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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": [ "source": [
"import os\n", "import os\n",
"\n",
"from google.colab import userdata\n", "from google.colab import userdata\n",
"\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", "\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n", "from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
"\n",
"client = LlamaStackAsLibraryClient(\"together\")\n", "client = LlamaStackAsLibraryClient(\"together\")\n",
"_ = client.initialize()" "_ = client.initialize()\n"
] ]
}, },
{ {
@ -769,6 +771,7 @@
], ],
"source": [ "source": [
"from rich.pretty import pprint\n", "from rich.pretty import pprint\n",
"\n",
"print(\"Available models:\")\n", "print(\"Available models:\")\n",
"for m in client.models.list():\n", "for m in client.models.list():\n",
" print(f\"{m.identifier} (provider's alias: {m.provider_resource_id}) \")\n", " print(f\"{m.identifier} (provider's alias: {m.provider_resource_id}) \")\n",
@ -777,7 +780,7 @@
"print(\"Available shields (safety models):\")\n", "print(\"Available shields (safety models):\")\n",
"for s in client.shields.list():\n", "for s in client.shields.list():\n",
" print(s.identifier)\n", " print(s.identifier)\n",
"print(\"----\")" "print(\"----\")\n"
] ]
}, },
{ {
@ -822,7 +825,7 @@
"source": [ "source": [
"model_id = \"meta-llama/Llama-3.1-70B-Instruct\"\n", "model_id = \"meta-llama/Llama-3.1-70B-Instruct\"\n",
"\n", "\n",
"model_id" "model_id\n"
] ]
}, },
{ {
@ -863,11 +866,11 @@
" model_id=model_id,\n", " model_id=model_id,\n",
" messages=[\n", " messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\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", ")\n",
"\n", "\n",
"print(response.completion_message.content)" "print(response.completion_message.content)\n"
] ]
}, },
{ {
@ -900,12 +903,13 @@
"source": [ "source": [
"from termcolor import cprint\n", "from termcolor import cprint\n",
"\n", "\n",
"\n",
"def chat_loop():\n", "def chat_loop():\n",
" conversation_history = []\n", " conversation_history = []\n",
" while True:\n", " while True:\n",
" user_input = input('User> ')\n", " user_input = input(\"User> \")\n",
" if user_input.lower() in ['exit', 'quit', 'bye']:\n", " if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n",
" cprint('Ending conversation. Goodbye!', 'yellow')\n", " cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n",
" break\n", " break\n",
"\n", "\n",
" user_message = {\"role\": \"user\", \"content\": user_input}\n", " user_message = {\"role\": \"user\", \"content\": user_input}\n",
@ -915,14 +919,15 @@
" messages=conversation_history,\n", " messages=conversation_history,\n",
" model_id=model_id,\n", " model_id=model_id,\n",
" )\n", " )\n",
" cprint(f'> Response: {response.completion_message.content}', 'cyan')\n", " cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n",
"\n", "\n",
" assistant_message = {\n", " assistant_message = {\n",
" \"role\": \"assistant\", # was user\n", " \"role\": \"assistant\", # was user\n",
" \"content\": response.completion_message.content,\n", " \"content\": response.completion_message.content,\n",
" }\n", " }\n",
" conversation_history.append(assistant_message)\n", " conversation_history.append(assistant_message)\n",
"\n", "\n",
"\n",
"chat_loop()\n" "chat_loop()\n"
] ]
}, },
@ -978,21 +983,18 @@
"source": [ "source": [
"from llama_stack_client.lib.inference.event_logger import EventLogger\n", "from llama_stack_client.lib.inference.event_logger import EventLogger\n",
"\n", "\n",
"message = {\n", "message = {\"role\": \"user\", \"content\": \"Write me a sonnet about llama\"}\n",
" \"role\": \"user\",\n", "print(f'User> {message[\"content\"]}', \"green\")\n",
" \"content\": 'Write me a sonnet about llama'\n",
"}\n",
"print(f'User> {message[\"content\"]}', 'green')\n",
"\n", "\n",
"response = client.inference.chat_completion(\n", "response = client.inference.chat_completion(\n",
" messages=[message],\n", " messages=[message],\n",
" model_id=model_id,\n", " model_id=model_id,\n",
" stream=True, # <-----------\n", " stream=True, # <-----------\n",
")\n", ")\n",
"\n", "\n",
"# Print the tokens while they are received\n", "# Print the tokens while they are received\n",
"for log in EventLogger().log(response):\n", "for log in EventLogger().log(response):\n",
" log.print()" " log.print()\n"
] ]
}, },
{ {
@ -1045,26 +1047,26 @@
"source": [ "source": [
"from pydantic import BaseModel\n", "from pydantic import BaseModel\n",
"\n", "\n",
"\n",
"class Output(BaseModel):\n", "class Output(BaseModel):\n",
" name: str\n", " name: str\n",
" year_born: str\n", " year_born: str\n",
" year_retired: str\n", " year_retired: str\n",
"\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", "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", "response = client.inference.completion(\n",
" model_id=model_id,\n", " model_id=model_id,\n",
" content=user_input,\n", " content=user_input,\n",
" stream=False,\n", " stream=False,\n",
" sampling_params={\n", " sampling_params={\"strategy\": {\"type\": \"greedy\"}, \"max_tokens\": 50},\n",
" \"max_tokens\": 50,\n",
" },\n",
" response_format={\n", " response_format={\n",
" \"type\": \"json_schema\",\n", " \"type\": \"json_schema\",\n",
" \"json_schema\": Output.model_json_schema(),\n", " \"json_schema\": Output.model_json_schema(),\n",
" },\n", " },\n",
")\n", ")\n",
"\n", "\n",
"pprint(response)" "pprint(response)\n"
] ]
}, },
{ {
@ -1220,7 +1222,7 @@
" shield_id=available_shields[0],\n", " shield_id=available_shields[0],\n",
" params={},\n", " params={},\n",
" )\n", " )\n",
" pprint(response)" " pprint(response)\n"
] ]
}, },
{ {
@ -1489,8 +1491,8 @@
"source": [ "source": [
"from llama_stack_client.lib.agents.agent import Agent\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.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 import Attachment\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from termcolor import cprint\n", "from termcolor import cprint\n",
"\n", "\n",
"urls = [\"chat.rst\", \"llama3.rst\", \"datasets.rst\", \"lora_finetune.rst\"]\n", "urls = [\"chat.rst\", \"llama3.rst\", \"datasets.rst\", \"lora_finetune.rst\"]\n",
@ -1522,14 +1524,14 @@
" ),\n", " ),\n",
"]\n", "]\n",
"for prompt, attachments in user_prompts:\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", " response = rag_agent.create_turn(\n",
" messages=[{\"role\": \"user\", \"content\": prompt}],\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n",
" attachments=attachments,\n", " attachments=attachments,\n",
" session_id=session_id,\n", " session_id=session_id,\n",
" )\n", " )\n",
" for log in EventLogger().log(response):\n", " for log in EventLogger().log(response):\n",
" log.print()" " log.print()\n"
] ]
}, },
{ {
@ -1560,8 +1562,8 @@
"search_tool = {\n", "search_tool = {\n",
" \"type\": \"brave_search\",\n", " \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\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", "\n",
"session_id = agent.create_session(\"test-session\")\n", "session_id = agent.create_session(\"test-session\")\n",
"for prompt in user_prompts:\n", "for prompt in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n", " cprint(f\"User> {prompt}\", \"green\")\n",
" response = agent.create_turn(\n", " response = agent.create_turn(\n",
" messages=[\n", " messages=[\n",
" {\n", " {\n",
@ -1758,7 +1760,7 @@
" search_tool,\n", " search_tool,\n",
" {\n", " {\n",
" \"type\": \"code_interpreter\",\n", " \"type\": \"code_interpreter\",\n",
" }\n", " },\n",
" ],\n", " ],\n",
" tool_choice=\"required\",\n", " tool_choice=\"required\",\n",
" input_shields=[],\n", " input_shields=[],\n",
@ -1788,7 +1790,7 @@
"]\n", "]\n",
"\n", "\n",
"for prompt in user_prompts:\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", " response = codex_agent.create_turn(\n",
" messages=[\n", " messages=[\n",
" {\n", " {\n",
@ -1841,27 +1843,57 @@
} }
], ],
"source": [ "source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n", "\n",
"# Read the CSV file\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", "\n",
