--- tags: - merge - mergekit - moe - frankenmoe - abacusai/Llama-3-Smaug-8B - cognitivecomputations/dolphin-2.9-llama3-8b - Weyaxi/Einstein-v6.1-Llama3-8B - dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2 base_model: - abacusai/Llama-3-Smaug-8B - cognitivecomputations/dolphin-2.9-llama3-8b - Weyaxi/Einstein-v6.1-Llama3-8B - dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2 --- # Skyro-4X8B Skyro-4X8B is a Mixure of Experts (MoE) made with the following models using [Mergekit](https://github.com/arcee-ai/mergekit): * [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B) * [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) * [Weyaxi/Einstein-v6.1-Llama3-8B](https://huggingface.co/Weyaxi/Einstein-v6.1-Llama3-8B) * [dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2](https://huggingface.co/dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2) ## 🧩 Configuration ```yamlname: "Skyro-4X8B" base_model: meta-llama/Meta-Llama-3-8B gate_mode: hidden experts: - source_model: abacusai/Llama-3-Smaug-8B positive_prompts: - "chat" - "assistant" - "tell me" - "explain" - "I want" - source_model: cognitivecomputations/dolphin-2.9-llama3-8b positive_prompts: - "math" - "mathematics" - "code" - "engineering" - "solve" - "logic" - "rationality" - "puzzle" - "solve" - source_model: Weyaxi/Einstein-v6.1-Llama3-8B positive_prompts: - "science" - "medical" - "physics" - "engineering" - "math" - "logic" - "rationality" - "mathematics" - "solve" - source_model: dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2 positive_prompts: - "story" - "roleplay" - "role-play" - "storywriting" - "character" - "narrative" - "creative" ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "saucam/Skyro-4X8B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```