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iaravagni/deepseek-uncensored

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Response includes
  • classification m-uncensored
  • files 8
  • author_summary 3 models
  • readme_text full
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
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Model age
19mo ago
created 2025-03-15
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Metadata

Tags
transformers safetensors generated_from_trainer unsloth trl sft endpoints_compatible region:us

Related

Total size
160 MB
Files
8
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-03-25 18:40

Files by quantization

Auxiliary files 8 files 177 MB
adapter_model.safetensors 160 MB 6a458b47 download
training_args.bin 5.55 KB b9c539bc download
tokenizer.json 16.4 MB 921372d2 download
tokenizer_config.json 51.7 KB 592b1064 download
README.md 1.81 KB a2f4d67c download
.gitattributes 1.53 KB 52373fe2 download
adapter_config.json 824 B 19d6439d download
special_tokens_map.json 483 B afcf8b83 download

README current version from Hugging Face


base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
library_name: transformers
model_name: deepseek-uncensored
tags:

  • generated_from_trainer
  • unsloth
  • trl
  • sft
    licence: license

Model Card for deepseek-uncensored

This model is a fine-tuned version of unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit.
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="iaravagni/deepseek-uncensored", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

Visualize in Weights & Biases

This model was trained with SFT.

Framework versions

  • TRL: 0.15.2
  • Transformers: 4.50.0
  • Pytorch: 2.6.0+cu124
  • Datasets: 3.4.1
  • Tokenizers: 0.21.1

Citations

Cite TRL as:

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}

README history 4 versions

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

  1. 2025-03-25iaravagni/deepseek-uncensored2e808ec01.8 KB
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  2. 2025-03-15Model save7c86dc51.8 KB
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  3. 2025-03-15Model saveabc7b0d1.5 KB
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  4. 2025-03-15Model savee2159bb1.8 KB
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