"# Extract the year and inflation rate from the CSV file\n", "# Extract the year and inflation rate from the CSV file\n",
"df['Year'] = pd.to_datetime(df['Year'], format='%Y')\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 = 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", "\n",
"# Calculate the average yearly inflation rate\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", "\n",
"# Plot the average yearly inflation rate as a time series\n", "# Plot the average yearly inflation rate as a time series\n",
"plt.figure(figsize=(10, 6))\n", "plt.figure(figsize=(10, 6))\n",
"plt.plot(df['Year'], df['Yearly Inflation'], marker='o')\n", "plt.plot(df[\"Year\"], df[\"Yearly Inflation\"], marker=\"o\")\n",
"plt.title('Average Yearly Inflation Rate')\n", "plt.title(\"Average Yearly Inflation Rate\")\n",
"plt.xlabel('Year')\n", "plt.xlabel(\"Year\")\n",
"plt.ylabel('Inflation Rate (%)')\n", "plt.ylabel(\"Inflation Rate (%)\")\n",
"plt.grid(True)\n", "plt.grid(True)\n",
"plt.show()" "plt.show()\n"
] ]
}, },
{ {
@ -2035,6 +2067,8 @@
"source": [ "source": [
"# disable logging for clean server logs\n", "# disable logging for clean server logs\n",
"import logging\n", "import logging\n",
"\n",
"\n",
"def remove_root_handlers():\n", "def remove_root_handlers():\n",
" root_logger = logging.getLogger()\n", " root_logger = logging.getLogger()\n",
" for handler in root_logger.handlers[:]:\n", " for handler in root_logger.handlers[:]:\n",
@ -2042,7 +2076,7 @@
" print(f\"Removed handler {handler.__class__.__name__} from root logger\")\n", " print(f\"Removed handler {handler.__class__.__name__} from root logger\")\n",
"\n", "\n",
"\n", "\n",
"remove_root_handlers()" "remove_root_handlers()\n"
] ]
}, },
{ {
@ -2083,10 +2117,10 @@
} }
], ],
"source": [ "source": [
"from google.colab import userdata\n",
"from llama_stack_client.lib.agents.agent import Agent\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.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n", "from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from google.colab import userdata\n",
"\n", "\n",
"agent_config = AgentConfig(\n", "agent_config = AgentConfig(\n",
" model=\"meta-llama/Llama-3.1-405B-Instruct\",\n", " model=\"meta-llama/Llama-3.1-405B-Instruct\",\n",
@ -2096,7 +2130,7 @@
" {\n", " {\n",
" \"type\": \"brave_search\",\n", " \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\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", " ]\n",
" ),\n", " ),\n",
@ -2125,7 +2159,7 @@
" )\n", " )\n",
"\n", "\n",
" for log in EventLogger().log(response):\n", " for log in EventLogger().log(response):\n",
" log.print()" " log.print()\n"
] ]
}, },
{ {
@ -2265,20 +2299,21 @@
"source": [ "source": [
"print(f\"Getting traces for session_id={session_id}\")\n", "print(f\"Getting traces for session_id={session_id}\")\n",
"import json\n", "import json\n",
"\n",
"from rich.pretty import pprint\n", "from rich.pretty import pprint\n",
"\n", "\n",
"agent_logs = []\n", "agent_logs = []\n",
"\n", "\n",
"for span in client.telemetry.query_spans(\n", "for span in client.telemetry.query_spans(\n",
" attribute_filters=[\n", " attribute_filters=[\n",
" {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n", " {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n",
" ],\n", " ],\n",
" attributes_to_return=[\"input\", \"output\"]\n", " attributes_to_return=[\"input\", \"output\"],\n",
" ):\n", "):\n",
" if span.attributes[\"output\"] != \"no shields\":\n", " if span.attributes[\"output\"] != \"no shields\":\n",
" agent_logs.append(span.attributes)\n", " agent_logs.append(span.attributes)\n",
"\n", "\n",
"pprint(agent_logs)" "pprint(agent_logs)\n"
] ]
}, },
{ {
@ -2389,23 +2424,25 @@
"eval_rows = []\n", "eval_rows = []\n",
"\n", "\n",
"for log in agent_logs:\n", "for log in agent_logs:\n",
" last_msg = log['input'][-1]\n", " last_msg = log[\"input\"][-1]\n",
" if \"\\\"role\\\":\\\"user\\\"\" in last_msg:\n", " if '\"role\":\"user\"' in last_msg:\n",
" eval_rows.append(\n", " eval_rows.append(\n",
" {\n", " {\n",
" \"input_query\": last_msg,\n", " \"input_query\": last_msg,\n",
" \"generated_answer\": log[\"output\"],\n", " \"generated_answer\": log[\"output\"],\n",
" # check if generated_answer uses tools brave_search\n", " # check if generated_answer uses tools brave_search\n",
" \"expected_answer\": \"brave_search\",\n", " \"expected_answer\": \"brave_search\",\n",
" },\n", " },\n",
" )\n", " )\n",
"\n", "\n",
"pprint(eval_rows)\n", "pprint(eval_rows)\n",
"scoring_params = {\n", "scoring_params = {\n",
" \"basic::subset_of\": None,\n", " \"basic::subset_of\": None,\n",
"}\n", "}\n",
"scoring_response = client.scoring.score(input_rows=eval_rows, scoring_functions=scoring_params)\n", "scoring_response = client.scoring.score(\n",
"pprint(scoring_response)" " input_rows=eval_rows, scoring_functions=scoring_params\n",
")\n",
"pprint(scoring_response)\n"
] ]
}, },
{ {
@ -2506,7 +2543,9 @@
"EXPECTED_RESPONSE: {expected_answer}\n", "EXPECTED_RESPONSE: {expected_answer}\n",
"\"\"\"\n", "\"\"\"\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", "generated_answer = \"\"\"\n",
"Here are the top 5 topics that were explained in the documentation for Torchtune:\n", "Here are the top 5 topics that were explained in the documentation for Torchtune:\n",
"\n", "\n",
@ -2537,7 +2576,7 @@
"}\n", "}\n",
"\n", "\n",
"response = client.scoring.score(input_rows=rows, scoring_functions=scoring_params)\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": [ "source": [
"import os\n", "import os\n",
"\n",
"from google.colab import userdata\n", "from google.colab import userdata\n",
"\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", "\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n", "from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
"\n",
"client = LlamaStackAsLibraryClient(\"together\")\n", "client = LlamaStackAsLibraryClient(\"together\")\n",
"_ = client.initialize()\n", "_ = client.initialize()\n",
"\n", "\n",
@ -631,7 +633,7 @@
" model_id=\"meta-llama/Llama-3.1-405B-Instruct\",\n", " model_id=\"meta-llama/Llama-3.1-405B-Instruct\",\n",
" provider_model_id=\"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\",\n", " provider_model_id=\"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\",\n",
" provider_id=\"together\",\n", " provider_id=\"together\",\n",
")" ")\n"
] ]
}, },
{ {
@ -668,7 +670,7 @@
"source": [ "source": [
"name = \"llamastack/mmmu\"\n", "name = \"llamastack/mmmu\"\n",
"subset = \"Agriculture\"\n", "subset = \"Agriculture\"\n",
"split = \"dev\"" "split = \"dev\"\n"
] ]
}, },
{ {
@ -914,9 +916,10 @@
], ],
"source": [ "source": [
"import datasets\n", "import datasets\n",
"\n",
"ds = datasets.load_dataset(path=name, name=subset, split=split)\n", "ds = datasets.load_dataset(path=name, name=subset, split=split)\n",
"ds = ds.select_columns([\"chat_completion_input\", \"input_query\", \"expected_answer\"])\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": [ "source": [
"from tqdm import tqdm\n",
"from rich.pretty import pprint\n", "from rich.pretty import pprint\n",
"from tqdm import tqdm\n",
"\n", "\n",
"SYSTEM_PROMPT_TEMPLATE = \"\"\"\n", "SYSTEM_PROMPT_TEMPLATE = \"\"\"\n",
"You are an expert in {subject} whose job is to answer questions from the user using images.\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", "client.eval_tasks.register(\n",
" eval_task_id=\"meta-reference::mmmu\",\n", " eval_task_id=\"meta-reference::mmmu\",\n",
" dataset_id=f\"mmmu-{subset}-{split}\",\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",
"\n", "\n",
"response = client.eval.evaluate_rows(\n", "response = client.eval.evaluate_rows(\n",
@ -1052,16 +1055,17 @@
" \"type\": \"model\",\n", " \"type\": \"model\",\n",
" \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n", " \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n",
" \"sampling_params\": {\n", " \"sampling_params\": {\n",
" \"temperature\": 0.0,\n", " \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" \"max_tokens\": 4096,\n", " \"max_tokens\": 4096,\n",
