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---
base_model:
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
tags:
- merge
- mergekit
- lazymergekit
- Replete-AI/WizardLM-2-7b
---
# BiggerWizardLM-2-7B-Extended
BiggerWizardLM-2-7B-Extended is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 0
- 4
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 3
- 4
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 4
- 8
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 7
- 8
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 8
- 12
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 11
- 12
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 12
- 16
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 15
- 16
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 16
- 20
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 19
- 20
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 20
- 24
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 23
- 24
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 24
- 28
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 27
- 28
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 28
- 32
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 31
- 32
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Gille/BiggerWizardLM-2-7B-Extended"
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"])
```