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Hy3, Kimi-K2.5, INTELLECT-3, NousCoder. • 8 items • Updated
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("0xSero/NousCoder-14B-SFT-Tools", dtype="auto")Quick Links
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NousCoder-14B-SFT-Tools
SFT fine-tune of NousResearch/Hermes-3-Llama-3.1-8B.
At a glance
| Base model | NousResearch/Hermes-3-Llama-3.1-8B |
| Format | SFT |
| Total params | 14B |
| Active / token | — |
| Experts / layer | — |
| Layers | — |
| Hidden size | — |
| Context | — |
| On-disk size | 1 GB |
Which variant should I pick?
| Variant | Format | Link |
|---|---|---|
NousCoder-14B-SFT |
SFT | link |
NousCoder-14B-SFT-Tools (this) |
SFT | link |
NousCoder-14B-Tools |
Tools | link |
License & citation
License inherited from the base model.
@misc{lasby2025reap,
title = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
year = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}
Sponsors
Made possible by NVIDIA · TNG Technology · Lambda · Prime Intellect · Hot Aisle.
Model tree for 0xSero/NousCoder-14B-SFT-Tools
Base model
meta-llama/Llama-3.1-8B Finetuned
NousResearch/Hermes-3-Llama-3.1-8B
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="0xSero/NousCoder-14B-SFT-Tools")