---
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

<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](https://huggingface.co/datasets/bigcode/starcoderdata?utm_source=chatgpt.com))

#### 5. **General, Deduplicated Web Data**

* **cerebras/SlimPajama-627B**: Offers a massive, deduplicated dataset for solid and diverse language foundation training. ([cerebras.ai](https://cerebras.ai/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama?utm_source=chatgpt.com))

#### 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](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct](https://huggingface.co/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](https://huggingface.co/Danielbrdz/Barcenas-Tiny-1.1b-DPO)
* [MysteriousAI/Mia-1B](https://huggingface.co/MysteriousAI/Mia-1B)
* [cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser](https://huggingface.co/cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser)
* [mrcuddle/tiny-darkllama-dpo](https://huggingface.co/mrcuddle/tiny-darkllama-dpo)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
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]

```