Novaciano/TinyKiller-NSFW-DPO-1.1B
TinyKiller NSFW DPO 1.1B
<center><img src="https://i.ibb.co/FZtCxWY/IMG-20250506-210550.jpg" alt="IMG-20250506-210550" border="0"></a></center>
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.
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Model Stock merge method using Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct as a base.
Models Merged
The following models were included in the merge:
- Danielbrdz/Barcenas-Tiny-1.1b-DPO
- MysteriousAI/Mia-1B
- cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
- mrcuddle/tiny-darkllama-dpo
Configuration
The following YAML configuration was used to produce this model:
dtype: float32
out_dtype: bfloat16
merge_method: model_stock
base_model: Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct
models:
- model: Danielbrdz/Barcenas-Tiny-1.1b-DPO
parameters:
weight:
- filter: mlp
value: [2, 1]
- value: 1
- model: cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
parameters:
weight:
- filter: mlp
value: [1, 2]
- value: 1
- model: mrcuddle/tiny-darkllama-dpo
parameters:
weight:
- filter: mlp
value: [1, 2]
- value: 1
- model: MysteriousAI/Mia-1B
parameters:
weight:
- filter: mlp
value: [2, 1]
- value: 1
- model: Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct
parameters:
weight:
- filter: lm_head
value: 0
- value: [1, 0.5]
