nace-ai/drex-dlm

Drex DLM

We introduce Drex DLM from Nace.AI, a decision model that answers typed questions about a given context. Pass that context as state, provide one or more named questions, and get a probability for every option. Questions can be yes/no, multiple choice or a rating scale, and all of them are scored in a single forward pass. The model is built on NVIDIA's Efficient-DLM-8B diffusion language model plus a small pointer head, and it serves the same POST /v1/systemone API as the hosted Drex API, so you can run it yourself.

BF16 weights · Q8_0 GGUF · llama.cpp fork (branch edlm) · Ollama fork (branch nace-edlm) · Agent skill

Tested on Apple M5 Max (128 GiB), Apple M5 Pro (48 GiB) and NVIDIA H100 80 GB SXM (CUDA). CPU-only inference has not been tested. Python 3.12 is the validated interpreter. The BF16 weights take about 16 GB; memory use grows with context length and batch size.

How it works

The shared context (state) uses bidirectional attention. Each question branch attends to the context and uses causal attention within itself; state tokens cannot attend to questions, and branches cannot attend to each other. A shared pointer head projects the final-layer state at the decision marker into a query and the state at each option-ending marker into a key. Scaled dot products, temperature scaling and a softmax over each question's options give the probabilities.

_{The diagram uses <decide> and </opt> as readable aliases for the decision and option-ending markers.}

Requests within the packed-token budget run in one forward pass; larger ones are split across question rows.

Drex DLM follows the System One request format used by Jev and by Kev, an open-source family of Jev-style models. SDKs and servers written for that format work unchanged, and the KEV_* environment variables in serve.py come from Kev's server code.

Performance

Drex DLM scores 52.31 on the Decision Index 0.2 leaderboard, ahead of the next five entries as of October 2026:

Model	Index	Knowledge & Reasoning	Language Understanding	Retrieval & Classification	Tools & Automation	Arts & Human Taste
Drex DLM	52.31	50.71	57.77	54.25	48.59	50.25
Decider chat · Gemma-4-31B	51.93	44.54	58.91	50.34	66.99	38.89
Jev	51.67	50.54	59.74	43.35	66.86	37.86
AutoJev-27B	50.94	40.93	61.90	42.00	69.98	39.88
Jebadiah 27B	50.32	38.82	58.97	45.48	69.30	39.02
simple-jev · Qwen3.8-27B	50.21	36.61	60.16	50.22	66.95	37.10

Quick start

git clone https://github.com/nace-ai/drex-decision-models.git
cd drex-decision-models
python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt
hf download nace-ai/drex-dlm --local-dir weights/drex-dlm
python inference.py --model weights/drex-dlm --request examples/request.json

The last command prints answers for the sample ticket. To run the server instead:

python serve.py --model weights/drex-dlm --port 8000
curl http://127.0.0.1:8000/v1/systemone \
  -H 'Content-Type: application/json' \
  -d @examples/request.json

GET /health returns {"status": "ok", "model": "drex-dlm"} once the weights are loaded. The commands below run from the repository root, with llama.cpp and ollama cloned inside it.

Request format

Field	What to put there
state	The document: a string, object, or list. Nested objects render as indented text.
questions	A dictionary of named questions. The names come back as the keys of answers.

{
  "state": {
    "ticket": "I was charged twice for the same order. Please refund the extra payment."
  },
  "questions": {
    "team": {
      "type": "choice",
      "instructions": "Which team should handle this ticket?",
      "criteria": {
        "billing": "Payments, charges, and refunds",
        "technical": "Bugs and outages",
        "other": "Anything else"
      }
    },
    "refund": {
      "type": "noul",
      "instructions": "Does the customer explicitly ask for a refund?"
    },
    "urgency": {
      "type": "score",
      "instructions": "How urgent is this ticket?",
      "criteria": ["Routine", "Soon", "Urgent"]
    }
  }
}

Type	You supply	You get back
choice	Named options with short descriptions. Key order is option order.	choice, a probability per option, and confidence
noul	A yes/no question, with optional criteria.true / criteria.false.	noul, the probability of "yes" (0 to 1)
score	An ordered scale, lowest first.	A probability-weighted score, plus legend, per-level probabilities and confidence

For choice, confidence rescales the winning probability above a uniform baseline; for score, it measures concentration around the modal level. The local score formula approximates the hosted API's.

Response from a local bfloat16 run of examples/request.json:

{
  "answers": {
    "team": {
      "type": "choice",
      "choice": "billing",
      "confidence": 0.9461,
      "probabilities": { "billing": 0.9641, "technical": 0.001, "other": 0.0349 }
    },
    "refund": { "type": "noul", "noul": 0.8548 },
    "urgency": {
      "type": "score",
      "score": 1.4078,
      "legend": ["Routine", "Soon", "Urgent"],
      "probabilities": { "0": 0.1876, "1": 0.2169, "2": 0.5955 },
      "confidence": 0.7039
    }
  }
}

usage.input_tokens is the encoded document plus questions (87 here). usage.output_tokens counts the serialized answers, not generated text. latency_ms is the scoring time.

Serving

Runner	Weights	Endpoint
Python	weights/drex-dlm (includes head.pt)	POST /v1/systemone, port 8000
llama-server	drex-dlm-f16.gguf	POST /v1/systemone, port 8097
Ollama	the same GGUF	POST /v1/systemone, port 11434

The Q8_0 GGUF is a smaller option for llama-server (pointer-head matrices stay F16). Download it into weights/drex-dlm and swap its path into the command below; its model card has the tested configuration.

If the combined questions exceed the packed-token cap, the runners score question rows separately as long as the state plus each question fits the row limit.

