File size: 2,067 Bytes
018ea24 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
---
tags:
- merge
- mergekit
- lazymergekit
- Or4cl3-1/cognitive-agent-xtts-optimized
- Or4cl3-1/multimodal-fusion-optimized
base_model:
- Or4cl3-1/cognitive-agent-xtts-optimized
- Or4cl3-1/multimodal-fusion-optimized
---
# CogniFusion-XTTS-slerp
CogniFusion-XTTS-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Or4cl3-1/cognitive-agent-xtts-optimized](https://huggingface.co/Or4cl3-1/cognitive-agent-xtts-optimized)
* [Or4cl3-1/multimodal-fusion-optimized](https://huggingface.co/Or4cl3-1/multimodal-fusion-optimized)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Or4cl3-1/cognitive-agent-xtts-optimized
layer_range: [0, 32] # Specify appropriate layer range for cognitive agent
- model: Or4cl3-1/multimodal-fusion-optimized
layer_range: [0, 32] # Specify appropriate layer range for multimodal fusion
merge_method: slerp
base_model: Or4cl3-1/cognitive-agent-xtts-optimized
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1] # Fine-tune self-attention parameters
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0] # Adjust MLP parameters for optimal fusion
- value: 0.5 # Set overall fusion parameter value
dtype: bfloat16
# Add ethical considerations and any additional optimization parameters here
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Or4cl3-1/CogniFusion-XTTS-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"])
``` |