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README.md
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---
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-0.5B-Instruct
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- Qwen/Qwen2.5-Coder-0.5B
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- funnyPhani/Qwen-2.5-0.5B-MATH
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- caelancooper/Qwen2.5-0.5B-business
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- KingNish/Qwen2.5-0.5b-Test-ft
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tags:
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- moe
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- frankenmoe
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- merge
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- mergekit
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- lazymergekit
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- Qwen/Qwen2.5-0.5B-Instruct
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- Qwen/Qwen2.5-Coder-0.5B
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- funnyPhani/Qwen-2.5-0.5B-MATH
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- caelancooper/Qwen2.5-0.5B-business
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- KingNish/Qwen2.5-0.5b-Test-ft
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---
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# LazyMergekit-Qwen2.5-0.5B-Mixtral
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LazyMergekit-Qwen2.5-0.5B-Mixtral is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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* [Qwen/Qwen2.5-Coder-0.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B)
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* [funnyPhani/Qwen-2.5-0.5B-MATH](https://huggingface.co/funnyPhani/Qwen-2.5-0.5B-MATH)
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* [caelancooper/Qwen2.5-0.5B-business](https://huggingface.co/caelancooper/Qwen2.5-0.5B-business)
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* [KingNish/Qwen2.5-0.5b-Test-ft](https://huggingface.co/KingNish/Qwen2.5-0.5b-Test-ft)
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## 🧩 Configuration
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```yaml
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base_model: Qwen/Qwen2.5-0.5B-Instruct # Base model for shared layers
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gate_mode: hidden # Use hidden representations for router initialization
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dtype: float16 # Data type for the merged model
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experts:
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- source_model: Qwen/Qwen2.5-0.5B-Instruct
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positive_prompts:
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- "chat"
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- "assistant"
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- "tell me"
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- "explain"
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- "I want"
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- source_model: Qwen/Qwen2.5-Coder-0.5B
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positive_prompts:
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- "code"
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- "python"
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- "javascript"
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- "programming"
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- "algorithm"
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- source_model: funnyPhani/Qwen-2.5-0.5B-MATH
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positive_prompts:
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- "math"
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- "mathematics"
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- "solve"
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- "count"
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- "reason"
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- source_model: caelancooper/Qwen2.5-0.5B-business
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positive_prompts:
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- "business"
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- "finance"
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- "market"
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- "strategy"
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- "analysis"
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- source_model: KingNish/Qwen2.5-0.5b-Test-ft
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positive_prompts:
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```
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## 💻 Usage
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```python
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!pip install -qU transformers bitsandbytes 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 = "Xiaojian9992024/LazyMergekit-Qwen2.5-0.5B-Mixtral"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
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)
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messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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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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