" \"top_p\": 0.9,\n",
" \"repeat_penalty\": 1.0,\n", " \"repeat_penalty\": 1.0,\n",
" },\n", " },\n",
" \"system_message\": system_message\n", " \"system_message\": system_message,\n",
" }\n", " },\n",
" }\n", " },\n",
")\n", ")\n",
"pprint(response)" "pprint(response)\n"
] ]
}, },
{ {
@ -1098,8 +1102,8 @@
" \"input_query\": {\"type\": \"string\"},\n", " \"input_query\": {\"type\": \"string\"},\n",
" \"expected_answer\": {\"type\": \"string\"},\n", " \"expected_answer\": {\"type\": \"string\"},\n",
" \"chat_completion_input\": {\"type\": \"chat_completion_input\"},\n", " \"chat_completion_input\": {\"type\": \"chat_completion_input\"},\n",
" }\n", " },\n",
")" ")\n"
] ]
}, },
{ {
@ -1113,7 +1117,7 @@
"eval_rows = client.datasetio.get_rows_paginated(\n", "eval_rows = client.datasetio.get_rows_paginated(\n",
" dataset_id=simpleqa_dataset_id,\n", " dataset_id=simpleqa_dataset_id,\n",
" rows_in_page=5,\n", " rows_in_page=5,\n",
")" ")\n"
] ]
}, },
{ {
@ -1209,7 +1213,7 @@
"client.eval_tasks.register(\n", "client.eval_tasks.register(\n",
" eval_task_id=\"meta-reference::simpleqa\",\n", " eval_task_id=\"meta-reference::simpleqa\",\n",
" dataset_id=simpleqa_dataset_id,\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",
"\n", "\n",
"response = client.eval.evaluate_rows(\n", "response = client.eval.evaluate_rows(\n",
@ -1222,15 +1226,16 @@
" \"type\": \"model\",\n", " \"type\": \"model\",\n",
" \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n", " \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n",
" \"sampling_params\": {\n", " \"sampling_params\": {\n",
" \"temperature\": 0.0,\n", " \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" \"max_tokens\": 4096,\n", " \"max_tokens\": 4096,\n",
" \"top_p\": 0.9,\n",
" \"repeat_penalty\": 1.0,\n", " \"repeat_penalty\": 1.0,\n",
" },\n", " },\n",
" }\n", " },\n",
" }\n", " },\n",
")\n", ")\n",
"pprint(response)" "pprint(response)\n"
] ]
}, },
{ {
@ -1347,23 +1352,19 @@
"agent_config = {\n", "agent_config = {\n",
" \"model\": \"meta-llama/Llama-3.1-405B-Instruct\",\n", " \"model\": \"meta-llama/Llama-3.1-405B-Instruct\",\n",
" \"instructions\": \"You are a helpful assistant\",\n", " \"instructions\": \"You are a helpful assistant\",\n",
" \"sampling_params\": {\n", " \"sampling_params\": {\"strategy\": {\"type\": \"greedy\"}},\n",
" \"strategy\": \"greedy\",\n",
" \"temperature\": 0.0,\n",
" \"top_p\": 0.95,\n",
" },\n",
" \"tools\": [\n", " \"tools\": [\n",
" {\n", " {\n",
" \"type\": \"brave_search\",\n", " \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\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", " ],\n",
" \"tool_choice\": \"auto\",\n", " \"tool_choice\": \"auto\",\n",
" \"tool_prompt_format\": \"json\",\n", " \"tool_prompt_format\": \"json\",\n",
" \"input_shields\": [],\n", " \"input_shields\": [],\n",
" \"output_shields\": [],\n", " \"output_shields\": [],\n",
" \"enable_session_persistence\": False\n", " \"enable_session_persistence\": False,\n",
"}\n", "}\n",
"\n", "\n",
"response = client.eval.evaluate_rows(\n", "response = client.eval.evaluate_rows(\n",
@ -1375,10 +1376,10 @@
" \"eval_candidate\": {\n", " \"eval_candidate\": {\n",
" \"type\": \"agent\",\n", " \"type\": \"agent\",\n",
" \"config\": agent_config,\n", " \"config\": agent_config,\n",
" }\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": [ "source": [
"from rich.pretty import pprint\n", "from rich.pretty import pprint\n",
"\n",
"print(\"Available models:\")\n", "print(\"Available models:\")\n",
"for m in client.models.list():\n", "for m in client.models.list():\n",
" print(f\"{m.identifier} (provider's alias: {m.provider_resource_id}) \")\n", " print(f\"{m.identifier} (provider's alias: {m.provider_resource_id}) \")\n",
@ -1344,7 +1345,7 @@
"print(\"Available shields (safety models):\")\n", "print(\"Available shields (safety models):\")\n",
"for s in client.shields.list():\n", "for s in client.shields.list():\n",
" print(s.identifier)\n", " print(s.identifier)\n",
"print(\"----\")" "print(\"----\")\n"
] ]
}, },
{ {
@ -1389,7 +1390,7 @@
"source": [ "source": [
"model_id = \"meta-llama/Llama-3.1-70B-Instruct\"\n", "model_id = \"meta-llama/Llama-3.1-70B-Instruct\"\n",
"\n", "\n",
"model_id" "model_id\n"
] ]
}, },
{ {
@ -1432,11 +1433,11 @@
" model_id=model_id,\n", " model_id=model_id,\n",
" messages=[\n", " messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\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", ")\n",
"\n", "\n",
"print(response.completion_message.content)" "print(response.completion_message.content)\n"
] ]
}, },
{ {
@ -1489,12 +1490,13 @@
"source": [ "source": [
"from termcolor import cprint\n", "from termcolor import cprint\n",
"\n", "\n",
"\n",
"def chat_loop():\n", "def chat_loop():\n",
" conversation_history = []\n", " conversation_history = []\n",
" while True:\n", " while True:\n",
" user_input = input('User> ')\n", " user_input = input(\"User> \")\n",
" if user_input.lower() in ['exit', 'quit', 'bye']:\n", " if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n",
" cprint('Ending conversation. Goodbye!', 'yellow')\n", " cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n",
" break\n", " break\n",
"\n", "\n",
" user_message = {\"role\": \"user\", \"content\": user_input}\n", " user_message = {\"role\": \"user\", \"content\": user_input}\n",
@ -1504,15 +1506,16 @@
" messages=conversation_history,\n", " messages=conversation_history,\n",
" model_id=model_id,\n", " model_id=model_id,\n",
" )\n", " )\n",
" cprint(f'> Response: {response.completion_message.content}', 'cyan')\n", " cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n",
"\n", "\n",
" assistant_message = {\n", " assistant_message = {\n",
" \"role\": \"assistant\", # was user\n", " \"role\": \"assistant\", # was user\n",
" \"content\": response.completion_message.content,\n", " \"content\": response.completion_message.content,\n",
" \"stop_reason\": response.completion_message.stop_reason,\n", " \"stop_reason\": response.completion_message.stop_reason,\n",
" }\n", " }\n",
" conversation_history.append(assistant_message)\n", " conversation_history.append(assistant_message)\n",
"\n", "\n",
"\n",
"chat_loop()\n" "chat_loop()\n"
] ]
}, },
@ -1568,21 +1571,18 @@
"source": [ "source": [
"from llama_stack_client.lib.inference.event_logger import EventLogger\n", "from llama_stack_client.lib.inference.event_logger import EventLogger\n",
"\n", "\n",
"message = {\n", "message = {\"role\": \"user\", \"content\": \"Write me a sonnet about llama\"}\n",
" \"role\": \"user\",\n", "print(f'User> {message[\"content\"]}', \"green\")\n",
" \"content\": 'Write me a sonnet about llama'\n",
"}\n",
"print(f'User> {message[\"content\"]}', 'green')\n",
"\n", "\n",
"response = client.inference.chat_completion(\n", "response = client.inference.chat_completion(\n",
" messages=[message],\n", " messages=[message],\n",
" model_id=model_id,\n", " model_id=model_id,\n",
" stream=True, # <-----------\n", " stream=True, # <-----------\n",
")\n", ")\n",
"\n", "\n",
"# Print the tokens while they are received\n", "# Print the tokens while they are received\n",
"for log in EventLogger().log(response):\n", "for log in EventLogger().log(response):\n",
" log.print()" " log.print()\n"
] ]
}, },
{ {
@ -1648,17 +1648,22 @@
"source": [ "source": [
"from pydantic import BaseModel\n", "from pydantic import BaseModel\n",
"\n", "\n",
"\n",
"class Output(BaseModel):\n", "class Output(BaseModel):\n",
" name: str\n", " name: str\n",
" year_born: str\n", " year_born: str\n",
" year_retired: str\n", " year_retired: str\n",
"\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", "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", "response = client.inference.completion(\n",
" model_id=model_id,\n", " model_id=model_id,\n",
" content=user_input,\n", " content=user_input,\n",
" stream=False,\n", " stream=False,\n",
" sampling_params={\n", " sampling_params={\n",
" \"strategy\": {\n",
" \"type\": \"greedy\",\n",
" },\n",
" \"max_tokens\": 50,\n", " \"max_tokens\": 50,\n",
