Figuring out general specs for running LLM models – Deep-learning
The Problem: I have a machine learning model with a certain number of parameters. How … Read more
The Problem: I have a machine learning model with a certain number of parameters. How … Read more
Quick Fix: Install python-magic and python-magic-bin using pip to resolve the issue. The Problem: Loading … Read more
The Solutions: Solution 1: Use a model with instruction tuning and logits processor To resolve … Read more
Quick Fix: To increase the maximum token size in a Hugging Face model, you can … Read more
Quick Fix: Enclose the system_message within the <<SYS>> and <</SYS>> tags, and place it within … Read more
Quick Fix: Instead of checking solvability by comparing the parity of the start and goal … Read more
Quick Fix: To use SQLDatabaseChain from LangChain with memory, you can add a template to … Read more
Quick Fix: 1. Install brew, git, and pyenv. 2. Clone the Stable Diffusion web UI … Read more
Quick Fix: Create the chat object ConversationalRetrievalChain outside the ask function and pass it as … Read more
Quick Fix: Replace the string value assigned to the context key with a lambda function … Read more
Quick Fix: You can use the Weighted-Mean-Pooling approach to retrieve sentence embeddings from the Llama … Read more
The Problem: How to provide prompts to the ConversationalRetrievalChain function from Langchain for guiding the … Read more
Quick Fix: Install llama-cpp-python and huggingface_hub, download the required model file, instantiate the model from … Read more
Quick Fix: The auto_find_batch_size argument automates the batch size lowering process. Enable it to use … Read more
Quick Fix: To return the result of llama_index’s index.query(query, streaming=True) query, use StreamingResponse like return … Read more