llmware は AI PC およびローカルノートパソコン、エッジおよび自己ホスト型デプロイメント向けに最適化されており、Windows、Mac、Linux プラットフォームの広範囲に対応しています。GGUF、OpenVINO、ONNXRuntime、ONNXRuntime-QNN (Qualcomm)、WindowsLocalFoundry、PyTorch などをサポートし、ターゲットプラットフォーム向けに最適化された推論テクノロジーを簡単に活用できる高レベルなインターフェースを提供します。
llmware は 2 つの主要なコンポーネントで構成されています：
-
300 以上のモデルを含むモデルカタログ—デバイスの GPU および NPU 機能を活用するための量子化済み・最適化フォーマットで事前パッケージ化されたモデルです。主要なオープンソースモデルファミリーをサポートし、50 以上の llmware ファインチューン済み SLIM、Bling、Dragon、Industry-Bert モデルを備えており、エンタープライズプロセスオートメーションの重要なタスク向けに特化しています。OpenAI、Anthropic、Google などの主要なクラウドモデルにも対応しています。
-
RAG パイプライン—知識ソースを生成 AI モデルに接続するための完全なライフサイクル向けの統合コンポーネントです。豊富なドキュメント解析および取り込み機能を備えており、スケーラブルな知識ベースを作成する能力を提供します。
これら 2 つのコンポーネントを組み合わせることで、llmware は知識ベースのエンタープライズ LLM アプリケーションを迅速に構築するための包括的なツールセットを提供します。
当社のビジョンは、AI が持続可能で正確かつコスト効率的であり、タスクを完成させるために必要な最小限のコンピュートフットプリントを使用することです。
事実上すべての当社の例とモデルはデバイス上で実行できます—ノートパソコンですぐに始めることができます。
Discord に参加する | YouTube チュートリアルを視聴する | Hugging Face で当社のモデルファミリーを探索する
llmwareを使用してコードを書くことは、いくつかの主要なコンセプトに基づいています。
Model Catalog: 基盤となる実装に関わらず、簡単に検索して、すべてのモデルに同じ方法でアクセスできます。
# 300+ Models in Catalog with 50+ RAG-optimized BLING, DRAGON and Industry BERT models
# Full support for GGUF, OpenVINO, Onnxruntime, HuggingFace, Sentence Transformers and major API-based models
# Easy to extend to add custom models - see examples
from llmware.models import ModelCatalog
from llmware.prompts import Prompt
# all models accessed through the ModelCatalog
models = ModelCatalog().list_all_models()
# to use any model in the ModelCatalog - "load_model" method and pass the model_name parameter
my_model = ModelCatalog().load_model("llmware/bling-phi-3-gguf")
# call model with: inference
output = my_model.inference("what is the future of AI?", add_context="Here is the article to read")
# call model with: stream
for token in my_model.stream("What is the future of AI?"):
print(token, end="")
# to integrate model into a Prompt
prompter = Prompt().load_model("llmware/bling-tiny-llama-v0")
response = prompter.prompt_main("what is the future of AI?", context="Insert Sources of information")Library: 規模に合わせて知識のコレクションを取り込み、整理、インデックスします。パース、テキストチャンク、埋め込みを行います。
from llmware.library import Library
# to parse and text chunk a set of documents (pdf, pptx, docx, xlsx, txt, csv, md, json/jsonl, wav, png, jpg, html)
# step 1 - create a library, which is the 'knowledge-base container' construct
# - libraries have both text collection (DB) resources, and file resources (e.g., llmware_data/accounts/{library_name})
# - embeddings and queries are run against a library
lib = Library().create_new_library("my_library")
# step 2 - add_files is the universal ingestion function - point it at a local file folder with mixed file types
# - files will be routed by file extension to the correct parser, parsed, text chunked and indexed in text collection DB
lib.add_files("/folder/path/to/my/files")
# to install an embedding on a library - pick an embedding model and vector_db
lib.install_new_embedding(embedding_model_name="mini-lm-sbert", vector_db="milvus", batch_size=500)
# to add a second embedding to the same library (mix-and-match models + vector db)
lib.install_new_embedding(embedding_model_name="industry-bert-sec", vector_db="chromadb", batch_size=100)
# easy to create multiple libraries for different projects and groups
finance_lib = Library().create_new_library("finance_q4_2023")
finance_lib.add_files("/finance_folder/")
hr_lib = Library().create_new_library("hr_policies")
hr_lib.add_files("/hr_folder/")
# pull library card with key metadata - documents, text chunks, images, tables, embedding record
lib_card = Library().get_library_card("my_library")
# see all libraries
all_my_libs = Library().get_all_library_cards()Query: テキスト、セマンティック、ハイブリッド、メタデータ、カスタムフィルタの組み合わせを使用してライブラリをクエリします。
from llmware.retrieval import Query
from llmware.library import Library
# step 1 - load the previously created library
lib = Library().load_library("my_library")
# step 2 - create a query object and pass the library
q = Query(lib)