" },\n", " },\n",
" response_format={\n", " response_format={\n",
@ -1667,7 +1672,7 @@
" },\n", " },\n",
")\n", ")\n",
"\n", "\n",
"pprint(response)" "pprint(response)\n"
] ]
}, },
{ {
@ -1823,7 +1828,7 @@
" shield_id=available_shields[0],\n", " shield_id=available_shields[0],\n",
" params={},\n", " params={},\n",
" )\n", " )\n",
" pprint(response)" " pprint(response)\n"
] ]
}, },
{ {
@ -2025,7 +2030,7 @@
"\n", "\n",
"session_id = agent.create_session(\"test-session\")\n", "session_id = agent.create_session(\"test-session\")\n",
"for prompt in user_prompts:\n", "for prompt in user_prompts:\n",
" cprint(f'User> {prompt}', 'green')\n", " cprint(f\"User> {prompt}\", \"green\")\n",
" response = agent.create_turn(\n", " response = agent.create_turn(\n",
" messages=[\n", " messages=[\n",
" {\n", " {\n",
@ -2451,8 +2456,8 @@
} }
], ],
"source": [ "source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n", "\n",
"# Load data\n", "# Load data\n",
"df = pd.read_csv(\"/tmp/tmpvzjigv7g/n2OzlTWhinflation.csv\")\n", "df = pd.read_csv(\"/tmp/tmpvzjigv7g/n2OzlTWhinflation.csv\")\n",
@ -2536,10 +2541,10 @@
} }
], ],
"source": [ "source": [
"from google.colab import userdata\n",
"from llama_stack_client.lib.agents.agent import Agent\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.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n", "from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from google.colab import userdata\n",
"\n", "\n",
"agent_config = AgentConfig(\n", "agent_config = AgentConfig(\n",
" model=\"meta-llama/Llama-3.1-405B-Instruct-FP8\",\n", " model=\"meta-llama/Llama-3.1-405B-Instruct-FP8\",\n",
@ -2570,7 +2575,7 @@
" )\n", " )\n",
"\n", "\n",
" for log in EventLogger().log(response):\n", " for log in EventLogger().log(response):\n",
" log.print()" " log.print()\n"
] ]
}, },
{ {
@ -2790,20 +2795,21 @@
"source": [ "source": [
"print(f\"Getting traces for session_id={session_id}\")\n", "print(f\"Getting traces for session_id={session_id}\")\n",
"import json\n", "import json\n",
"\n",
"from rich.pretty import pprint\n", "from rich.pretty import pprint\n",
"\n", "\n",
"agent_logs = []\n", "agent_logs = []\n",
"\n", "\n",
"for span in client.telemetry.query_spans(\n", "for span in client.telemetry.query_spans(\n",
" attribute_filters=[\n", " attribute_filters=[\n",
" {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n", " {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n",
" ],\n", " ],\n",
" attributes_to_return=[\"input\", \"output\"]\n", " attributes_to_return=[\"input\", \"output\"],\n",
" ):\n", "):\n",
" if span.attributes[\"output\"] != \"no shields\":\n", " if span.attributes[\"output\"] != \"no shields\":\n",
" agent_logs.append(span.attributes)\n", " agent_logs.append(span.attributes)\n",
"\n", "\n",
"pprint(agent_logs)" "pprint(agent_logs)\n"
] ]
}, },
{ {
@ -2914,23 +2920,25 @@
"eval_rows = []\n", "eval_rows = []\n",
"\n", "\n",
"for log in agent_logs:\n", "for log in agent_logs:\n",
" last_msg = log['input'][-1]\n", " last_msg = log[\"input\"][-1]\n",
" if \"\\\"role\\\":\\\"user\\\"\" in last_msg:\n", " if '\"role\":\"user\"' in last_msg:\n",
" eval_rows.append(\n", " eval_rows.append(\n",
" {\n", " {\n",
" \"input_query\": last_msg,\n", " \"input_query\": last_msg,\n",
" \"generated_answer\": log[\"output\"],\n", " \"generated_answer\": log[\"output\"],\n",
" # check if generated_answer uses tools brave_search\n", " # check if generated_answer uses tools brave_search\n",
" \"expected_answer\": \"brave_search\",\n", " \"expected_answer\": \"brave_search\",\n",
" },\n", " },\n",
" )\n", " )\n",
"\n", "\n",
"pprint(eval_rows)\n", "pprint(eval_rows)\n",
"scoring_params = {\n", "scoring_params = {\n",
" \"basic::subset_of\": None,\n", " \"basic::subset_of\": None,\n",
"}\n", "}\n",
"scoring_response = client.scoring.score(input_rows=eval_rows, scoring_functions=scoring_params)\n", "scoring_response = client.scoring.score(\n",
"pprint(scoring_response)" " input_rows=eval_rows, scoring_functions=scoring_params\n",
")\n",
"pprint(scoring_response)\n"
] ]
}, },
{ {
@ -3031,7 +3039,9 @@
"EXPECTED_RESPONSE: {expected_answer}\n", "EXPECTED_RESPONSE: {expected_answer}\n",
"\"\"\"\n", "\"\"\"\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", "generated_answer = \"\"\"\n",
"Here are the top 5 topics that were explained in the documentation for Torchtune:\n", "Here are the top 5 topics that were explained in the documentation for Torchtune:\n",
"\n", "\n",
@ -3062,7 +3072,7 @@
"}\n", "}\n",
"\n", "\n",
"response = client.scoring.score(input_rows=rows, scoring_functions=scoring_params)\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" "tool_calls"
] ]
}, },
"GreedySamplingStrategy": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "greedy",
"default": "greedy"
}
},
"additionalProperties": false,
"required": [
"type"
]
},
"ImageContentItem": { "ImageContentItem": {
"type": "object", "type": "object",
"properties": { "properties": {
@ -3581,20 +3595,17 @@
"type": "object", "type": "object",
"properties": { "properties": {
"strategy": { "strategy": {
"$ref": "#/components/schemas/SamplingStrategy", "oneOf": [
"default": "greedy" {
}, "$ref": "#/components/schemas/GreedySamplingStrategy"
"temperature": { },
"type": "number", {
"default": 0.0 "$ref": "#/components/schemas/TopPSamplingStrategy"
}, },
"top_p": { {
"type": "number", "$ref": "#/components/schemas/TopKSamplingStrategy"
"default": 0.95 }
}, ]
"top_k": {
"type": "integer",
"default": 0
}, },
"max_tokens": { "max_tokens": {
"type": "integer", "type": "integer",
@ -3610,14 +3621,6 @@
"strategy" "strategy"
] ]
}, },
"SamplingStrategy": {
"type": "string",
"enum": [
"greedy",
"top_p",
"top_k"
]
},
"StopReason": { "StopReason": {
"type": "string", "type": "string",
"enum": [ "enum": [
@ -3871,6 +3874,45 @@
"content" "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": { "URL": {
"type": "object", "type": "object",
"properties": { "properties": {
@ -8887,6 +8929,10 @@
"name": "GraphMemoryBankParams", "name": "GraphMemoryBankParams",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/GraphMemoryBankParams\" />" "description": "<SchemaDefinition schemaRef=\"#/components/schemas/GraphMemoryBankParams\" />"
}, },
{
"name": "GreedySamplingStrategy",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/GreedySamplingStrategy\" />"
},
{ {
"name": "HealthInfo", "name": "HealthInfo",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/HealthInfo\" />" "description": "<SchemaDefinition schemaRef=\"#/components/schemas/HealthInfo\" />"
@ -9136,10 +9182,6 @@
"name": "SamplingParams", "name": "SamplingParams",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/SamplingParams\" />" "description": "<SchemaDefinition schemaRef=\"#/components/schemas/SamplingParams\" />"
}, },
{
"name": "SamplingStrategy",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/SamplingStrategy\" />"
},
{ {
"name": "SaveSpansToDatasetRequest", "name": "SaveSpansToDatasetRequest",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/SaveSpansToDatasetRequest\" />" "description": "<SchemaDefinition schemaRef=\"#/components/schemas/SaveSpansToDatasetRequest\" />"
@ -9317,6 +9359,14 @@
{ {
"name": "ToolRuntime" "name": "ToolRuntime"
}, },
{
"name": "TopKSamplingStrategy",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/TopKSamplingStrategy\" />"
},
{
"name": "TopPSamplingStrategy",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/TopPSamplingStrategy\" />"
},
{ {
"name": "Trace", "name": "Trace",
"description": "<SchemaDefinition schemaRef=\"#/components/schemas/Trace\" />" "description": "<SchemaDefinition schemaRef=\"#/components/schemas/Trace\" />"
@ -9456,6 +9506,7 @@
"GetSpanTreeRequest", "GetSpanTreeRequest",
"GraphMemoryBank", "GraphMemoryBank",
"GraphMemoryBankParams", "GraphMemoryBankParams",
"GreedySamplingStrategy",
"HealthInfo", "HealthInfo",
"ImageContentItem", "ImageContentItem",
"InferenceStep", "InferenceStep",
@ -9513,7 +9564,6 @@
"RunShieldResponse", "RunShieldResponse",
"SafetyViolation", "SafetyViolation",
"SamplingParams", "SamplingParams",
