Create README.md
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README.md
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# Lumosia-v2-MoE-4x10.7
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"Lumosia" was selected as its a MoE of Multiple SOLAR Merges so it really "Lights the way".... its 3am.
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This is a very experimantal model. its a MoE of all good performing Solar models (based off of personal experiance not open leaderboard),
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The models goal was to make a good all rounder, in chat/logic/rp
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Why? Dunno whated to see what would happen
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context is 4k but coherent up to 16k
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A Lumosia Personality tavern card has been added
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Come join the Discord:
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[ConvexAI](https://discord.gg/yYqmNmg7Wj)
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Template:
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```
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### System:
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### USER:{prompt}
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### Assistant:
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```
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Settings:
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```
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Temp: 1.0
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min-p: 0.02-0.1
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```
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## Evals:
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* Avg:
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* ARC:
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* HellaSwag:
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* MMLU:
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* T-QA:
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* Winogrande:
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* GSM8K:
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## Examples:
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```
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Example 1:
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User:
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Lumosia:
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```
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```
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Example 2:
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User:
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Lumosia:
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```
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## 🧩 Configuration
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```
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yaml
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base_model: DopeorNope/SOLARC-M-10.7B
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gate_mode: hidden
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dtype: bfloat16
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experts:
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- source_model: DopeorNope/SOLARC-M-10.7B
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positive_prompts: [""]
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- source_model: maywell/PiVoT-10.7B-Mistral-v0.2-RP
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positive_prompts: [""]
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- source_model: kyujinpy/Sakura-SOLAR-Instruct
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positive_prompts: [""]
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- source_model: jeonsworld/CarbonVillain-en-10.7B-v1
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positive_prompts: [""]
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```
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## 💻 Usage
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```
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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 = "Steelskull/Lumosia-MoE-4x10.7"
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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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