---
tags:
- merge
- mergekit
- lazymergekit
- mistral
- ResplendentAI/Datura_7B
- Epiculous/Mika-7B
base_model:
- ResplendentAI/Datura_7B
- Epiculous/Mika-7B
language:
- en
library_name: transformers
license: apache-2.0
---
<div style="margin-bottom: -40px; margin-top: -30px;">
    <img src="https://cdn-uploads.huggingface.co/production/uploads/65ad2502043d53781aad2ee4/ZrffOEBz2rxwAwdgzOkhd.png" alt="favicon" style="display: inline-block; vertical-align: middle; width: 25px; height: 25px; margin-right: 10px;">
    <span style="display: inline-block; vertical-align: middle; font-weight: 600; font-size: 20px;">Foxglove_7B</span>
</div>

<img src="https://cdn-uploads.huggingface.co/production/uploads/65ad2502043d53781aad2ee4/FUH__CjalqBRPiSaqZfO6.png" alt="image" width="540" height="540" style="margin-bottom: 10px;">
Foxglove is a well-rounded RP model. It is smart, does a great job of sticking to character card, and is proficient at following desired markdown.

Foxglove_7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [ResplendentAI/Datura_7B](https://huggingface.co/ResplendentAI/Datura_7B)
* [Epiculous/Mika-7B](https://huggingface.co/Epiculous/Mika-7B)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: ResplendentAI/Datura_7B
        layer_range: [0, 32]
      - model: Epiculous/Mika-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: ResplendentAI/Datura_7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.7, 0.4, 0.6, 1]  
    - filter: mlp
      value: [0.8, 0.5, 0.7, 0.3, 0]  
    - value: 0.6  
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "rmdhirr/Foxglove_7B"
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"])
```