ModelÜberprüftExtern

Dpo Writingprompts Qwen3 32b As Nemotron Nano 30b A3b Seed1

LoRA adapter trained via Tinker as part of the dementor intervention-ladder fingerprint persistence study (AAAI 2026 conference).

Quellehuggingface.co/dementor-research/dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1/tree/0bf04a35faff98fbe4e2f9e83652cbe976da0117

ArtLoRABasismodellQwen/Qwen3-32BVersionadapter_modelVerleger@dementor-researchCBewertung

Modellquelle

Art: LoRA
Dateigröße: 1,02 GB
Basismodell: Qwen/Qwen3-32B
Version: vadapter_model
Aktualisiert: 3. Okt. 2026
Verleger: @dementor-research
Downloads: 4
Parameter: 32B
Quelle: Hugging Face

Quellenbeschreibung

dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1

LoRA adapter trained via Tinker as part of the dementor intervention-ladder fingerprint persistence study (AAAI 2026 conference).

Base model: Qwen/Qwen3-32B
Training stage: DPO (LoRA rank 32, target_modules=all-linear)
Alias: dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-32B")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-32B")
model = PeftModel.from_pretrained(base, "dementor-research/dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1")

Part of the dementor matrix: 4 source models × 3 cross-targets × 3 train datasets × 3 seeds × 2 stages = 216 adapters.

Quellen

1 Quelle

Hugging FacePrimärVersion 0bf04a35faff98fbe4e2f9e83652cbe976da0117

huggingface.co/dementor-research/dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1/tree/0bf04a35faff98fbe4e2f9e83652cbe976da0117

Verifiziert 3. Okt.

Modellartefakte

1 Artefakt

adapter_model.safetensors

safetensors · 1,02 GB · SHA-256 77cc691b76c3…b38b · Hugging Face

Herunterladen

Quellenauszüge

3 Auszüge

Readme11. Julihuggingface.co/dementor-research/dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1

--- base_model: Qwen/Qwen3-32B library_name: peft tags: - lora - dpo - dementor-research --- # dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1 LoRA adapter trained via [Tinker](https://thinkingmachines.ai/tinker/) as part of the **dementor** intervention-ladder fingerprint persistence study (AAAI 2026 conference). - **Base model:** `Qwen/Qwen3-32B` - **Training stage:** DPO (LoRA rank 32, target_modules=all-linear) - **Alias:** `dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1` ## Usage ```python from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-32B") tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-32B") model = PeftModel.from_pretrained(base, "dementor-research/dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1") ``` Part of the dementor matrix: 4 source models × 3 cross-targets × 3 train datasets × 3 seeds × 2 stages = 216 adapters.

Note3. Okt.huggingface.co/dementor-research/dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1

Source context: 4 downloads · 0 likes · Library peft · Repo dementor-research/dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1

Note17. Julihuggingface.co/dementor-research/dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1

Source context: 3 downloads · 0 likes · Library peft · Repo dementor-research/dpo_writingprompts_qwen3-32b_as_nemotron-nano-30b-a3b_seed1