Text Classification
Transformers
Safetensors
English
qwen3_5_text
feature-extraction
decision-model
calibration
full-weight-sft
weight-averaging
multiple-choice
typesafe
qwen3.8
Eval Results (legacy)
Instructions to use jaredpalmer/kev-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaredpalmer/kev-27b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jaredpalmer/kev-27b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jaredpalmer/kev-27b") model = AutoModel.from_pretrained("jaredpalmer/kev-27b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
running kev on dgx-spark
#2
by pawlz - opened
Hi,
has anyone successfully run Kev-27B on NVIDIA DGX Spark / GB10?
I’m looking for a known-working Docker image or reproducible setup for Spark (ARM64, CUDA 13, unified memory).
I’ve tried running the official Kev server with the full BF16 checkpoint, but after:
Loading weights: 100%
the process appears to hang indefinitely. GPU memory is allocated, but GPU utilization stays at 0%.
I also tried a few custom Docker setups with pinned PyTorch/Transformers versions and different loading strategies, but I haven’t found a stable configuration yet.
Does anyone have:
- a working Dockerfile/image for DGX Spark,
- exact versions of PyTorch / Transformers / safetensors / CUDA that work,
- or confirmation that Kev-27B is currently supported on GB10?
I’d be happy to test a reference setup and report back.
Hardware: NVIDIA DGX Spark, GB10, 128 GB unified memory
Thanks.
Pawlz