"SamplingStrategy",
"SaveSpansToDatasetRequest", "SaveSpansToDatasetRequest",
"ScoreBatchRequest", "ScoreBatchRequest",
"ScoreBatchResponse", "ScoreBatchResponse",
@ -9553,6 +9603,8 @@
"ToolPromptFormat", "ToolPromptFormat",
"ToolResponse", "ToolResponse",
"ToolResponseMessage", "ToolResponseMessage",
"TopKSamplingStrategy",
"TopPSamplingStrategy",
"Trace", "Trace",
"TrainingConfig", "TrainingConfig",
"Turn", "Turn",

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

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

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

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

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

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

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

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

78
docs/zero_to_hero_guide/04_Tool_Calling101.ipynb
View file

@ -26,27 +26,28 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"import os\n",
"import requests\n",
"import json\n",
"import asyncio\n", "import asyncio\n",
"import nest_asyncio\n", "import json\n",
"import os\n",
"from typing import Dict, List\n", "from typing import Dict, List\n",
"\n",
"import nest_asyncio\n",
"import requests\n",
"from dotenv import load_dotenv\n", "from dotenv import load_dotenv\n",
"from llama_stack_client import LlamaStackClient\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.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.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.agent_create_params import AgentConfig\n",
"from llama_stack_client.types.shared.tool_response_message import ToolResponseMessage\n",
"\n", "\n",
"# Allow asyncio to run in Jupyter Notebook\n", "# Allow asyncio to run in Jupyter Notebook\n",
"nest_asyncio.apply()\n", "nest_asyncio.apply()\n",
"\n", "\n",
"HOST='localhost'\n", "HOST = \"localhost\"\n",
"PORT=5001\n", "PORT = 5001\n",
"MODEL_NAME='meta-llama/Llama-3.2-3B-Instruct'" "MODEL_NAME = \"meta-llama/Llama-3.2-3B-Instruct\"\n"
] ]
}, },
{ {
@ -69,7 +70,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"load_dotenv()\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"
] ]
}, },
{ {
@ -118,7 +119,7 @@
" cleaned = {k: v for k, v in results[idx].items() if k in selected_keys}\n", " cleaned = {k: v for k, v in results[idx].items() if k in selected_keys}\n",
" clean_response.append(cleaned)\n", " clean_response.append(cleaned)\n",
"\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", " for message in messages:\n",
" if isinstance(message, CompletionMessage) and message.tool_calls:\n", " if isinstance(message, CompletionMessage) and message.tool_calls:\n",
" for tool_call in message.tool_calls:\n", " for tool_call in message.tool_calls:\n",
" if 'query' in tool_call.arguments:\n", " if \"query\" in tool_call.arguments:\n",
" query = tool_call.arguments['query']\n", " query = tool_call.arguments[\"query\"]\n",
" call_id = tool_call.call_id\n", " call_id = tool_call.call_id\n",
"\n", "\n",
" if query:\n", " if query:\n",
" search_result = await self.run_impl(query)\n", " search_result = await self.run_impl(query)\n",
" return [ToolResponseMessage(\n", " return [\n",
" call_id=call_id,\n", " ToolResponseMessage(\n",
" role=\"ipython\",\n", " call_id=call_id,\n",
" content=self._format_response_for_agent(search_result),\n", " role=\"ipython\",\n",
" tool_name=\"brave_search\"\n", " content=self._format_response_for_agent(search_result),\n",
" )]\n", " tool_name=\"brave_search\",\n",
" )\n",
" ]\n",
"\n", "\n",
" return [ToolResponseMessage(\n", " return [\n",
" call_id=\"no_call_id\",\n", " ToolResponseMessage(\n",
" role=\"ipython\",\n", " call_id=\"no_call_id\",\n",
" content=\"No query provided.\",\n", " role=\"ipython\",\n",
" tool_name=\"brave_search\"\n", " content=\"No query provided.\",\n",
" )]\n", " tool_name=\"brave_search\",\n",
" )\n",
" ]\n",
"\n", "\n",
" def _format_response_for_agent(self, search_result):\n", " def _format_response_for_agent(self, search_result):\n",
" parsed_result = json.loads(search_result)\n", " parsed_result = json.loads(search_result)\n",
@ -186,7 +191,7 @@
" f\" URL: {result.get('url', 'No URL')}\\n\"\n", " f\" URL: {result.get('url', 'No URL')}\\n\"\n",
" f\" Description: {result.get('description', 'No Description')}\\n\\n\"\n", " f\" Description: {result.get('description', 'No Description')}\\n\\n\"\n",
" )\n", " )\n",
" return formatted_result" " return formatted_result\n"
] ]
}, },
{ {
@ -209,7 +214,7 @@
"async def execute_search(query: str):\n", "async def execute_search(query: str):\n",
" web_search_tool = WebSearchTool(api_key=BRAVE_SEARCH_API_KEY)\n", " web_search_tool = WebSearchTool(api_key=BRAVE_SEARCH_API_KEY)\n",
" result = await web_search_tool.run_impl(query)\n", " result = await web_search_tool.run_impl(query)\n",
" print(\"Search Results:\", result)" " print(\"Search Results:\", result)\n"
] ]
}, },
{ {
@ -236,7 +241,7 @@
], ],
"source": [ "source": [
"query = \"Latest developments in quantum computing\"\n", "query = \"Latest developments in quantum computing\"\n",
"asyncio.run(execute_search(query))" "asyncio.run(execute_search(query))\n"
] ]
}, },
{ {
@ -288,19 +293,17 @@
"\n", "\n",
" # Initialize custom tool (ensure `WebSearchTool` is defined earlier in the notebook)\n", " # Initialize custom tool (ensure `WebSearchTool` is defined earlier in the notebook)\n",
" webSearchTool = WebSearchTool(api_key=BRAVE_SEARCH_API_KEY)\n", " webSearchTool = WebSearchTool(api_key=BRAVE_SEARCH_API_KEY)\n",
" \n", "\n",
" # Define the agent configuration, including the model and tool setup\n", " # Define the agent configuration, including the model and tool setup\n",
" agent_config = AgentConfig(\n", " agent_config = AgentConfig(\n",
" model=MODEL_NAME,\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", " instructions=\"\"\"You are a helpful assistant that responds to user queries with relevant information and cites sources when available.\"\"\",\n",
" sampling_params={\n", " sampling_params={\n",
" \"strategy\": \"greedy\",\n", " \"strategy\": {\n",
" \"temperature\": 1.0,\n", " \"type\": \"greedy\",\n",
" \"top_p\": 0.9,\n", " },\n",
" },\n", " },\n",
" tools=[\n", " tools=[webSearchTool.get_tool_definition()],\n",
" webSearchTool.get_tool_definition()\n",
" ],\n",
" tool_choice=\"auto\",\n", " tool_choice=\"auto\",\n",
" tool_prompt_format=\"python_list\",\n", " tool_prompt_format=\"python_list\",\n",
" input_shields=input_shields,\n", " input_shields=input_shields,\n",
@ -329,8 +332,9 @@
" async for log in EventLogger().log(response):\n", " async for log in EventLogger().log(response):\n",
" log.print()\n", " log.print()\n",
"\n", "\n",
"\n",
"# Run the function asynchronously in a Jupyter Notebook cell\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": [], "outputs": [],
"source": [ "source": [
"HOST = \"localhost\" # Replace with your host\n", "HOST = \"localhost\" # Replace with your host\n",
"PORT = 5001 # Replace with your port\n", "PORT = 5001 # Replace with your port\n",
"MODEL_NAME='meta-llama/Llama-3.2-3B-Instruct'" "MODEL_NAME = \"meta-llama/Llama-3.2-3B-Instruct\"\n"
] ]
}, },
{ {
@ -60,10 +60,12 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from dotenv import load_dotenv\n",
"import os\n", "import os\n",
"\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv()\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": [ "source": [
"import os\n", "import os\n",
"\n",
"from llama_stack_client import LlamaStackClient\n", "from llama_stack_client import LlamaStackClient\n",
"from llama_stack_client.lib.agents.agent import Agent\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.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n", "from llama_stack_client.types.agent_create_params import AgentConfig\n",
"\n", "\n",
"\n",
"async def agent_example():\n", "async def agent_example():\n",
" client = LlamaStackClient(base_url=f\"http://{HOST}:{PORT}\")\n", " client = LlamaStackClient(base_url=f\"http://{HOST}:{PORT}\")\n",
