Text Classification
Transformers
Safetensors
edlm
feature-extraction
decision-model
system-one
typed-decisions
decision-index
classification
calibrated-probabilities
decisionbench
zero-shot
recommendation
custom_code
Instructions to use nace-ai/drex-dlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nace-ai/drex-dlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nace-ai/drex-dlm", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nace-ai/drex-dlm", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 287 Bytes
e95d5bf ca680a0 | 1 2 3 4 5 6 7 | # Model weights: CC BY-NC 4.0, not MIT. See MODEL_LICENSE.md.
# Model maximum: 32768 tokens; this local runner defaults to 16384 tokens.
# For 32768, set num_ctx to 32768 and start Ollama with SYSTEMONE_CONTEXT=32768.
FROM ./drex-dlm-f16.gguf
CAPABILITY decision
PARAMETER num_ctx 16384
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