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---
tags:
- merge
- mergekit
- lazymergekit
- paulml/OGNO-7B
- AiMavenAi/AiMaven-Prometheus
base_model:
- paulml/OGNO-7B
- AiMavenAi/AiMaven-Prometheus
---
# InnerI-AI-merge-7B-slerp
InnerI-AI-merge-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [paulml/OGNO-7B](https://huggingface.co/paulml/OGNO-7B)
* [AiMavenAi/AiMaven-Prometheus](https://huggingface.co/AiMavenAi/AiMaven-Prometheus)
# Avg model loss 1.0132372907549143
I used this testing script that loads your local model, pulls the latest data from cortex and calculates the loss:
[avg loss script](https://gist.github.com/romanorac/59ccde7cbf07d8950ef9fb5b5db6a24e)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: paulml/OGNO-7B
layer_range: [0, 32]
- model: AiMavenAi/AiMaven-Prometheus
layer_range: [0, 32]
merge_method: slerp
base_model: paulml/OGNO-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 = "InnerI/InnerI-AI-merge-7B-slerp"
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"])
``` |