" agent_config = AgentConfig(\n", " agent_config = AgentConfig(\n",
" model=MODEL_NAME,\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", " instructions=\"You are a helpful assistant! If you call builtin tools like brave search, follow the syntax brave_search.call(…)\",\n",
" sampling_params={\n", " sampling_params={\n",
" \"strategy\": \"greedy\",\n", " \"strategy\": {\n",
" \"temperature\": 1.0,\n", " \"type\": \"greedy\",\n",
" \"top_p\": 0.9,\n", " },\n",
" },\n", " },\n",
" tools=[\n", " tools=[\n",
" {\n", " {\n",
@ -157,7 +161,7 @@
" log.print()\n", " log.print()\n",
"\n", "\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": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Write me a 2-sentence poem about the moon"} {"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 EOF
``` ```

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

@ -83,8 +83,8 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"LLAMA_STACK_API_TOGETHER_URL=\"https://llama-stack.together.ai\"\n", "LLAMA_STACK_API_TOGETHER_URL = \"https://llama-stack.together.ai\"\n",
"LLAMA31_8B_INSTRUCT = \"Llama3.1-8B-Instruct\"" "LLAMA31_8B_INSTRUCT = \"Llama3.1-8B-Instruct\"\n"
] ]
}, },
{ {
@ -107,12 +107,13 @@
" AgentConfigToolSearchToolDefinition,\n", " AgentConfigToolSearchToolDefinition,\n",
")\n", ")\n",
"\n", "\n",
"\n",
"# Helper function to create an agent with tools\n", "# Helper function to create an agent with tools\n",
"async def create_tool_agent(\n", "async def create_tool_agent(\n",
" client: LlamaStackClient,\n", " client: LlamaStackClient,\n",
" tools: List[Dict],\n", " tools: List[Dict],\n",
" instructions: str = \"You are a helpful assistant\",\n", " instructions: str = \"You are a helpful assistant\",\n",
" model: str = LLAMA31_8B_INSTRUCT\n", " model: str = LLAMA31_8B_INSTRUCT,\n",
") -> Agent:\n", ") -> Agent:\n",
" \"\"\"Create an agent with specified tools.\"\"\"\n", " \"\"\"Create an agent with specified tools.\"\"\"\n",
" print(\"Using the following model: \", model)\n", " print(\"Using the following model: \", model)\n",
@ -120,9 +121,9 @@
" model=model,\n", " model=model,\n",
" instructions=instructions,\n", " instructions=instructions,\n",
" sampling_params={\n", " sampling_params={\n",
" \"strategy\": \"greedy\",\n", " \"strategy\": {\n",
" \"temperature\": 1.0,\n", " \"type\": \"greedy\",\n",
" \"top_p\": 0.9,\n", " },\n",
" },\n", " },\n",
" tools=tools,\n", " tools=tools,\n",
" tool_choice=\"auto\",\n", " tool_choice=\"auto\",\n",
@ -130,7 +131,7 @@
" enable_session_persistence=True,\n", " enable_session_persistence=True,\n",
" )\n", " )\n",
"\n", "\n",
" return Agent(client, agent_config)" " return Agent(client, agent_config)\n"
] ]
}, },
{ {
@ -172,7 +173,8 @@
], ],
"source": [ "source": [
"# comment this if you don't have a BRAVE_SEARCH_API_KEY\n", "# 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", "\n",
"async def create_search_agent(client: LlamaStackClient) -> Agent:\n", "async def create_search_agent(client: LlamaStackClient) -> Agent:\n",
" \"\"\"Create an agent with Brave Search capability.\"\"\"\n", " \"\"\"Create an agent with Brave Search capability.\"\"\"\n",
@ -186,8 +188,8 @@
"\n", "\n",
" return await create_tool_agent(\n", " return await create_tool_agent(\n",
" client=client,\n", " client=client,\n",
" tools=[search_tool], # set this to [] if you don't have a BRAVE_SEARCH_API_KEY\n", " tools=[search_tool], # set this to [] if you don't have a BRAVE_SEARCH_API_KEY\n",
" model = LLAMA31_8B_INSTRUCT,\n", " model=LLAMA31_8B_INSTRUCT,\n",
" instructions=\"\"\"\n", " instructions=\"\"\"\n",
" You are a research assistant that can search the web.\n", " You are a research assistant that can search the web.\n",
" Always cite your sources with URLs when providing information.\n", " Always cite your sources with URLs when providing information.\n",
@ -198,9 +200,10 @@
"\n", "\n",
" SOURCES:\n", " SOURCES:\n",
" - [Source title](URL)\n", " - [Source title](URL)\n",
" \"\"\"\n", " \"\"\",\n",
" )\n", " )\n",
"\n", "\n",
"\n",
"# Example usage\n", "# Example usage\n",
"async def search_example():\n", "async def search_example():\n",
" client = LlamaStackClient(base_url=LLAMA_STACK_API_TOGETHER_URL)\n", " client = LlamaStackClient(base_url=LLAMA_STACK_API_TOGETHER_URL)\n",
@ -212,7 +215,7 @@
" # Example queries\n", " # Example queries\n",
" queries = [\n", " queries = [\n",
" \"What are the latest developments in quantum computing?\",\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",
"\n", "\n",
" for query in queries:\n", " for query in queries:\n",
@ -227,8 +230,9 @@
" async for log in EventLogger().log(response):\n", " async for log in EventLogger().log(response):\n",
" log.print()\n", " log.print()\n",
"\n", "\n",
"\n",
"# Run the example (in Jupyter, use asyncio.run())\n", "# Run the example (in Jupyter, use asyncio.run())\n",
"await search_example()" "await search_example()\n"
] ]
}, },
{ {
@ -286,12 +290,16 @@
} }
], ],
"source": [ "source": [
"from typing import TypedDict, Optional, Dict, Any\n",
"from datetime import datetime\n",
"import json\n", "import json\n",
"from llama_stack_client.types.tool_param_definition_param import ToolParamDefinitionParam\n", "from datetime import datetime\n",
"from llama_stack_client.types import CompletionMessage,ToolResponseMessage\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.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", "\n",
"class WeatherTool(CustomTool):\n", "class WeatherTool(CustomTool):\n",
" \"\"\"Example custom tool for weather information.\"\"\"\n", " \"\"\"Example custom tool for weather information.\"\"\"\n",
@ -305,16 +313,15 @@
" def get_params_definition(self) -> Dict[str, ToolParamDefinitionParam]:\n", " def get_params_definition(self) -> Dict[str, ToolParamDefinitionParam]:\n",
" return {\n", " return {\n",
" \"location\": ToolParamDefinitionParam(\n", " \"location\": ToolParamDefinitionParam(\n",
" param_type=\"str\",\n", " param_type=\"str\", description=\"City or location name\", required=True\n",
" description=\"City or location name\",\n",
" required=True\n",
" ),\n", " ),\n",
" \"date\": ToolParamDefinitionParam(\n", " \"date\": ToolParamDefinitionParam(\n",
" param_type=\"str\",\n", " param_type=\"str\",\n",
" description=\"Optional date (YYYY-MM-DD)\",\n", " description=\"Optional date (YYYY-MM-DD)\",\n",
" required=False\n", " required=False,\n",
" )\n", " ),\n",
" }\n", " }\n",
"\n",
" async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:\n", " async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:\n",
" assert len(messages) == 1, \"Expected single message\"\n", " assert len(messages) == 1, \"Expected single message\"\n",
"\n", "\n",
@ -337,20 +344,14 @@
" )\n", " )\n",
" return [message]\n", " return [message]\n",
"\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", " \"\"\"Simulate getting weather data (replace with actual API call).\"\"\"\n",
" # Mock implementation\n", " # Mock implementation\n",
" if date:\n", " if date:\n",
" return {\n", " return {\"temperature\": 90.1, \"conditions\": \"sunny\", \"humidity\": 40.0}\n",
" \"temperature\": 90.1,\n", " return {\"temperature\": 72.5, \"conditions\": \"partly cloudy\", \"humidity\": 65.0}\n",
" \"conditions\": \"sunny\",\n",
" \"humidity\": 40.0\n",
" }\n",
" return {\n",
" \"temperature\": 72.5,\n",
" \"conditions\": \"partly cloudy\",\n",
" \"humidity\": 65.0\n",
" }\n",
"\n", "\n",
"\n", "\n",
"async def create_weather_agent(client: LlamaStackClient) -> Agent:\n", "async def create_weather_agent(client: LlamaStackClient) -> Agent:\n",
@ -358,38 +359,33 @@
"\n", "\n",
" # Create the agent with the tool\n", " # Create the agent with the tool\n",
" weather_tool = WeatherTool()\n", " weather_tool = WeatherTool()\n",
" \n", "\n",
" agent_config = AgentConfig(\n", " agent_config = AgentConfig(\n",
" model=LLAMA31_8B_INSTRUCT,\n", " model=LLAMA31_8B_INSTRUCT,\n",
" #model=model_name,\n", " # model=model_name,\n",
" instructions=\"\"\"\n", " instructions=\"\"\"\n",
" You are a weather assistant that can provide weather information.\n", " You are a weather assistant that can provide weather information.\n",
