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* Add support for Llada-8b: diffusion model * Add README * Fix README and convert_hf_to_gguf * convert_hf_to_gguf.py: address review comments * Make everything in a single example * Remove model-specific sampling * Remove unused argmax * Remove braced initializers, improve README.md a bit * Add diffusion specific gguf params in set_vocab, remove setting rope_theta and rms_norm_eps * Remove adding the mask token * Move add_add_bos_token to set_vocab * use add_bool in gguf_writer.py
Diffusion Text Generation
This directory contains implementations for Diffusion LLMs (DLLMs)
More Info:
Example of using Dream architechture: llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual
Example of using LLaDA architechture: llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual