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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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- frankenmoe |
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- merge |
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- mergekit |
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- lazymergekit |
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- indischepartij/MiniCPM-3B-Hercules-v2.0 |
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- indischepartij/MiniCPM-3B-OpenHermes-2.5-v2 |
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- indischepartij/MiniCPM-3B-Bacchus |
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base_model: |
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- indischepartij/MiniCPM-3B-Hercules-v2.0 |
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- indischepartij/MiniCPM-3B-OpenHermes-2.5-v2 |
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- indischepartij/MiniCPM-3B-Bacchus |
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--- |
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# MaxiCPM-3x3B-Test |
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MaxiCPM-3x3B-Test 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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* [indischepartij/MiniCPM-3B-Hercules-v2.0](https://huggingface.co/indischepartij/MiniCPM-3B-Hercules-v2.0) |
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* [indischepartij/MiniCPM-3B-OpenHermes-2.5-v2](https://huggingface.co/indischepartij/MiniCPM-3B-OpenHermes-2.5-v2) |
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* [indischepartij/MiniCPM-3B-Bacchus](https://huggingface.co/indischepartij/MiniCPM-3B-Bacchus) |
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## 🧩 Configuration |
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```yaml |
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base_model: openbmb/MiniCPM-2B-dpo-bf16-llama-format |
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experts: |
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- source_model: indischepartij/MiniCPM-3B-Hercules-v2.0 |
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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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- source_model: indischepartij/MiniCPM-3B-OpenHermes-2.5-v2 |
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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: indischepartij/MiniCPM-3B-Bacchus |
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positive_prompts: |
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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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- "reason" |
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- "math" |
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- "mathematics" |
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- "solve" |
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- "count" |
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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 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 = "gmonsoon/MaxiCPM-3x3B-Test" |
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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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``` |
