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---
tags:
- merge
- mergekit
- lazymergekit
- nbeerbower/bophades-mistral-truthy-DPO-7B
- mlabonne/NeuralMonarch-7B
- unsloth
base_model:
- nbeerbower/bophades-mistral-truthy-DPO-7B
- mlabonne/NeuralMonarch-7B
license: apache-2.0
pipeline_tag: text-generation
---
# Boptruth-NeuralMonarch-7B
Boptruth-NeuralMonarch-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [nbeerbower/bophades-mistral-truthy-DPO-7B](https://huggingface.co/nbeerbower/bophades-mistral-truthy-DPO-7B)
* [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B)
## 🚨 Use the alpaca prompt format
If you use standard ChatML, you may end up with <|im_end|> tokens at the end of responses.
## 👀 Looking for GGUF?
Find quantized versions of this model [right here](https://huggingface.co/mradermacher/Boptruth-NeuralMonarch-7B-GGUF).
## 🧩 Configuration
```yaml
slices:
- sources:
- model: nbeerbower/bophades-mistral-truthy-DPO-7B
layer_range: [0, 32]
- model: mlabonne/NeuralMonarch-7B
layer_range: [0, 32]
merge_method: slerp
base_model: nbeerbower/bophades-mistral-truthy-DPO-7B
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: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "theprint/Boptruth-NeuralMonarch-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"])
``` |