# step 3 - run lots of different queries (many other options in the examples)
# basic text query
results1 = q.text_query("text query", result_count=20, exact_mode=False)
# semantic query
results2 = q.semantic_query("semantic query", result_count=10)
# combining a text query restricted to only certain documents in the library and "exact" match to the query
results3 = q.text_query_with_document_filter("new query", {"file_name": "selected file name"}, exact_mode=True)
# to apply a specific embedding (if multiple on library), pass the names when creating the query object
q2 = Query(lib, embedding_model_name="mini_lm_sbert", vector_db="milvus")
results4 = q2.semantic_query("new semantic query")Prompt with Sources: 知識検索とLLM推論を組み合わせる最も簡単な方法です。
from llmware.prompts import Prompt
from llmware.retrieval import Query
from llmware.library import Library
# build a prompt
prompter = Prompt().load_model("llmware/bling-tiny-llama-v0")
# add a file -> file is parsed, text chunked, filtered by query, and then packaged as model-ready context,
# including in batches, if needed, to fit the model context window
source = prompter.add_source_document("/folder/to/one/doc/", "filename", query="fast query")
# attach query results (from a Query) into a Prompt
my_lib = Library().load_library("my_library")
results = Query(my_lib).query("my query")
source2 = prompter.add_source_query_results(results)
# run a new query against a library and load directly into a prompt
source3 = prompter.add_source_new_query(my_lib, query="my new query", query_type="semantic", result_count=15)
# to run inference with 'prompt with sources'
responses = prompter.prompt_with_source("my query")
# to run fact-checks - post inference
fact_check = prompter.evidence_check_sources(responses)
# to view source materials (batched 'model-ready' and attached to prompt)
source_materials = prompter.review_sources_summary()
# to see the full prompt history
prompt_history = prompter.get_current_history()RAG-Optimized Models - RAGワークフロー統合とローカル実行用に設計された1〜7Bパラメータモデル。
""" This 'Hello World' example demonstrates how to get started using local BLING models with provided context, using both Pytorch and GGUF versions. """ import time from llmware.prompts import Prompt def hello_world_questions(): test_list = [ {"query": "What is the total amount of the invoice?", "answer": "$22,500.00", "context": "Services Vendor Inc. \n100 Elm Street Pleasantville, NY \nTO Alpha Inc. 5900 1st Street " "Los Angeles, CA \nDescription Front End Engineering Service $5000.00 \n Back End Engineering" " Service $7500.00 \n Quality Assurance Manager $10,000.00 \n Total Amount $22,500.00 \n" "Make all checks payable to Services Vendor Inc. Payment is due within 30 days." "If you have any questions concerning this invoice, contact Bia Hermes. " "THANK YOU FOR YOUR BUSINESS! INVOICE INVOICE # 0001 DATE 01/01/2022 FOR Alpha Project P.O. # 1000"}, {"query": "What was the amount of the trade surplus?", "answer": "62.4 billion yen ($416.6 million)", "context": "Japan's September trade balance swings into surplus, surprising expectations" "Japan recorded a trade surplus of 62.4 billion yen ($416.6 million) for September, " "beating expectations from economists polled by Reuters for a trade deficit of 42.5 " "billion yen. Data from Japan's customs agency revealed that exports in September " "increased 4.3% year on year, while imports slid 16.3% compared to the same period " "last year. According to FactSet, exports to Asia fell for the ninth straight month, " "which reflected ongoing China weakness. Exports were supported by shipments to " "Western markets, FactSet added. — Lim Hui Jie"}, {"query": "When did the LISP machine market collapse?", "answer": "1987.", "context": "The attendees became the leaders of AI research in the 1960s." " They and their students produced programs that the press described as 'astonishing': " "computers were learning checkers strategies, solving word problems in algebra, " "proving logical theorems and speaking English. By the middle of the 