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---
base_model:
- DewEfresh/neo_7b
- DewEfresh/neo_7b
tags:
- merge
- mergekit
- lazymergekit
- DewEfresh/neo_7b
---
# Neo_7b-merge14
Neo_7b-merge14 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b)
* [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b)
## 🧩 Configuration
```yaml
# Define the slices for the model merging process
slices:
- sources:
# First part: merge layer 0 with layer 3
- model: DewEfresh/neo_7b
layer_range: [0, 0]
- model: DewEfresh/neo_7b
layer_range: [3, 3]
- sources:
# Second part: merge layer 1 with layer 3
- model: DewEfresh/neo_7b
layer_range: [1, 1]
- model: DewEfresh/neo_7b
layer_range: [3, 3]
- sources:
# Third part: merge layer 2 with layer 3
- model: DewEfresh/neo_7b
layer_range: [2, 2]
- model: DewEfresh/neo_7b
layer_range: [3, 3]
- sources:
# Fourth part: merge layer 4 with layer 7
- model: DewEfresh/neo_7b
layer_range: [4, 4]
- model: DewEfresh/neo_7b
layer_range: [7, 7]
- sources:
# Fifth part: merge layer 5 with layer 7
- model: DewEfresh/neo_7b
layer_range: [5, 5]
- model: DewEfresh/neo_7b
layer_range: [7, 7]
- sources:
# Sixth part: merge layer 6 with layer 7
- model: DewEfresh/neo_7b
layer_range: [6, 6]
- model: DewEfresh/neo_7b
layer_range: [7, 7]
- sources:
# Seventh part: merge layer 8 with layer 11
- model: DewEfresh/neo_7b
layer_range: [8, 8]
- model: DewEfresh/neo_7b
layer_range: [11, 11]
- sources:
# Eighth part: merge layer 9 with layer 11
- model: DewEfresh/neo_7b
layer_range: [9, 9]
- model: DewEfresh/neo_7b
layer_range: [11, 11]
- sources:
# Ninth part: merge layer 10 with layer 11
- model: DewEfresh/neo_7b
layer_range: [10, 10]
- model: DewEfresh/neo_7b
layer_range: [11, 11]
- sources:
# Tenth part: merge layer 12 with layer 15
- model: DewEfresh/neo_7b
layer_range: [12, 12]
- model: DewEfresh/neo_7b
layer_range: [15, 15]
- sources:
# Eleventh part: merge layer 13 with layer 15
- model: DewEfresh/neo_7b
layer_range: [13, 13]
- model: DewEfresh/neo_7b
layer_range: [15, 15]
- sources:
# Twelfth part: merge layer 14 with layer 15
- model: DewEfresh/neo_7b
layer_range: [14, 14]
- model: DewEfresh/neo_7b
layer_range: [15, 15]
- sources:
# Thirteenth part: merge layer 16 with layer 19
- model: DewEfresh/neo_7b
layer_range: [16, 16]
- model: DewEfresh/neo_7b
layer_range: [19, 19]
- sources:
# Fourteenth part: merge layer 17 with layer 19
- model: DewEfresh/neo_7b
layer_range: [17, 17]
- model: DewEfresh/neo_7b
layer_range: [19, 19]
- sources:
# Fifteenth part: merge layer 18 with layer 19
- model: DewEfresh/neo_7b
layer_range: [18, 18]
- model: DewEfresh/neo_7b
layer_range: [19, 19]
- sources:
# Sixteenth part: merge layer 20 with layer 23
- model: DewEfresh/neo_7b
layer_range: [20, 20]
- model: DewEfresh/neo_7b
layer_range: [23, 23]
- sources:
# Seventeenth part: merge layer 21 with layer 23
- model: DewEfresh/neo_7b
layer_range: [21, 21]
- model: DewEfresh/neo_7b
layer_range: [23, 23]
- sources:
# Eighteenth part: merge layer 22 with layer 23
- model: DewEfresh/neo_7b
layer_range: [22, 22]
- model: DewEfresh/neo_7b
layer_range: [23, 23]
- sources:
# Nineteenth part: merge layer 24 with layer 27
- model: DewEfresh/neo_7b
layer_range: [24, 24]
- model: DewEfresh/neo_7b
layer_range: [27, 27]
- sources:
# Twentieth part: merge layer 25 with layer 27
- model: DewEfresh/neo_7b
layer_range: [25, 25]
- model: DewEfresh/neo_7b
layer_range: [27, 27]
- sources:
# Twenty-first part: merge layer 26 with layer 27
- model: DewEfresh/neo_7b
layer_range: [26, 26]
- model: DewEfresh/neo_7b
layer_range: [27, 27]
# Specify the merging method for the slices
merge_method: slerp
base_model: DewEfresh/neo_7b
parameters:
t: 0.3333 # Set global interpolation value to 33.33%
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "DewEfresh/Neo_7b-merge14"
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"])
``` |