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Xi Yan 2025-03-20 16:10:59 -07:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from llama_stack_client import LlamaStackClient\n",
"from llama_stack_client.types import Document\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from llama_stack_client.lib.agents.agent import Agent\n",
"from rich.pretty import pprint\n",
"import json\n",
"import uuid\n",
"from pydantic import BaseModel\n",
"import rich\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"MODEL_ID = \"meta-llama/Llama-3.3-70B-Instruct\"\n",
"\n",
"client = LlamaStackClient(\n",
" base_url=\"http://localhost:8321\",\n",
" provider_data={\n",
" \"fireworks_api_key\": os.environ[\"FIREWORKS_API_KEY\"]\n",
" }\n",
")\n",
"\n",
"urls = [\n",
" \"memory_optimizations.rst\",\n",
" \"chat.rst\",\n",
" \"llama3.rst\",\n",
" \"datasets.rst\",\n",
" \"qat_finetune.rst\",\n",
" \"lora_finetune.rst\",\n",
"]\n",
"\n",
"attachments = [\n",
" {\n",
" \"content\": f\"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}\",\n",
" \"mime_type\": \"text/plain\",\n",
" }\n",
"\n",
" for i, url in enumerate(urls)\n",
"]\n",
"\n",
"simple_agent = Agent(client, model=MODEL_ID, \n",
" instructions=\"You are a helpful assistant that can answer questions about the Torchtune project.\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">api_model_response</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">CompletionMessage</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Torchtune supports the following precision formats:\\n\\n* FP32 (32-bit floating point)\\n* FP16 (16-bit floating point)\\n* INT8 (8-bit integer)\\n* BF16 (Brain Floating Point 16, a 16-bit floating point format)\\n\\nThese precision formats can be used for model weights, activations, and gradients, allowing for flexible and efficient tuning of models for various hardware and performance requirements.'</span>,\n",
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"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">]</span>,\n",
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"<span style=\"font-weight: bold\">)</span>\n",
"</pre>\n"
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"\u001b[2;32m│ │ \u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m'Torchtune supports the following precision formats:\\n\\n* FP32 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m32-bit floating point\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* FP16 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m16-bit floating point\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* INT8 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m8-bit integer\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* BF16 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBrain Floating Point 16, a 16-bit floating point format\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nThese precision formats can be used for model weights, activations, and gradients, allowing for flexible and efficient tuning of models for various hardware and performance requirements.'\u001b[0m,\n",
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"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mturn_id\u001b[0m=\u001b[32m'efb92c6d-d482-4dd2-ad4b-3250c1e9a231'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mcompleted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m22\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m618765\u001b[0m, \u001b[33mtzinfo\u001b[0m=\u001b[1;35mTzInfo\u001b[0m\u001b[1m(\u001b[0mUTC\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mstarted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m22\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m175855\u001b[0m, \u001b[33mtzinfo\u001b[0m=\u001b[1;35mTzInfo\u001b[0m\u001b[1m(\u001b[0mUTC\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mturn_id\u001b[0m=\u001b[32m'efb92c6d-d482-4dd2-ad4b-3250c1e9a231'\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mcompleted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m22\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m631357\u001b[0m, \u001b[33mtzinfo\u001b[0m=\u001b[1;35mTzInfo\u001b[0m\u001b[1m(\u001b[0mUTC\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33moutput_attachments\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"simple_session_id = simple_agent.create_session(session_name=f\"simple_session_{uuid.uuid4()}\")\n",
"response = simple_agent.create_turn(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"What precision formats does torchtune support?\"\n",
" }\n",
" ],\n",
" session_id=simple_session_id,\n",
" stream=False\n",
" )\n",
"\n",
"pprint(response)\n",
"\n",
"session_response = client.agents.session.retrieve(agent_id=simple_agent.agent_id, session_id=simple_session_id)\n",
"pprint(session_response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "master",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 2
}