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TinyKiller-NSFW-DPO-1.1B-Q5_K_M-GGUF

Transformers
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uncensored
abliterated
roleplay
rp
nsfw
1b
4-bit precision
tinyllama
conversational
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metadata
base_model:
  - Danielbrdz/Barcenas-Tiny-1.1b-DPO
  - MysteriousAI/Mia-1B
  - mrcuddle/tiny-darkllama-dpo
  - Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct
  - cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
library_name: transformers
tags:
  - uncensored
  - abliterated
  - roleplay
  - rp
  - nsfw
  - 1b
  - 4-bit
  - tinyllama
license: apache-2.0
language:
  - es
  - en
datasets:
  - Intel/orca_dpo_pairs
  - OEvortex/vortex-mini
  - jondurbin/airoboros-3.2
  - LDJnr/Capybara
  - unalignment/toxic-dpo-v0.1
  - LDJnr/Verified-Camel
  - HuggingFaceH4/no_robots
  - Doctor-Shotgun/no-robots-sharegpt
  - Doctor-Shotgun/capybara-sharegpt
  - cerebras/SlimPajama-627B
  - bigcode/starcoderdata
  - teknium/openhermes

TinyKiller NSFW DPO 1.1B

IMG-20250506-210550

Merge Details

TinyKiller-1.1B is a language model based on the TinyLlama-1.1B architecture, designed to deliver exceptional performance in text generation, reasoning, and programming tasks. This model has been fine-tuned using a combination of diverse and high-quality datasets, allowing it to excel across multiple domains.

🧠 Core Capabilities of TinyKiller-1.1B

1. Conversational Alignment and Reasoning

  • Intel/orca_dpo_pairs: Provides data pairs for preference-based optimization training, improving the quality of generated responses.
  • LDJnr/Verified-Camel: Offers verified conversations to strengthen coherence and accuracy in dialogue.
  • HuggingFaceH4/no_robots and Doctor-Shotgun/no-robots-sharegpt: Datasets designed to prevent robotic-sounding replies, encouraging more natural, human-like interactions.

2. Toxicity Resistance

  • unalignment/toxic-dpo-v0.1: Includes data contained within is "toxic"/"harmful", and contains profanity and other types of sensitive content.

3. Instruction Following and Complex Reasoning

  • jondurbin/airoboros-3.2: Challenges the model with complex, multi-step tasks, enhancing its instruction-following and reasoning skills.
  • LDJnr/Capybara and Doctor-Shotgun/capybara-sharegpt: Provide diverse, high-quality instruction datasets to strengthen task comprehension and execution.

4. Programming and Code Understanding

  • bigcode/starcoderdata: Contains a broad collection of code across multiple languages, GitHub issues, and Jupyter notebooks, enabling effective code understanding and generation. (huggingface.co)

5. General, Deduplicated Web Data

  • cerebras/SlimPajama-627B: Offers a massive, deduplicated dataset for solid and diverse language foundation training. (cerebras.ai)

6. Compact and Efficient Instruction Tuning

  • OEvortex/vortex-mini: Provides instruction-tuned data optimized for small models, enhancing task efficiency and performance.

⚙️ Key Use Cases

  • Virtual Assistants: With training from datasets like no_robots and airoboros, TinyKiller-1.1B can hold natural and coherent conversations.
  • Content Moderation: Thanks to toxic-dpo-v0.1, the model can detect and manage harmful or inappropriate content.
  • Code Generation & Understanding: Training with starcoderdata allows the model to assist in programming and code analysis.
  • Education & Tutoring: Its ability to follow detailed instructions makes it suitable for educational applications and personalized tutoring.

🧩 Conclusion

TinyKiller-1.1B represents a carefully crafted integration of multiple high-quality datasets, enabling robust performance across a wide range of natural language processing tasks. Its balanced design—combining conversational ability, toxicity resistance, and code comprehension—makes it a versatile tool for many applications.