" Always specify the location clearly in your responses.\n", " Always specify the location clearly in your responses.\n",
" Include both temperature and conditions in your summaries.\n", " Include both temperature and conditions in your summaries.\n",
" \"\"\",\n", " \"\"\",\n",
" sampling_params={\n", " sampling_params={\n",
" \"strategy\": \"greedy\",\n", " \"strategy\": {\n",
" \"temperature\": 1.0,\n", " \"type\": \"greedy\",\n",
" \"top_p\": 0.9,\n", " },\n",
" },\n", " },\n",
" tools=[\n", " tools=[weather_tool.get_tool_definition()],\n",
" weather_tool.get_tool_definition()\n",
" ],\n",
" tool_choice=\"auto\",\n", " tool_choice=\"auto\",\n",
" tool_prompt_format=\"json\",\n", " tool_prompt_format=\"json\",\n",
" input_shields=[],\n", " input_shields=[],\n",
" output_shields=[],\n", " output_shields=[],\n",
" enable_session_persistence=True\n", " enable_session_persistence=True,\n",
" )\n", " )\n",
"\n", "\n",
" agent = Agent(\n", " agent = Agent(client=client, agent_config=agent_config, custom_tools=[weather_tool])\n",
" client=client,\n",
" agent_config=agent_config,\n",
" custom_tools=[weather_tool]\n",
" )\n",
"\n", "\n",
" return agent\n", " return agent\n",
"\n", "\n",
"\n",
"# Example usage\n", "# Example usage\n",
"async def weather_example():\n", "async def weather_example():\n",
" client = LlamaStackClient(base_url=LLAMA_STACK_API_TOGETHER_URL)\n", " client = LlamaStackClient(base_url=LLAMA_STACK_API_TOGETHER_URL)\n",
@ -413,12 +409,14 @@
" async for log in EventLogger().log(response):\n", " async for log in EventLogger().log(response):\n",
" log.print()\n", " log.print()\n",
"\n", "\n",
"\n",
"# For Jupyter notebooks\n", "# For Jupyter notebooks\n",
"import nest_asyncio\n", "import nest_asyncio\n",
"\n",
"nest_asyncio.apply()\n", "nest_asyncio.apply()\n",
"\n", "\n",
"# Run the example\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.subcommand import Subcommand
from llama_stack.cli.table import print_table from llama_stack.cli.table import print_table
from llama_stack.distribution.utils.serialize import EnumEncoder
class ModelDescribe(Subcommand): class ModelDescribe(Subcommand):
@ -72,7 +71,7 @@ class ModelDescribe(Subcommand):
rows.append( rows.append(
( (
"Recommended sampling params", "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 # Sampling Parameters
st.markdown("##### Sampling Parameters") st.markdown("##### Sampling Parameters")
strategy = st.selectbox(
"Strategy",
["greedy", "top_p", "top_k"],
index=0,
)
temperature = st.slider( temperature = st.slider(
"Temperature", "Temperature",
min_value=0.0, 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.", 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 candidate_type == "model":
if temperature > 0.0:
strategy = {
"type": "top_p",
"temperature": temperature,
"top_p": top_p,
}
else:
strategy = {"type": "greedy"}
eval_candidate = { eval_candidate = {
"type": "model", "type": "model",
"model": selected_model, "model": selected_model,
"sampling_params": { "sampling_params": {
"strategy": strategy, "strategy": strategy,
"temperature": temperature,
"top_p": top_p,
"max_tokens": max_tokens, "max_tokens": max_tokens,
"repetition_penalty": repetition_penalty, "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() message_placeholder = st.empty()
full_response = "" 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( response = llama_stack_api.client.inference.chat_completion(
messages=[ messages=[
{"role": "system", "content": system_prompt}, {"role": "system", "content": system_prompt},
@ -103,8 +112,7 @@ if prompt := st.chat_input("Example: What is Llama Stack?"):
model_id=selected_model, model_id=selected_model,
stream=stream, stream=stream,
sampling_params={ sampling_params={
"temperature": temperature, "strategy": strategy,
"top_p": top_p,
"max_tokens": max_tokens, "max_tokens": max_tokens,
"repetition_penalty": repetition_penalty, "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"]): with st.chat_message(message["role"]):
st.markdown(message["content"]) 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( agent_config = AgentConfig(
model=selected_model, model=selected_model,
instructions=system_prompt, instructions=system_prompt,
sampling_params={ sampling_params={
"strategy": "greedy", "strategy": strategy,
"temperature": temperature,
"top_p": top_p,
}, },
tools=[ 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, initialize_model_parallel,
model_parallel_is_initialized, 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.args import ModelArgs
from llama_models.llama3.api.chat_format import ChatFormat, LLMInput from llama_models.llama3.api.chat_format import ChatFormat, LLMInput
from llama_models.llama3.api.datatypes import Model from llama_models.llama3.api.datatypes import Model
@ -363,11 +368,12 @@ class Llama:
max_gen_len = self.model.params.max_seq_len - 1 max_gen_len = self.model.params.max_seq_len - 1
model_input = self.formatter.encode_content(request.content) model_input = self.formatter.encode_content(request.content)
temperature, top_p = _infer_sampling_params(sampling_params)
yield from self.generate( yield from self.generate(
model_input=model_input, model_input=model_input,
max_gen_len=max_gen_len, max_gen_len=max_gen_len,
temperature=sampling_params.temperature, temperature=temperature,
top_p=sampling_params.top_p, top_p=top_p,
logprobs=bool(request.logprobs), logprobs=bool(request.logprobs),
include_stop_token=True, include_stop_token=True,
logits_processor=get_logits_processor( logits_processor=get_logits_processor(
@ -390,14 +396,15 @@ class Llama:
): ):
max_gen_len = self.model.params.max_seq_len - 1 max_gen_len = self.model.params.max_seq_len - 1
temperature, top_p = _infer_sampling_params(sampling_params)
yield from self.generate( yield from self.generate(
model_input=self.formatter.encode_dialog_prompt( model_input=self.formatter.encode_dialog_prompt(
request.messages, request.messages,
request.tool_prompt_format, request.tool_prompt_format,
), ),
max_gen_len=max_gen_len, max_gen_len=max_gen_len,
temperature=sampling_params.temperature, temperature=temperature,
top_p=sampling_params.top_p, top_p=top_p,
logprobs=bool(request.logprobs), logprobs=bool(request.logprobs),
include_stop_token=True, include_stop_token=True,
logits_processor=get_logits_processor( 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) is_word_start_token = len(decoded_after_0) > len(decoded_regular)
regular_tokens.append((token_idx, decoded_after_0, is_word_start_token)) regular_tokens.append((token_idx, decoded_after_0, is_word_start_token))
return regular_tokens 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.apis.models import Model
from llama_stack.providers.datatypes import ModelsProtocolPrivate from llama_stack.providers.datatypes import ModelsProtocolPrivate
from llama_stack.providers.utils.inference.openai_compat import ( from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
OpenAICompatCompletionChoice, OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse, OpenAICompatCompletionResponse,
process_chat_completion_response, process_chat_completion_response,
@ -126,21 +127,12 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
if sampling_params is None: if sampling_params is None:
return VLLMSamplingParams(max_tokens=self.config.max_tokens) 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 options = get_sampling_options(sampling_params)
kwargs = { if "repeat_penalty" in options:
"temperature": sampling_params.temperature, options["repetition_penalty"] = options["repeat_penalty"]
"max_tokens": self.config.max_tokens, del options["repeat_penalty"]
}
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
return VLLMSamplingParams(**kwargs) return VLLMSamplingParams(**options)
async def unregister_model(self, model_id: str) -> None: async def unregister_model(self, model_id: str) -> None:
pass 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, ModelRegistryHelper,
) )
