disable_safety=True)\n"
]
}
],

20
docs/zero_to_hero_guide/07_Agents101.ipynb
View file

@ -50,8 +50,8 @@
"outputs": [],
"source": [
"HOST = \"localhost\" # Replace with your host\n",
"PORT = 5001 # Replace with your port\n",
"MODEL_NAME='meta-llama/Llama-3.2-3B-Instruct'"
"PORT = 5001 # Replace with your port\n",
"MODEL_NAME = \"meta-llama/Llama-3.2-3B-Instruct\"\n"
]
},
{
@ -60,10 +60,12 @@
"metadata": {},
"outputs": [],
"source": [
"from dotenv import load_dotenv\n",
"import os\n",
"\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv()\n",
"BRAVE_SEARCH_API_KEY = os.environ['BRAVE_SEARCH_API_KEY']"
"BRAVE_SEARCH_API_KEY = os.environ[\"BRAVE_SEARCH_API_KEY\"]\n"
]
},
{
@ -104,20 +106,22 @@
],
"source": [
"import os\n",
"\n",
"from llama_stack_client import LlamaStackClient\n",
"from llama_stack_client.lib.agents.agent import Agent\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"\n",
"\n",
"async def agent_example():\n",
" client = LlamaStackClient(base_url=f\"http://{HOST}:{PORT}\")\n",
" agent_config = AgentConfig(\n",
" model=MODEL_NAME,\n",
" instructions=\"You are a helpful assistant! If you call builtin tools like brave search, follow the syntax brave_search.call(…)\",\n",
" sampling_params={\n",
" \"strategy\": \"greedy\",\n",
" \"temperature\": 1.0,\n",
" \"top_p\": 0.9,\n",
" \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" },\n",
" tools=[\n",
" {\n",
@ -157,7 +161,7 @@
" log.print()\n",
"\n",
"\n",
"await agent_example()"
"await agent_example()\n"
]
},
{

10
docs/zero_to_hero_guide/README.md
View file

@ -157,7 +157,15 @@ curl http://localhost:$LLAMA_STACK_PORT/alpha/inference/chat-completion
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Write me a 2-sentence poem about the moon"}
],
"sampling_params": {"temperature": 0.7, "seed": 42, "max_tokens": 512}
"sampling_params": {
"strategy": {
"type": "top_p",
"temperatrue": 0.7,
"top_p": 0.95,
},
"seed": 42,
"max_tokens": 512
}
}
EOF
```

94
docs/zero_to_hero_guide/Tool_Calling101_Using_Together's_Llama_Stack_Server.ipynb
View file

@ -83,8 +83,8 @@
},
"outputs": [],
"source": [
"LLAMA_STACK_API_TOGETHER_URL=\"https://llama-stack.together.ai\"\n",
"LLAMA31_8B_INSTRUCT = \"Llama3.1-8B-Instruct\""
"LLAMA_STACK_API_TOGETHER_URL = \"https://llama-stack.together.ai\"\n",
"LLAMA31_8B_INSTRUCT = \"Llama3.1-8B-Instruct\"\n"
]
},
{
@ -107,12 +107,13 @@
" AgentConfigToolSearchToolDefinition,\n",
")\n",
"\n",
"\n",
"# Helper function to create an agent with tools\n",
"async def create_tool_agent(\n",
" client: LlamaStackClient,\n",
" tools: List[Dict],\n",
" instructions: str = \"You are a helpful assistant\",\n",
" model: str = LLAMA31_8B_INSTRUCT\n",
" model: str = LLAMA31_8B_INSTRUCT,\n",
") -> Agent:\n",
" \"\"\"Create an agent with specified tools.\"\"\"\n",
" print(\"Using the following model: \", model)\n",
@ -120,9 +121,9 @@
" model=model,\n",
" instructions=instructions,\n",
" sampling_params={\n",
" \"strategy\": \"greedy\",\n",
" \"temperature\": 1.0,\n",
" \"top_p\": 0.9,\n",
" \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" },\n",
" tools=tools,\n",
" tool_choice=\"auto\",\n",
@ -130,7 +131,7 @@
" enable_session_persistence=True,\n",
" )\n",
"\n",
" return Agent(client, agent_config)"
" return Agent(client, agent_config)\n"
]
},
{
@ -172,7 +173,8 @@
],
"source": [
"# comment this if you don't have a BRAVE_SEARCH_API_KEY\n",
"os.environ[\"BRAVE_SEARCH_API_KEY\"] = 'YOUR_BRAVE_SEARCH_API_KEY'\n",
"os.environ[\"BRAVE_SEARCH_API_KEY\"] = \"YOUR_BRAVE_SEARCH_API_KEY\"\n",
"\n",
"\n",
"async def create_search_agent(client: LlamaStackClient) -> Agent:\n",
" \"\"\"Create an agent with Brave Search capability.\"\"\"\n",
@ -186,8 +188,8 @@
"\n",
" return await create_tool_agent(\n",
" client=client,\n",
" tools=[search_tool], # set this to [] if you don't have a BRAVE_SEARCH_API_KEY\n",
" model = LLAMA31_8B_INSTRUCT,\n",
