Drex DLM

Drex DLM is a decision model from Nace.AI that answers typed questions about a supplied context. You pass the context as state along with one or more named questions, and it returns a probability for every option. The repository provides Python inference and server code, and the weights are published on Hugging Face in BF16 and Q8_0 GGUF forms.

What It Is

Drex DLM is built on NVIDIA Efficient-DLM-8B, a diffusion language model, with a decision adapter merged into its weights. One forward pass plus a shared pointer head produces the probability distribution. The pointer head projects the hidden state at a decision marker into a query and the hidden states at option-ending markers into keys. Scaled dot products, temperature scaling and a softmax then give per-question probabilities. The shared context uses bidirectional attention, and question branches attend to that context but not to one another.

Request and Output Format

A request has a state (a string, object or list) and a dictionary of named questions. Three question types are supported:

• choice: named options, returning the chosen option, a probability per option and a confidence value.
• noul: a yes/no question, returning the probability of yes.
• score: an ordered scale, returning a probability-weighted score, a legend, per-level probabilities and a confidence value.

Responses also include token usage and scoring latency. One decision per request is the stated release contract.

Serving Options

The model can be run through three local runners that each expose POST /v1/systemone: a Python server, a llama-server build from the edlm branch of Nace's llama.cpp fork, and a custom Ollama fork. The maximum context window is 32,768 tokens, while the local runners default to 16,384 tokens. A separate Drex agent skill lets coding agents and other harnesses call a self-hosted server without a hosted API key.

Requirements and Licensing

Python 3.12 is the validated interpreter. Local inference was tested on an Apple M5 Max with 128 GiB of unified memory; CUDA and CPU-only inference have not been validated. BF16 weights take about 16 GB. The model weights are released under CC BY-NC 4.0, while the original Nace.AI code in the repository is MIT licensed.