78 lines
2.4 KiB
Markdown
78 lines
2.4 KiB
Markdown
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---
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tags:
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- merge
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- mergekit
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- lazymergekit
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- NousResearch/Genstruct-7B
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- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
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base_model:
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- NousResearch/Genstruct-7B
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- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
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---
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# GenStructDolphin-7B-Slerp
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GenStructDolphin-7B-Slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-7B)
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* [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
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## 🧩 Configuration
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```yaml
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slices:
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- sources:
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- model: NousResearch/Genstruct-7B
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layer_range: [0, 32]
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- model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
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layer_range: [0, 32]
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merge_method: slerp
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base_model: NousResearch/Genstruct-7B
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---
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### 🌐 Website
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You can find more of my models, projects, and information on my official website:
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- **[artificialguy.com](https://artificialguy.com/)**
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### 🚀 Prompt Hub
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Need high-quality prompts for image models and LLMs? Explore **[findgoodprompt.com](https://findgoodprompt.com)**.
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### 💖 Support My Work
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If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
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- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
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- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
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- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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dtype: bfloat16
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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 = "artificialguybr/GenStructDolphin-7B-Slerp"
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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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```
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