---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- ray0rf1re/Nix2.5
- ray0rf1re/Nix1.5
---

# Nix2.5-plus
i still recomend normal Nix2.5

## Model Description

This is a merged model, `Nix2.5-plus`, created using `mergekit`'s `slerp` (Spherical Linear Interpolation) method. It combines the strengths of `ray0rf1re/Nix2.5` and `ray0rf1re/Nix1.5` to potentially offer improved performance or a different balance of capabilities.

## Merge Details

`Nix2.5-plus` is a merge of the following models using the `slerp` merge method from [mergekit](https://github.com/cg123/mergekit):
* [ray0rf1re/Nix2.5](https://huggingface.co/ray0rf1re/Nix2.5)
* [ray0rf1re/Nix1.5](https://huggingface.co/ray0rf1re/Nix1.5)

The merge was performed with a specific `t` parameter of `0.275`. This signifies a weighted combination where `ray0rf1re/Nix1.5` contributes approximately 27.5% and `ray0rf1re/Nix2.5` contributes approximately 72.5% to the final merged model's characteristics. `ray0rf1re/Nix2.5` was used as the base model for this slerp merge.

## ⚙  Configuration

```yaml
slices:
  - sources:
      - model: ray0rf1re/Nix2.5
        layer_range: [0, 32]
      - model: ray0rf1re/Nix1.5
        layer_range: [0, 32]
merge_method: slerp
base_model: ray0rf1re/Nix2.5
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.275
dtype: bfloat16

```

## Usage

To use this model, you can load it with the `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ray0rf1re/Nix2.5-plus"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage (adjust as needed)
input_text = "Hello, my name is"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
output = model.generate(input_ids, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Training Data

This merged model leverages the training data of its constituent models: `ray0rf1re/Nix2.5` and `ray0rf1re/Nix1.5`. Please refer to the respective model cards for details on their training datasets.

## Limitations

As a merged model, its performance and biases are inherited from its base models. Thorough evaluation is recommended for specific use cases. Merged models may sometimes exhibit unexpected behaviors or a degradation in certain tasks compared to their individual components.