" tools=[search_tool], # set this to [] if you don't have a BRAVE_SEARCH_API_KEY\n",
" model=LLAMA31_8B_INSTRUCT,\n",
" instructions=\"\"\"\n",
" You are a research assistant that can search the web.\n",
" Always cite your sources with URLs when providing information.\n",
@ -198,9 +200,10 @@
"\n",
" SOURCES:\n",
" - [Source title](URL)\n",
" \"\"\"\n",
" \"\"\",\n",
" )\n",
"\n",
"\n",
"# Example usage\n",
"async def search_example():\n",
" client = LlamaStackClient(base_url=LLAMA_STACK_API_TOGETHER_URL)\n",
@ -212,7 +215,7 @@
" # Example queries\n",
" queries = [\n",
" \"What are the latest developments in quantum computing?\",\n",
" #\"Who won the most recent Super Bowl?\",\n",
" # \"Who won the most recent Super Bowl?\",\n",
" ]\n",
"\n",
" for query in queries:\n",
@ -227,8 +230,9 @@
" async for log in EventLogger().log(response):\n",
" log.print()\n",
"\n",
"\n",
"# Run the example (in Jupyter, use asyncio.run())\n",
"await search_example()"
"await search_example()\n"
]
},
{
@ -286,12 +290,16 @@
}
],
"source": [
"from typing import TypedDict, Optional, Dict, Any\n",
"from datetime import datetime\n",
"import json\n",
"from llama_stack_client.types.tool_param_definition_param import ToolParamDefinitionParam\n",
"from llama_stack_client.types import CompletionMessage,ToolResponseMessage\n",
"from datetime import datetime\n",
"from typing import Any, Dict, Optional, TypedDict\n",
"\n",
"from llama_stack_client.lib.agents.custom_tool import CustomTool\n",
"from llama_stack_client.types import CompletionMessage, ToolResponseMessage\n",
"from llama_stack_client.types.tool_param_definition_param import (\n",
" ToolParamDefinitionParam,\n",
")\n",
"\n",
"\n",
"class WeatherTool(CustomTool):\n",
" \"\"\"Example custom tool for weather information.\"\"\"\n",
@ -305,16 +313,15 @@
" def get_params_definition(self) -> Dict[str, ToolParamDefinitionParam]:\n",
" return {\n",
" \"location\": ToolParamDefinitionParam(\n",
" param_type=\"str\",\n",
" description=\"City or location name\",\n",
" required=True\n",
" param_type=\"str\", description=\"City or location name\", required=True\n",
" ),\n",
" \"date\": ToolParamDefinitionParam(\n",
" param_type=\"str\",\n",
" description=\"Optional date (YYYY-MM-DD)\",\n",
" required=False\n",
" )\n",
" required=False,\n",
" ),\n",
" }\n",
"\n",
" async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:\n",
" assert len(messages) == 1, \"Expected single message\"\n",
"\n",
@ -337,20 +344,14 @@
" )\n",
" return [message]\n",
"\n",
" async def run_impl(self, location: str, date: Optional[str] = None) -> Dict[str, Any]:\n",
" async def run_impl(\n",
" self, location: str, date: Optional[str] = None\n",
" ) -> Dict[str, Any]:\n",
" \"\"\"Simulate getting weather data (replace with actual API call).\"\"\"\n",
" # Mock implementation\n",
" if date:\n",
" return {\n",
" \"temperature\": 90.1,\n",
" \"conditions\": \"sunny\",\n",
" \"humidity\": 40.0\n",
" }\n",
" return {\n",
" \"temperature\": 72.5,\n",
" \"conditions\": \"partly cloudy\",\n",
" \"humidity\": 65.0\n",
" }\n",
" return {\"temperature\": 90.1, \"conditions\": \"sunny\", \"humidity\": 40.0}\n",
" return {\"temperature\": 72.5, \"conditions\": \"partly cloudy\", \"humidity\": 65.0}\n",
"\n",
"\n",
"async def create_weather_agent(client: LlamaStackClient) -> Agent:\n",
@ -358,38 +359,33 @@
"\n",
" # Create the agent with the tool\n",
" weather_tool = WeatherTool()\n",
" \n",
"\n",
" agent_config = AgentConfig(\n",
" model=LLAMA31_8B_INSTRUCT,\n",
" #model=model_name,\n",
" # model=model_name,\n",
" instructions=\"\"\"\n",
" You are a weather assistant that can provide weather information.\n",
" Always specify the location clearly in your responses.\n",
" Include both temperature and conditions in your summaries.\n",
" \"\"\",\n",
" sampling_params={\n",
" \"strategy\": \"greedy\",\n",
" \"temperature\": 1.0,\n",
" \"top_p\": 0.9,\n",
