README.md

---
tags:
- merge
- mergekit
- lazymergekit
- VAGOsolutions/SauerkrautLM-7b-HerO
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
base_model:
- VAGOsolutions/SauerkrautLM-7b-HerO
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
---

# Sappho_V0.0.2

Sappho_V0.0.2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [VAGOsolutions/SauerkrautLM-7b-HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
* [cognitivecomputations/dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02)

## 🧩 Configuration

```yaml
models:
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B # no parameters necessary for base model
  - model: VAGOsolutions/SauerkrautLM-7b-HerO
    parameters:
      density: 0.3  # fraction of weights in differences from the base model to retain
      weight:   # weight gradient
        - filter: mlp
          value: 0.5
        - value: 0
  - model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
    parameters:
      density: 0.5
      weight: 0.4
merge_method: ties
base_model: mlabonne/NeuralHermes-2.5-Mistral-7B
parameters:
  normalize: true
  int8_mask: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Jakolo121/Sappho_V0.0.2"
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"])
```