transformers safetensors mistral text-generation merge mergekit lazymergekit CultriX/MonaTrix-v4 bardsai/jaskier-7b-dpo-v5.6 eren23/ogno-monarch-jaskier-merge-7b conversational base_model:CultriX/MonaTrix-v4 base_model:bardsai/jaskier-7b-dpo-v5.6 base_model:eren23/ogno-monarch-jaskier-merge-7b autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- merge
- mergekit
- lazymergekit
- CultriX/MonaTrix-v4
- bardsai/jaskier-7b-dpo-v5.6
- eren23/ogno-monarch-jaskier-merge-7b
base_model:
- CultriX/MonaTrix-v4
- bardsai/jaskier-7b-dpo-v5.6
- eren23/ogno-monarch-jaskier-merge-7b
---

# DominaTrix-7B-v2

DominaTrix-7B-v2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [CultriX/MonaTrix-v4](https://huggingface.co/CultriX/MonaTrix-v4)
* [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6)
* [eren23/ogno-monarch-jaskier-merge-7b](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b)

## 🧩 Configuration

```yaml
models:
  - model: mistralai/Mistral-7B-Instruct-v0.2
    # No parameters necessary for base model
  - model: CultriX/MonaTrix-v4
    #Emphasize the beginning of Vicuna format models
    parameters:
      weight: 0.36
      density: 0.65
  - model: bardsai/jaskier-7b-dpo-v5.6
    parameters:
      weight: 0.34
      density: 0.6
  # Vicuna format
  - model: eren23/ogno-monarch-jaskier-merge-7b
    parameters:
      weight: 0.3
      density: 0.6
merge_method: dare_ties
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "CultriX/DominaTrix-7B-v2"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```