" \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" },\n",
" tools=[\n",
" weather_tool.get_tool_definition()\n",
" ],\n",
" tools=[weather_tool.get_tool_definition()],\n",
" tool_choice=\"auto\",\n",
" tool_prompt_format=\"json\",\n",
" input_shields=[],\n",
" output_shields=[],\n",
" enable_session_persistence=True\n",
" enable_session_persistence=True,\n",
" )\n",
"\n",
" agent = Agent(\n",
" client=client,\n",
" agent_config=agent_config,\n",
" custom_tools=[weather_tool]\n",
" )\n",
" agent = Agent(client=client, agent_config=agent_config, custom_tools=[weather_tool])\n",
"\n",
" return agent\n",
"\n",
"\n",
"# Example usage\n",
"async def weather_example():\n",
" client = LlamaStackClient(base_url=LLAMA_STACK_API_TOGETHER_URL)\n",
@ -413,12 +409,14 @@
" async for log in EventLogger().log(response):\n",
" log.print()\n",
"\n",
"\n",
"# For Jupyter notebooks\n",
"import nest_asyncio\n",
"\n",
"nest_asyncio.apply()\n",
"\n",
"# Run the example\n",
"await weather_example()"
"await weather_example()\n"
]
},
{

3
llama_stack/cli/model/describe.py
View file

@ -13,7 +13,6 @@ from termcolor import colored
from llama_stack.cli.subcommand import Subcommand
from llama_stack.cli.table import print_table
from llama_stack.distribution.utils.serialize import EnumEncoder
class ModelDescribe(Subcommand):
@ -72,7 +71,7 @@ class ModelDescribe(Subcommand):
rows.append(
(
"Recommended sampling params",
json.dumps(sampling_params, cls=EnumEncoder, indent=4),
json.dumps(sampling_params, indent=4),
)
)

16
llama_stack/distribution/ui/page/evaluations/native_eval.py
View file

@ -58,11 +58,6 @@ def define_eval_candidate_2():
# Sampling Parameters
st.markdown("##### Sampling Parameters")
strategy = st.selectbox(
"Strategy",
["greedy", "top_p", "top_k"],
index=0,
)
temperature = st.slider(
"Temperature",
min_value=0.0,
@ -95,13 +90,20 @@ def define_eval_candidate_2():
help="Controls the likelihood for generating the same word or phrase multiple times in the same sentence or paragraph. 1 implies no penalty, 2 will strongly discourage model to repeat words or phrases.",
)
if candidate_type == "model":
if temperature > 0.0:
strategy = {
"type": "top_p",
"temperature": temperature,
"top_p": top_p,
}
else:
strategy = {"type": "greedy"}
eval_candidate = {
"type": "model",
"model": selected_model,
"sampling_params": {
"strategy": strategy,
"temperature": temperature,
"top_p": top_p,
"max_tokens": max_tokens,
"repetition_penalty": repetition_penalty,
},

12
llama_stack/distribution/ui/page/playground/chat.py
View file

@ -95,6 +95,15 @@ if prompt := st.chat_input("Example: What is Llama Stack?"):
message_placeholder = st.empty()
full_response = ""
if temperature > 0.0:
strategy = {
"type": "top_p",
"temperature": temperature,
"top_p": top_p,
}
else:
strategy = {"type": "greedy"}
response = llama_stack_api.client.inference.chat_completion(
messages=[
{"role": "system", "content": system_prompt},
@ -103,8 +112,7 @@ if prompt := st.chat_input("Example: What is Llama Stack?"):
model_id=selected_model,
stream=stream,
sampling_params={
"temperature": temperature,
"top_p": top_p,
"strategy": strategy,
"max_tokens": max_tokens,
"repetition_penalty": repetition_penalty,
},

13
llama_stack/distribution/ui/page/playground/rag.py
View file

@ -118,13 +118,20 @@ def rag_chat_page():
with st.chat_message(message["role"]):
st.markdown(message["content"])
if temperature > 0.0:
strategy = {
"type": "top_p",
"temperature": temperature,
"top_p": top_p,
}
else:
strategy = {"type": "greedy"}
agent_config = AgentConfig(
model=selected_model,
instructions=system_prompt,
sampling_params={
"strategy": "greedy",
"temperature": temperature,
"top_p": top_p,
"strategy": strategy,
},
tools=[
{

27
llama_stack/providers/inline/inference/meta_reference/generation.py
View file

@ -23,6 +23,11 @@ from fairscale.nn.model_parallel.initialize import (
initialize_model_parallel,
