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--- |
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license: apache-2.0 |
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tags: |
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- moe |
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- merge |
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- mergekit |
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- lazymergekit |
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- mlabonne/NeuralBeagle14-7B |
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- AdaptLLM/finance-chat |
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- AdaptLLM/medicine-chat |
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- AdaptLLM/law-chat |
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--- |
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# AdaptLLM-4x7B-MoE |
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AdaptLLM-4x7B-MoE is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
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* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) |
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* [AdaptLLM/finance-chat](https://huggingface.co/AdaptLLM/finance-chat) |
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* [AdaptLLM/medicine-chat](https://huggingface.co/AdaptLLM/medicine-chat) |
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* [AdaptLLM/law-chat](https://huggingface.co/AdaptLLM/law-chat) |
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## 🧩 Configuration |
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```yaml |
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base_model: mlabonne/NeuralBeagle14-7B |
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experts: |
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- source_model: mlabonne/NeuralBeagle14-7B |
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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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- "storywriting" |
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- "write" |
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- "scene" |
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- "story" |
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- "character" |
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- "instruct" |
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- "summarize" |
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- "count" |
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- source_model: AdaptLLM/finance-chat |
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positive_prompts: |
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- "personal finance" |
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- "budgeting" |
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- "investing" |
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- "retirement planning" |
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- "debt management" |
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- "financial education" |
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- "consumer protection" |
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- "financial" |
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- "money" |
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- "investment" |
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- "banking" |
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- "stock" |
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- "bond" |
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- "portfolio" |
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- "risk" |
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- "return" |
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- source_model: AdaptLLM/medicine-chat |
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positive_prompts: |
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- "diagnose" |
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- "treat" |
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- "disease" |
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- "symptom" |
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- "medication" |
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- "anatomy" |
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- "physiology" |
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- "pharmacology" |
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- "clinical trial" |
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- "medical research" |
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- source_model: AdaptLLM/law-chat |
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positive_prompts: |
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- "law" |
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- "legal" |
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- "attorney" |
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- "lawyer" |
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- "court" |
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- "contract" |
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- "criminal" |
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- "evidence" |
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- "procedure" |
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- "contracts" |
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- "mergers & acquisitions" |
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- "corporate governance" |
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- "intellectual property" |
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- "employment law" |
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- "international trade" |
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- "competition law" |
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- "antitrust" |
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- "litigation" |
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- "arbitration" |
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- "mediation" |
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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 = "Isotonic/AdaptLLM-4x7B-MoE" |
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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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``` |