1960s, research in " "the U.S. was heavily funded by the Department of Defense and laboratories had been " "established around the world. Herbert Simon predicted, 'machines will be capable, " "within twenty years, of doing any work a man can do'. Marvin Minsky agreed, writing, " "'within a generation ... the problem of creating 'artificial intelligence' will " "substantially be solved'. They had, however, underestimated the difficulty of the problem. " "Both the U.S. and British governments cut off exploratory research in response " "to the criticism of Sir James Lighthill and ongoing pressure from the US Congress " "to fund more productive projects. Minsky's and Papert's book Perceptrons was understood " "as proving that artificial neural networks approach would never be useful for solving " "real-world tasks, thus discrediting the approach altogether. The 'AI winter', a period " "when obtaining funding for AI projects was difficult, followed. In the early 1980s, " "AI research was revived by the commercial success of expert systems, a form of AI " "program that simulated the knowledge and analytical skills of human experts. By 1985, " "the market for AI had reached over a billion dollars. At the same time, Japan's fifth " "generation computer project inspired the U.S. and British governments to restore funding " "for academic research. However, beginning with the collapse of the Lisp Machine market " "in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began."}, {"query": "What is the current rate on 10-year treasuries?", "answer": "4.58%", "context": "Stocks rallied Friday even after the release of stronger-than-expected U.S. jobs data " "and a major increase in Treasury yields. The Dow Jones Industrial Average gained 195.12 points, " "or 0.76%, to close at 31,419.58. The S&P 500 added 1.59% at 4,008.50. The tech-heavy " "Nasdaq Composite rose 1.35%, closing at 12,299.68. The U.S. economy added 438,000 jobs in " "August, the Labor Department said. Economists polled by Dow Jones expected 273,000 " "jobs. However, wages rose less than expected last month. Stocks posted a stunning " "turnaround on Friday, after initially falling on the stronger-than-expected jobs report. " "At its session low, the Dow had fallen as much as 198 points; it surged by more than " "500 points at the height of the rally. The Nasdaq and the S&P 500 slid by 0.8% during " "their lowest points in the day. Traders were unclear of the reason for the intraday " "reversal. Some noted it could be the softer wage number in the jobs report that made " "investors rethink their earlier bearish stance. Others noted the pullback in yields from " "the day's highs. Part of the rally may just be to do a market that had gotten extremely " "oversold with the S&P 500 at one point this week down more than 9% from its high earlier " "this year. Yields initially surged after the report, with the 10-year Treasury rate trading " "near its highest level in 14 years. The benchmark rate later eased from those levels, but " "was still up around 6 basis points at 4.58%. 'We're seeing a little bit of a give back " "in yields from where we were around 4.8%. [With] them pulling back a bit, I think that's " "helping the stock market,' said Margaret Jones, chief investment officer at Vibrant Industries " "Capital Advisors. 'We've had a lot of weakness in the market in recent weeks, and potentially " "some oversold conditions.'"}, {"query": "Is the expected gross margin greater than 70%?", "answer": "Yes, between 71.5% and 72.