from llama_stack.providers.utils.inference.openai_compat import ( from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_strategy_options,
OpenAICompatCompletionChoice, OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse, OpenAICompatCompletionResponse,
process_chat_completion_response, process_chat_completion_response,
@ -166,16 +167,13 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
) -> Dict: ) -> Dict:
bedrock_model = request.model bedrock_model = request.model
inference_config = {} sampling_params = request.sampling_params
param_mapping = { options = get_sampling_strategy_options(sampling_params)
"max_tokens": "max_gen_len",
"temperature": "temperature",
"top_p": "top_p",
}
for k, v in param_mapping.items(): if sampling_params.max_tokens:
if getattr(request.sampling_params, k): options["max_gen_len"] = sampling_params.max_tokens
inference_config[v] = getattr(request.sampling_params, k) if sampling_params.repetition_penalty > 0:
options["repetition_penalty"] = sampling_params.repetition_penalty
prompt = await chat_completion_request_to_prompt( prompt = await chat_completion_request_to_prompt(
request, self.get_llama_model(request.model), self.formatter request, self.get_llama_model(request.model), self.formatter
@ -185,7 +183,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
"body": json.dumps( "body": json.dumps(
{ {
"prompt": prompt, "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 cerebras.cloud.sdk import AsyncCerebras
from llama_models.datatypes import CoreModelId from llama_models.datatypes import CoreModelId
from llama_models.llama3.api.chat_format import ChatFormat 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_models.llama3.api.tokenizer import Tokenizer
from llama_stack.apis.common.content_types import InterleavedContent from llama_stack.apis.common.content_types import InterleavedContent
@ -172,7 +173,9 @@ class CerebrasInferenceAdapter(ModelRegistryHelper, Inference):
async def _get_params( async def _get_params(
self, request: Union[ChatCompletionRequest, CompletionRequest] self, request: Union[ChatCompletionRequest, CompletionRequest]
) -> dict: ) -> 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") raise ValueError("`top_k` not supported by Cerebras")
prompt = "" prompt = ""

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

@ -48,6 +48,9 @@ from llama_stack.apis.inference import (
ToolDefinition, ToolDefinition,
ToolPromptFormat, ToolPromptFormat,
) )
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_strategy_options,
)
def convert_chat_completion_request( def convert_chat_completion_request(
@ -77,6 +80,7 @@ def convert_chat_completion_request(
if request.tool_prompt_format != ToolPromptFormat.json: if request.tool_prompt_format != ToolPromptFormat.json:
warnings.warn("tool_prompt_format is not used by Groq. Ignoring.") warnings.warn("tool_prompt_format is not used by Groq. Ignoring.")
sampling_options = get_sampling_strategy_options(request.sampling_params)
return CompletionCreateParams( return CompletionCreateParams(
model=request.model, model=request.model,
messages=[_convert_message(message) for message in request.messages], messages=[_convert_message(message) for message in request.messages],
@ -84,8 +88,8 @@ def convert_chat_completion_request(
frequency_penalty=None, frequency_penalty=None,
stream=request.stream, stream=request.stream,
max_tokens=request.sampling_params.max_tokens or None, max_tokens=request.sampling_params.max_tokens or None,
temperature=request.sampling_params.temperature, temperature=sampling_options.get("temperature", 1.0),
top_p=request.sampling_params.top_p, top_p=sampling_options.get("top_p", 1.0),
tools=[_convert_groq_tool_definition(tool) for tool in request.tools or []], tools=[_convert_groq_tool_definition(tool) for tool in request.tools or []],
tool_choice=request.tool_choice.value if request.tool_choice else None, 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: if request.sampling_params.max_tokens:
payload.update(max_tokens=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) nvext.update(top_k=-1)
payload.update(top_p=request.sampling_params.top_p) payload.update(top_p=strategy.top_p)
elif request.sampling_params.strategy == "top_k": payload.update(temperature=strategy.temperature)
if ( elif isinstance(strategy, TopKSamplingStrategy):
request.sampling_params.top_k != -1 if strategy.top_k != -1 and strategy.top_k < 1:
and request.sampling_params.top_k < 1
):
warnings.warn("top_k must be -1 or >= 1") warnings.warn("top_k must be -1 or >= 1")
nvext.update(top_k=request.sampling_params.top_k) nvext.update(top_k=strategy.top_k)
elif request.sampling_params.strategy == "greedy": elif strategy.strategy == "greedy":
nvext.update(top_k=-1) nvext.update(top_k=-1)
payload.update(temperature=request.sampling_params.temperature) payload.update(temperature=strategy.temperature)
return payload return payload

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

@ -22,7 +22,12 @@ from llama_stack.apis.agents import (
ToolExecutionStep, ToolExecutionStep,
Turn, 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.apis.safety import ViolationLevel
from llama_stack.providers.datatypes import Api from llama_stack.providers.datatypes import Api
@ -42,7 +47,9 @@ def common_params(inference_model):
model=inference_model, model=inference_model,
instructions="You are a helpful assistant.", instructions="You are a helpful assistant.",
enable_session_persistence=True, 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=[], input_shields=[],
output_shields=[], output_shields=[],
toolgroups=[], 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, Function,
) )
from groq.types.shared.function_definition import FunctionDefinition 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_models.llama3.api.datatypes import ToolParamDefinition
from llama_stack.apis.inference import ( from llama_stack.apis.inference import (
ChatCompletionRequest, ChatCompletionRequest,
@ -152,21 +153,30 @@ class TestConvertChatCompletionRequest:
assert converted["max_tokens"] == 100 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 = 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) converted = convert_chat_completion_request(request)
assert converted["temperature"] == 0.5 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 = self._dummy_chat_completion_request()
request.sampling_params.top_p = 0.95 request.sampling_params.strategy = GreedySamplingStrategy()
converted = convert_chat_completion_request(request) converted = convert_chat_completion_request(request)
assert converted["top_p"] == 0.95 assert converted["temperature"] == 0.0
def test_includes_tool_choice(self): def test_includes_tool_choice(self):
request = self._dummy_chat_completion_request() 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.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 pydantic import BaseModel
from llama_stack.apis.common.content_types import ( from llama_stack.apis.common.content_types import (
@ -49,12 +55,26 @@ class OpenAICompatCompletionResponse(BaseModel):
choices: List[OpenAICompatCompletionChoice] 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: def get_sampling_options(params: SamplingParams) -> dict:
options = {} options = {}
if params: if params:
for attr in {"temperature", "top_p", "top_k", "max_tokens"}: options.update(get_sampling_strategy_options(params))
if getattr(params, attr): options["max_tokens"] = params.max_tokens
options[attr] = getattr(params, attr)
if params.repetition_penalty is not None and params.repetition_penalty != 1.0: if params.repetition_penalty is not None and params.repetition_penalty != 1.0:
options["repeat_penalty"] = params.repetition_penalty 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, model=model_id,
instructions="You are a helpful assistant", instructions="You are a helpful assistant",
sampling_params={ sampling_params={
"strategy": "greedy", "strategy": {
"temperature": 1.0, "type": "greedy",
"top_p": 0.9, "temperature": 1.0,
"top_p": 0.9,
},
}, },
toolgroups=[], toolgroups=[],
tool_choice="auto", tool_choice="auto",