Finetuning an LLM, while incredibly powerful, operates like a black box, making the reasoning behind its responses more opaque. As the model internalises the information from the dataset, it becomes challenging to discern the exact source or reasoning behind each response. This can make it...
Retrieval Augmented Generation (RAG)is a popular technique to get LLMs to provide answers that are grounded in a data source. When we use RAG, we use the user's question to search a knowledge base (like Azure AI Search), then pass along both the question and the rele...
The first is to fine-tune the baseline LLM with propriety and context-relevant data. The second, and most cost-effective, approach is to connect the LLM to a data store with a retrieval model that extracts semantically relevant information from the database to add context to the LLM user in...
the vastness of the training data often surpasses the model’s parameters, leading to responses that may not be entirely accurate. Moreover, the diverse information used in training can cause the LLM to conflate details, resulting in plausible...
OpenRouter is a unified API to access any LLM. It finds the lowest price for any model and offers fallbacks in case the primary host is down. According to OpenRouter’s documentation, the main benefits of using OpenRouter include: OpenRouter是一个统一的API,用于访问任何LLM。它找到了任何型号...
因为Qwen本次支持了Transformers,使用HuggingFaceLLM加载模型,模型为(Qwen1.5-4B-Chat) # Model names qwen2_4B_CHAT = "qwen/Qwen1.5-4B-Chat" selected_model = snapshot_download(qwen2_4B_CHAT) SYSTEM_PROMPT = """You are a helpful AI assistant. """ query_wrapper_prompt = PromptTemplate( "[INST...
RAG or Fine-tuning ?In addition to RAG, the main optimization strategies for LLMs also include Prompt Engineering and Fine-tuning (FT). Each has its own unique features. Depending on their reliance on external knowledge and requirements for model adjustment, they each have suitable scenarios....
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) tools = [retriever_tool] agent = create_openai_functions_agent( llm, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) message_history = ChatMessageHistory() ...
🔥🔥🔥Multi-Model Agents with SLIM Models-Intro-Video🔥🔥🔥 Intro to SLIM Function Call Models Can't wait? Get SLIMs right away: fromllmware.modelsimportModelCatalogModelCatalog().get_llm_toolkit()# get all SLIM models, delivered as small, fast quantized toolsModelCatalog().tool_tes...
llm = OpenAI(temperature=0, model="gpt-3.5-turbo") gpt3 = OpenAI(temperature=0, model="text-davinci-003") embed_model = OpenAIEmbedding(model= OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002) service_context_gpt3 = ServiceContext.from_defaults(llm=gpt3, chunk_size = 256, chunk_overlap...