(ql:quickload :cl-markov) (e.g., Shakespeare):
| Model Source | Command / Link | |--------------|----------------| | | wget https://huggingface.co/gpt2/resolve/main/model.safetensors | | BERT | wget https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin | | CodeLlama (7B) | Request from Meta, then download .gguf from Hugging Face |
(load "https://beta.quicklisp.org/quicklisp.lisp") (quicklisp-quickstart:install) (ql:add-to-init-file) Now you can install libraries with (ql:quickload :library-name) . Option A: cl-gpt2 – A Native GPT-2 Inference Engine cl-gpt2 loads a small transformer model and generates text. ai generator lisp download
Place these in ~/lisp-models/ and point your Lisp code there. ;; Step 1 – install SBCL and Quicklisp ;; Step 2 – in REPL (ql:quickload :cl-gpt2) ;; Step 3 – load model (downloads weights automatically) (defparameter ai (cl-gpt2:load-model :gpt2-medium))
(ql:quickload :cl-llama) (cl-llama:load-model "path/to/llama-2-7b.Q4_K_M.gguf") (cl-llama:generate "Once upon a time") If a Lisp library expects local weights: (ql:quickload :cl-markov) (e
;; Step 4 – generate text (cl-gpt2:generate ai "The future of artificial intelligence" :max-tokens 100 :temperature 0.8)
For a modern LLM generator in Lisp, use (easy) or cl-llama + llama.cpp (more powerful). Avoid implementing transformers from scratch unless educational. ;; Step 1 – install SBCL and Quicklisp
# Outside Lisp, using wget wget https://huggingface.co/gpt2/resolve/main/pytorch_model.bin Then convert to Lisp-native format using provided scripts. A lightweight Markov chain generator (no neural nets, purely statistical).