%", "context": "Outlook NVIDIA's outlook for the third quarter of fiscal 2024 is as follows:" "Revenue is expected to be $16.00 billion, plus or minus 2%. GAAP and non-GAAP " "gross margins are expected to be 71.5% and 72.5%, respectively, plus or minus " "50 basis points. GAAP and non-GAAP operating expenses are expected to be " "approximately $2.95 billion and $2.00 billion, respectively. GAAP and non-GAAP " "other income and expense are expected to be an income of approximately $100 " "million, excluding gains and losses from non-affiliated investments. GAAP and " "non-GAAP tax rates are expected to be 14.5%, plus or minus 1%, excluding any discrete items." "Highlights NVIDIA achieved progress since its previous earnings announcement " "in these areas: Data Center Second-quarter revenue was a record $10.32 billion, " "up 141% from the previous quarter and up 171% from a year ago. Announced that the " "NVIDIA® GH200 Grace™ Hopper™ Superchip for complex AI and HPC workloads is shipping " "this quarter, with a second-generation version with HBM3e memory expected to ship " "in Q2 of calendar 2024. "}, {"query": "What is Bank of America's rating on Target?", "answer": "Buy", "context": "Here are some of the tickers on my radar for Thursday, Oct. 12, taken directly from " "my reporter's notebook: It's the one-year anniversary of the S&P 500′s bear market bottom " "of 3,577. Since then, as of Wednesday's close of 4,376, the broad market index " "soared more than 22%. Hotter than expected September consumer price index, consumer " "inflation. The Social Security Administration issues announced a 3.2% cost-of-living " "adjustment for 2024. Chipotle Mexican Grill (CMG) plans price increases. Pricing power. " "Cites consumer price index showing sticky retail inflation for the fourth time " "in two years. Bank of America upgrades Target (TGT) to buy from neutral. Cites " "risk/reward from depressed levels. Traffic could improve. Gross margin upside. " "Merchandising better. Freight and transportation better. Target to report quarter " "next month. In retail, the CNBC Investing Club portfolio owns TJX Companies (TJX), " "the off-price juggernaut behind T.J. Maxx, Marshalls and HomeGoods. Goldman Sachs " "tactical buy trades on Club names Wells Fargo (WFC), which reports quarter Friday, " "Humana (HUM) and Nvidia (NVDA). BofA initiates Snowflake (SNOW) with a buy rating." "If you like this story, sign up for Jim Cramer's Top 10 Morning Thoughts on the " "Market email newsletter for free. Barclays cuts price targets on consumer products: " "UTZ Brands (UTZ) to $16 per share from $17. Kraft Heinz (KHC) to $36 per share from " "$38. Cyclical drag. J.M. Smucker (SJM) to $129 from $160. Secular headwinds. " "Coca-Cola (KO) to $59 from $70. Barclays cut PTs on housing-related stocks: Toll Brothers" "(TOL) to $74 per share from $82. Keeps underweight. Lowers Trex (TREX) and Azek" "(AZEK), too. Goldman Sachs (GS) announces sale of fintech platform and warns on " "third quarter of 19-cent per share drag on earnings. The buyer: investors led by " "private equity firm Sixth Street. Exiting a mistake. Rise in consumer engagement for " "Spotify (SPOT), says Morgan Stanley. The analysts hike price target to $190 per share " "from $185. Keeps overweight (buy) rating. JPMorgan loves elf Beauty (ELF). Keeps " "overweight (buy) rating but lowers price target to $139 per share from $150. " "Sees "still challenging" environment into third-quarter print. The Club owns shares " "in high-end beauty company Estee Lauder (EL). Barclays upgrades First Solar (FSLR) " "to overweight from equal weight (buy from hold) but lowers price target to $224 per " "share from $230. Risk reward upgrade. Best visibility of utility scale names."}, {"query": "What was the rate of decline in 3rd quarter sales?", "answer": "20% year-on-year.", "context": "Nokia said it would cut up to 14,000 jobs as part of a cost cutting plan following " "third quarter earnings that plunged. The Finnish telecommunications giant said that " "it will reduce its cost base and increase operation efficiency to "address the " "challenging market environment. The substantial layoffs come after Nokia reported " "third-quarter net sales declined 20% year-on-year to 4.98 billion euros. Profit over " "the period plunged by 69% year-on-year to 133 million euros."}, {"query": "What is a list of the key points?", "answer": "•Stocks rallied on Friday with stronger-than-expected U.S jobs data and increase i