model_parallel_is_initialized,
)
from llama_models.datatypes import (
GreedySamplingStrategy,
SamplingParams,
TopPSamplingStrategy,
)
from llama_models.llama3.api.args import ModelArgs
from llama_models.llama3.api.chat_format import ChatFormat, LLMInput
from llama_models.llama3.api.datatypes import Model
@ -363,11 +368,12 @@ class Llama:
max_gen_len = self.model.params.max_seq_len - 1
model_input = self.formatter.encode_content(request.content)
temperature, top_p = _infer_sampling_params(sampling_params)
yield from self.generate(
model_input=model_input,
max_gen_len=max_gen_len,
temperature=sampling_params.temperature,
top_p=sampling_params.top_p,
temperature=temperature,
top_p=top_p,
logprobs=bool(request.logprobs),
include_stop_token=True,
logits_processor=get_logits_processor(
@ -390,14 +396,15 @@ class Llama:
):
max_gen_len = self.model.params.max_seq_len - 1
temperature, top_p = _infer_sampling_params(sampling_params)
yield from self.generate(
model_input=self.formatter.encode_dialog_prompt(
request.messages,
request.tool_prompt_format,
),
max_gen_len=max_gen_len,
temperature=sampling_params.temperature,
top_p=sampling_params.top_p,
temperature=temperature,
top_p=top_p,
logprobs=bool(request.logprobs),
include_stop_token=True,
logits_processor=get_logits_processor(
@ -492,3 +499,15 @@ def _build_regular_tokens_list(
is_word_start_token = len(decoded_after_0) > len(decoded_regular)
regular_tokens.append((token_idx, decoded_after_0, is_word_start_token))
return regular_tokens
def _infer_sampling_params(sampling_params: SamplingParams):
if isinstance(sampling_params.strategy, GreedySamplingStrategy):
temperature = 0.0
top_p = 1.0
elif isinstance(sampling_params.strategy, TopPSamplingStrategy):
temperature = sampling_params.strategy.temperature
top_p = sampling_params.strategy.top_p
else:
raise ValueError(f"Unsupported sampling strategy {sampling_params.strategy}")
return temperature, top_p

20
llama_stack/providers/inline/inference/vllm/vllm.py
View file

@ -36,6 +36,7 @@ from llama_stack.apis.inference import (
from llama_stack.apis.models import Model
from llama_stack.providers.datatypes import ModelsProtocolPrivate
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse,
process_chat_completion_response,
@ -126,21 +127,12 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
if sampling_params is None:
return VLLMSamplingParams(max_tokens=self.config.max_tokens)
# TODO convert what I saw in my first test ... but surely there's more to do here
kwargs = {
"temperature": sampling_params.temperature,
"max_tokens": self.config.max_tokens,
}
if sampling_params.top_k:
kwargs["top_k"] = sampling_params.top_k
if sampling_params.top_p:
kwargs["top_p"] = sampling_params.top_p
if sampling_params.max_tokens:
kwargs["max_tokens"] = sampling_params.max_tokens
if sampling_params.repetition_penalty > 0:
kwargs["repetition_penalty"] = sampling_params.repetition_penalty
options = get_sampling_options(sampling_params)
if "repeat_penalty" in options:
options["repetition_penalty"] = options["repeat_penalty"]
del options["repeat_penalty"]
return VLLMSamplingParams(**kwargs)
return VLLMSamplingParams(**options)
async def unregister_model(self, model_id: str) -> None:
pass

18
llama_stack/providers/remote/inference/bedrock/bedrock.py
View file

@ -34,6 +34,7 @@ from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_strategy_options,
OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse,
process_chat_completion_response,
@ -166,16 +167,13 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
) -> Dict:
bedrock_model = request.model
inference_config = {}
param_mapping = {
"max_tokens": "max_gen_len",
"temperature": "temperature",
"top_p": "top_p",
}
sampling_params = request.sampling_params
options = get_sampling_strategy_options(sampling_params)
for k, v in param_mapping.items():
if getattr(request.sampling_params, k):
