File size: 4,411 Bytes
2e7e617 71d7a4f 2e7e617 71d7a4f 2e7e617 71d7a4f 2e7e617 71d7a4f 2e7e617 71d7a4f 23992e1 71d7a4f 23992e1 71d7a4f 2e7e617 71d7a4f |
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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- Locutusque/StockQwen-2.5-7B
- allknowingroger/QwenSlerp8-7B
language:
- en
- zh
base_model:
- allknowingroger/QwenSlerp8-7B
- Locutusque/StockQwen-2.5-7B
library_name: transformers
---
# ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B
**Qwen-2.5-Aether-SlerpFusion-7B** is a sophisticated model merge that combines the strengths of multiple pre-trained language models using the powerful [mergekit](https://github.com/ZeroXClem/mergekit) framework. This fusion leverages spherical linear interpolation (SLERP) to seamlessly blend architectural layers, resulting in a model that benefits from enhanced performance and versatility.
## 🚀 Merged Models
This model merge incorporates the following:
- [**Locutusque/StockQwen-2.5-7B**](https://huggingface.co/Locutusque/StockQwen-2.5-7B): Serves as the foundational model, renowned for its robust language understanding and generation capabilities.
- [**allknowingroger/QwenSlerp8-7B**](https://huggingface.co/allknowingroger/QwenSlerp8-7B): Contributes advanced task-specific fine-tuning, enhancing the model's adaptability across various applications.
## 🧩 Merge Configuration
The configuration below outlines how the models are merged using **spherical linear interpolation (SLERP)**. This method ensures smooth transitions between the layers of both models, facilitating an optimal blend of their unique attributes:
```yaml
# ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B Merge Configuration
slices:
- sources:
- model: Locutusque/StockQwen-2.5-7B
layer_range: [0, 28]
- model: allknowingroger/QwenSlerp8-7B
layer_range: [0, 28]
merge_method: slerp
base_model: Locutusque/StockQwen-2.5-7B
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
```
### 🔑 Key Parameters
- **Self-Attention Filtering** (`self_attn`): Controls the blending extent across self-attention layers, allowing for a dynamic mix between the two source models.
- **MLP Filtering** (`mlp`): Adjusts the balance within the Multi-Layer Perceptrons, fine-tuning the model’s neural network layers for optimal performance.
- **Global Weight (`t.value`)**: Sets a general interpolation factor for all unspecified layers, ensuring an equal contribution from both models.
- **Data Type (`dtype`)**: Utilizes `bfloat16` to maintain computational efficiency while preserving high precision.
### 🗣️ Inference
Below is an example of how to load and use the model for text generation:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
# Define the model name
model_name = "ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B"
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Load the model
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Initialize the pipeline
text_generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Define the input prompt
prompt = "Explain the significance of artificial intelligence in modern healthcare."
# Generate the output
outputs = text_generator(
prompt,
max_new_tokens=150,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.95
)
# Print the generated text
print(outputs[0]["generated_text"])
```
## 🎯 Use Case & Applications
**Qwen-2.5-Aether-SlerpFusion-7B** excels in scenarios that require both robust language understanding and specialized task performance. This merged model is ideal for:
- **Advanced Text Generation and Comprehension**: Crafting coherent, contextually accurate, and nuanced text for applications like content creation, summarization, and translation.
- **Domain-Specific Tasks**: Enhancing performance in specialized areas such as legal document analysis, medical information processing, and technical support.
- **Interactive AI Systems**: Powering conversational agents and chatbots that require both general language capabilities and task-specific expertise.
## 📜 License
This model is open-sourced under the **Apache-2.0 License**.
## 💡 Tags
- `merge`
- `mergekit`
- `slerp`
- `Qwen`
- `Locutusque/StockQwen-2.5-7B`
- `allknowingroger/QwenSlerp8-7B`
--- |