71 lines
1.8 KiB
Markdown
71 lines
1.8 KiB
Markdown
---
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tags:
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- merge
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- mergekit
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- lazymergekit
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- shadowml/WestBeagle-7B
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- shadowml/Beaglake-7B
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- mlabonne/NeuralOmniBeagle-7B
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base_model:
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- shadowml/WestBeagle-7B
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- shadowml/Beaglake-7B
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- mlabonne/NeuralOmniBeagle-7B
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license: cc-by-nc-4.0
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---
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# NeuralOmniWestBeaglake-7B
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NeuralOmniWestBeaglake-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [shadowml/WestBeagle-7B](https://huggingface.co/shadowml/WestBeagle-7B)
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* [shadowml/Beaglake-7B](https://huggingface.co/shadowml/Beaglake-7B)
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* [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B)
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## 🧩 Configuration
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```yaml
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models:
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- model: mistralai/Mistral-7B-v0.1
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# no parameters necessary for base model
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- model: shadowml/WestBeagle-7B
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parameters:
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density: 0.65
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weight: 0.4
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- model: shadowml/Beaglake-7B
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parameters:
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density: 0.6
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weight: 0.35
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- model: mlabonne/NeuralOmniBeagle-7B
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parameters:
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density: 0.6
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weight: 0.45
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merge_method: dare_ties
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base_model: mistralai/Mistral-7B-v0.1
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parameters:
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int8_mask: true
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dtype: float16
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```
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "paulml/NeuralOmniWestBeaglake-7B"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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``` |