inference_config[v] = getattr(request.sampling_params, k)
if sampling_params.max_tokens:
options["max_gen_len"] = sampling_params.max_tokens
if sampling_params.repetition_penalty > 0:
options["repetition_penalty"] = sampling_params.repetition_penalty
prompt = await chat_completion_request_to_prompt(
request, self.get_llama_model(request.model), self.formatter
@ -185,7 +183,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
"body": json.dumps(
{
"prompt": prompt,
**inference_config,
**options,
}
),
}

5
llama_stack/providers/remote/inference/cerebras/cerebras.py
View file

@ -9,6 +9,7 @@ from typing import AsyncGenerator, List, Optional, Union
from cerebras.cloud.sdk import AsyncCerebras
from llama_models.datatypes import CoreModelId
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import TopKSamplingStrategy
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_stack.apis.common.content_types import InterleavedContent
@ -172,7 +173,9 @@ class CerebrasInferenceAdapter(ModelRegistryHelper, Inference):
async def _get_params(
self, request: Union[ChatCompletionRequest, CompletionRequest]
) -> dict:
if request.sampling_params and request.sampling_params.top_k:
if request.sampling_params and isinstance(
request.sampling_params.strategy, TopKSamplingStrategy
):
raise ValueError("`top_k` not supported by Cerebras")
prompt = ""

8
llama_stack/providers/remote/inference/groq/groq_utils.py
View file

@ -48,6 +48,9 @@ from llama_stack.apis.inference import (
ToolDefinition,
ToolPromptFormat,
)
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_strategy_options,
)
def convert_chat_completion_request(
@ -77,6 +80,7 @@ def convert_chat_completion_request(
if request.tool_prompt_format != ToolPromptFormat.json:
warnings.warn("tool_prompt_format is not used by Groq. Ignoring.")
sampling_options = get_sampling_strategy_options(request.sampling_params)
return CompletionCreateParams(
model=request.model,
messages=[_convert_message(message) for message in request.messages],
@ -84,8 +88,8 @@ def convert_chat_completion_request(
frequency_penalty=None,
stream=request.stream,
max_tokens=request.sampling_params.max_tokens or None,
temperature=request.sampling_params.temperature,
top_p=request.sampling_params.top_p,
temperature=sampling_options.get("temperature", 1.0),
top_p=sampling_options.get("top_p", 1.0),
tools=[_convert_groq_tool_definition(tool) for tool in request.tools or []],
tool_choice=request.tool_choice.value if request.tool_choice else None,
)

19
llama_stack/providers/remote/inference/nvidia/openai_utils.py
View file

@ -263,19 +263,18 @@ def convert_chat_completion_request(
if request.sampling_params.max_tokens:
payload.update(max_tokens=request.sampling_params.max_tokens)
if request.sampling_params.strategy == "top_p":
strategy = request.sampling_params.strategy
if isinstance(strategy, TopPSamplingStrategy):
nvext.update(top_k=-1)
payload.update(top_p=request.sampling_params.top_p)
elif request.sampling_params.strategy == "top_k":
if (
request.sampling_params.top_k != -1
and request.sampling_params.top_k < 1
):
payload.update(top_p=strategy.top_p)
payload.update(temperature=strategy.temperature)
elif isinstance(strategy, TopKSamplingStrategy):
if strategy.top_k != -1 and strategy.top_k < 1:
warnings.warn("top_k must be -1 or >= 1")
nvext.update(top_k=request.sampling_params.top_k)
elif request.sampling_params.strategy == "greedy":
nvext.update(top_k=strategy.top_k)
elif strategy.strategy == "greedy":
nvext.update(top_k=-1)
payload.update(temperature=request.sampling_params.temperature)
payload.update(temperature=strategy.temperature)
return payload

11
llama_stack/providers/tests/agents/test_agents.py
View file

@ -22,7 +22,12 @@ from llama_stack.apis.agents import (
ToolExecutionStep,
Turn,
)
from llama_stack.apis.inference import CompletionMessage, SamplingParams, UserMessage
from llama_stack.apis.inference import (