Python

source .venv/bin/activate
python serve.py --model weights/drex-dlm --host 127.0.0.1 --port 8000

--model is the directory holding the safetensors shards and head.pt; --name sets the fallback model name in responses.

llama-server

The edlm architecture, GGUF converter and /v1/systemone endpoint live on branch edlm of nace-ai/llama.cpp. The fork adds the Efficient-DLM architecture and the Kev pointer head to llama.cpp. You need CMake and a C/C++ toolchain (Xcode Metal toolchain on Apple Silicon). Run these from the repository root:

git clone --branch edlm --single-branch https://github.com/nace-ai/llama.cpp.git llama.cpp
cmake -S llama.cpp -B llama.cpp/build
cmake --build llama.cpp/build --target llama-server --parallel 8

On NVIDIA, add -DGGML_CUDA=ON when configuring. Convert the weights to GGUF:

python3.12 -m venv llama.cpp/.venv-convert
llama.cpp/.venv-convert/bin/python -m pip install \
  -r llama.cpp/requirements/requirements-convert_hf_to_gguf.txt
llama.cpp/.venv-convert/bin/python llama.cpp/convert_hf_to_gguf.py weights/drex-dlm \
  --outfile weights/drex-dlm/drex-dlm-f16.gguf --outtype f16

Start the server:

GGML_METAL_TENSOR_DISABLE=1 llama.cpp/build/bin/llama-server \
  -m weights/drex-dlm/drex-dlm-f16.gguf \
  --host 127.0.0.1 --port 8097 \
  --embedding --pooling none \
  -c 16384 -b 16384 -ub 16384 -np 1 --no-warmup

Then, from another terminal:

curl http://127.0.0.1:8097/v1/systemone \
  -H 'Content-Type: application/json' -d @examples/request.json

Keep GGML_METAL_TENSOR_DISABLE=1 on Apple Silicon: the Metal tensor matmul path gave incorrect results on long inputs. The flag is not needed on CUDA. Responses use the same answers schema as the Python server, plus latency_ms and an x-typesafe-request-id header.

Ollama

nace-ai/ollama, branch nace-edlm, launches a custom llama-server and forwards POST /v1/systemone. The fork adds System One inference support to Ollama. Finish the llama-server build and conversion above first. Build the runner and daemon from the repository root (Go 1.26; the toolchain downloads automatically):

git clone --branch nace-edlm --single-branch https://github.com/nace-ai/ollama.git ollama
cd ollama
export OLLAMA_LLAMA_CPP_SOURCE="$PWD/../llama.cpp"
cmake -S llama/server --preset darwin
cmake --build build/llama-server-darwin --target llama-server --parallel 8
GOTOOLCHAIN=auto go build -trimpath -o ollama .
cd ..

The darwin preset is for Apple Silicon. Write a Modelfile next to the GGUF:

cat > weights/drex-dlm/Modelfile <<'EOF'
FROM ./drex-dlm-f16.gguf
CAPABILITY decision
PARAMETER num_ctx 16384
EOF

Start the daemon and leave it running:

OLLAMA_HOST=127.0.0.1:11434 \
OLLAMA_LLAMA_SERVER="$PWD/ollama/build/llama-server-darwin/bin/llama-server" \
  ollama/ollama serve

In another terminal:

OLLAMA_HOST=127.0.0.1:11434 ollama/ollama create drex-dlm \
  -f weights/drex-dlm/Modelfile
curl http://127.0.0.1:11434/v1/systemone \
  -H 'Content-Type: application/json' -d @examples/request.json

GET /api/version checks readiness. The fork keeps native context, batch and microbatch capacities aligned and, on macOS, disables the Metal tensor path for eDLM runners.

Context length

The model supports up to 32,768 tokens. Every runner defaults to 16,384; the full window must be enabled explicitly.

Python:

KEV_CONTEXT=32768 python serve.py --model weights/drex-dlm --port 8000

llama-server. Raise the encode cap (SYSTEMONE_CONTEXT) and the slot size (-c, -b, -ub) together:

GGML_METAL_TENSOR_DISABLE=1 SYSTEMONE_CONTEXT=32768 llama.cpp/build/bin/llama-server \
  -m weights/drex-dlm/drex-dlm-f16.gguf \
  --host 127.0.0.1 --port 8097 \
  --embedding --pooling none \
  -c 32768 -b 32768 -ub 32768 -np 1 \
  --no-warmup

Ollama. Set PARAMETER num_ctx 32768 in the Modelfile, re-run ollama create, and restart the daemon with SYSTEMONE_CONTEXT=32768. num_ctx sizes the slot, decision batch and microbatch; SYSTEMONE_CONTEXT raises the encode cap. Keep them aligned.

KEV_SERVE_MAX_STATE, KEV_SERVE_MAX_BRANCH and KEV_SERVE_MAX_PACKED (Python), and the matching SYSTEMONE_* variables (llama-server), override individual caps. None are needed to use the full window.

Validation

Results were checked across the Python, llama-server and Ollama runners on Apple M5 Max and M5 Pro, and across Python and llama-server on CUDA. The Q8_0 GGUF loads and serves requests through llama-server at a 16,384-token context.

Files

This repository holds the weights: model-*-of-00004.safetensors (about 16 GB in bfloat16) and head.pt, the 256-dim pointer head with temperature 1.0. The inference code, server and example request are in the Drex repository.

Integrations

The Drex agent skill connects coding agents and other agentic harnesses to Drex. Its self-hosted instructions point an agent at a local /v1/systemone server; no API key is needed.

License

Model weights: CC BY-NC 4.0. Code: Apache-2.0, in the Drex repository.