Text Classification
Transformers
Safetensors
edlm
feature-extraction
decision-model
system-one
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
Download Modelfile from nace-ai/drex-dlm: direct link, hf CLI and curl.
- Browser
- Download file 287 Bytes
-
https://huggingface.co/nace-ai/drex-dlm/resolve/main/Modelfile
- Command line
-
hf download hf://nace-ai/drex-dlm/Modelfile
-
curl -L -o Modelfile https://huggingface.co/nace-ai/drex-dlm/resolve/main/Modelfile
287 Bytes
| # 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 | |