CompletionMessage,
SamplingParams,
TopPSamplingStrategy,
UserMessage,
)
from llama_stack.apis.safety import ViolationLevel
from llama_stack.providers.datatypes import Api
@ -42,7 +47,9 @@ def common_params(inference_model):
model=inference_model,
instructions="You are a helpful assistant.",
enable_session_persistence=True,
sampling_params=SamplingParams(temperature=0.7, top_p=0.95),
sampling_params=SamplingParams(
strategy=TopPSamplingStrategy(temperature=0.7, top_p=0.95)
),
input_shields=[],
output_shields=[],
toolgroups=[],

20
llama_stack/providers/tests/inference/groq/test_groq_utils.py
View file

@ -21,6 +21,7 @@ from groq.types.chat.chat_completion_message_tool_call import (
Function,
)
from groq.types.shared.function_definition import FunctionDefinition
from llama_models.datatypes import GreedySamplingStrategy, TopPSamplingStrategy
from llama_models.llama3.api.datatypes import ToolParamDefinition
from llama_stack.apis.inference import (
ChatCompletionRequest,
@ -152,21 +153,30 @@ class TestConvertChatCompletionRequest:
assert converted["max_tokens"] == 100
def test_includes_temperature(self):
def _dummy_chat_completion_request(self):
return ChatCompletionRequest(
model="Llama-3.2-3B",
messages=[UserMessage(content="Hello World")],
)
def test_includes_stratgy(self):
request = self._dummy_chat_completion_request()
request.sampling_params.temperature = 0.5
request.sampling_params.strategy = TopPSamplingStrategy(
temperature=0.5, top_p=0.95
)
converted = convert_chat_completion_request(request)
assert converted["temperature"] == 0.5
assert converted["top_p"] == 0.95
def test_includes_top_p(self):
def test_includes_greedy_strategy(self):
request = self._dummy_chat_completion_request()
request.sampling_params.top_p = 0.95
request.sampling_params.strategy = GreedySamplingStrategy()
converted = convert_chat_completion_request(request)
assert converted["top_p"] == 0.95
assert converted["temperature"] == 0.0
def test_includes_tool_choice(self):
request = self._dummy_chat_completion_request()

28
llama_stack/providers/utils/inference/openai_compat.py
View file

@ -8,7 +8,13 @@ from typing import AsyncGenerator, List, Optional
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import SamplingParams, StopReason
from llama_models.llama3.api.datatypes import (
GreedySamplingStrategy,
SamplingParams,
StopReason,
TopKSamplingStrategy,
TopPSamplingStrategy,
)
from pydantic import BaseModel
from llama_stack.apis.common.content_types import (
@ -49,12 +55,26 @@ class OpenAICompatCompletionResponse(BaseModel):
choices: List[OpenAICompatCompletionChoice]
def get_sampling_strategy_options(params: SamplingParams) -> dict:
options = {}
if isinstance(params.strategy, GreedySamplingStrategy):
options["temperature"] = 0.0
elif isinstance(params.strategy, TopPSamplingStrategy):
options["temperature"] = params.strategy.temperature
options["top_p"] = params.strategy.top_p
elif isinstance(params.strategy, TopKSamplingStrategy):
options["top_k"] = params.strategy.top_k
else:
raise ValueError(f"Unsupported sampling strategy: {params.strategy}")
return options
def get_sampling_options(params: SamplingParams) -> dict:
options = {}
if params:
for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
if getattr(params, attr):
options[attr] = getattr(params, attr)
options.update(get_sampling_strategy_options(params))
options["max_tokens"] = params.max_tokens
if params.repetition_penalty is not None and params.repetition_penalty != 1.0:
options["repeat_penalty"] = params.repetition_penalty

8
tests/client-sdk/agents/test_agents.py
View file

@ -97,9 +97,11 @@ def agent_config(llama_stack_client):
model=model_id,
instructions="You are a helpful assistant",
sampling_params={
"strategy": "greedy",
"temperature": 1.0,
"top_p": 0.9,
"strategy": {
"type": "greedy",
"temperature": 1.0,
"top_p": 0.9,
},
},
toolgroups=[],
tool_choice="auto",