Instructions to use Polydim-Einsof/DeepSeek-R1-Distill-Qwen-14B-Einsof-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Polydim-Einsof/DeepSeek-R1-Distill-Qwen-14B-Einsof-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-Distill-Qwen-14B-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Polydim-Einsof/DeepSeek-R1-Distill-Qwen-14B-Einsof-LoRA") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
DeepSeek-R1-Distill-Qwen-14B-Einsof-LoRA (v020)
Autor: Ariel García Traba (Independent Research / EinsofOS)
Institución: Einsof Research / Antigravity Autonomous Compute
Entrenado en: Google Colab Plus (NVIDIA A100-SXM4-40GB)
Modelo Base: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
Corpus Canónico: 1.736 teoremas formales de PostgreSQL 16 (polydim_vec_db) + 13 Leyes de Silicio de Einsof.
Hiperparámetros de Entrenamiento
- LoRA Rank (r): 16
- LoRA Alpha (α): 16
- Target Modules:
q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj - Parámetros Entrenables: 68,812,800
- Precisión Nativa: BF16 nativo (A100 SXM4)
- Optimizador:
adamw_8bit - Pérdida Final de Entrenamiento: 0.7225
Uso con Unsloth / Hugging Face Transformers
from unsloth import FastLanguageModel
import torch
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="polydim-einsof/DeepSeek-R1-Distill-Qwen-14B-Einsof-LoRA",
max_seq_length=4096,
dtype=torch.bfloat16,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
Global Scientific Indexing & Persistent Identifiers (CERN Zenodo / CNRS HAL / JST Jxiv / OSF / ORCID)
- Author: Ariel H. García Traba (ORCID: 0009-0001-2787-6067)
- Affiliation: EinsofOS Independent Research / POLYDIM Research Initiative
- CERN / Zenodo Primary DOI: 10.5281/zenodo.21136742
- CERN / Zenodo Secondary DOI: 10.5281/zenodo.21209973
- CNRS / HAL Open Science (France):
hal-05847496v1 - Center for Open Science (OSF):
https://osf.io/za3s7 - JST / Jxiv Preprint Server (Japan):
Jxiv ID 7040 - Canonical Papers & LaTeX Archive:
Polydim-Einsof/POLYDIM-EINSOF-Canonical-Papers-V1550
@article{garciatraba2026polydim,
title = {Dimension Is All You Need: Eradicating the 1D Worm Collapse via Native High-Dimensional Manifold Inter-Process Communication in S^{D-1}},
author = {Garc{\'i}a Traba, Ariel H.},
year = {2026},
month = {nov},
publisher = {Zenodo / CERN & HAL Open Science},
doi = {10.5281/zenodo.21209973},
url = {https://doi.org/10.5281/zenodo.21209973},
note = {Primary DOI: 10.5281/zenodo.21136742; HAL ID: hal-05847496v1; OSF: https://osf.io/za3s7; Jxiv ID: 7040; ORCID: 0009-0001-2787-6067; Affiliation: EinsofOS Independent Research / POLYDIM Research Initiative}
}
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