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edusc182/Qwen2.5-Coder_Web_Creator_Tailwin-Abliteratedv2

edusc182 Qwen 3.1B second-order
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  • files 11
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  • author_summary 3 models
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Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
Downloads · lifetime
86
14 last 30d - stable
Likes
1
Descendants
1
in 1 direct fork
Model age
3mo ago
created 2026-06-27
Downloads over time
Now93→from36↑158%
3355779936 on Jul 193 on Oct 1193 on Oct 7JulAugSepOct
Jul 1 → Oct 11 · 54 snapshots · spans 102 days

Genealogy 1 direct fork

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

Tags
transformers safetensors qwen2 text-generation generated_from_trainer trl dpo conversational arxiv:2305.18290 base_model:huihui-ai/Qwen2.5-Coder-3B-Instruct-abliterated base_model:finetune:huihui-ai/Qwen2.5-Coder-3B-Instruct-abliterated text-generation-inference

Related

Total size
5.76 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-06-27 22:15

Files by quantization

Auxiliary files 11 files 5.77 GB
model.safetensors 5.75 GB 2b961c35 download
adapter_model.safetensors 14.1 MB 931bf82a download
training_args.bin 5.70 KB fef900a3 download
tokenizer.json 10.9 MB 3fd16973 download
README.md 2.46 KB cc4c52c9 download
chat_template.jinja 2.45 KB bdf7919a download
.gitattributes 1.53 KB 52373fe2 download
config.json 1.51 KB badb9112 download
adapter_config.json 1.05 KB ddc3697a download
tokenizer_config.json 690 B 866c19af download
generation_config.json 243 B ded3a63a download

README current version from Hugging Face


base_model: huihui-ai/Qwen2.5-Coder-3B-Instruct-abliterated
library_name: transformers
model_name: Qwen2.5-Coder_Web_Creator_Tailwin-Abliteratedv2
tags:

  • generated_from_trainer
  • trl
  • dpo
    licence: license

Model Card for Qwen2.5-Coder_Web_Creator_Tailwin-Abliteratedv2

This model is a fine-tuned version of huihui-ai/Qwen2.5-Coder-3B-Instruct-abliterated.
It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="edusc182/Qwen2.5-Coder_Web_Creator_Tailwin-Abliteratedv2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Framework versions

  • TRL: 1.7.0
  • Transformers: 5.12.0
  • Pytorch: 2.11.0+cu128
  • Datasets: 5.0.0
  • Tokenizers: 0.22.2

Citations

Cite DPO as:

@inproceedings{rafailov2023direct,
    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
    year         = 2023,
    booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

README history 1 version

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-06-27Training in progress, step 50bc8f6e92.5 KB
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