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README.md

@@ -0,0 +1,259 @@
+---
+license: apache-2.0
+tags:
+- Solar Moe
+- Solar
+- Lumosia
+pipeline_tag: text-generation
+model-index:
+- name: Lumosia-v2-MoE-4x10.7
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 70.39
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 87.87
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 66.45
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 68.48
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 84.21
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 65.13
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Steelskull/Lumosia-v2-MoE-4x10.7
+      name: Open LLM Leaderboard
+---
+# Lumosia-v2-MoE-4x10.7
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/fKdOLTQNerr2fYYnWOiQD.png)
+
+The Lumosia Series upgraded with Lumosia V2.
+
+# What's New in Lumosia V2?
+
+Lumosia V2 takes the original vision of being an "all-rounder" and refines it with more nuanced capabilities.
+
+Topic/Prompt Based Approach:
+
+Diverging from the keyword-based approach of its counterpart, Umbra.
+
+Context and Coherence:
+
+With a base context of 8k scrolling window and the ability to maintain coherence up to 16k.
+
+Balanced and Versatile:
+
+The core ethos of Lumosia V2 is balance. It's designed to be your go-to assistant.
+
+Experimentation and User-Centric Development:
+
+Lumosia V2 remains an experimental model, a mosaic of the best-performing Solar models, (selected based on user experience). 
+This version is a testament to the idea that innovation is a journey, not a destination.
+
+Come join the Discord:
+[ConvexAI](https://discord.gg/yYqmNmg7Wj)
+
+
+Template:
+```
+### System:
+
+### USER:{prompt}
+
+### Assistant:
+```
+
+
+Settings:
+```
+Temp: 1.0
+min-p: 0.02-0.1
+```
+
+## Evals:
+
+* Avg:
+* ARC:
+* HellaSwag:
+* MMLU:
+* T-QA:
+* Winogrande:
+* GSM8K:
+
+## Examples:
+```
+Example 1:
+
+User:
+
+Lumosia:
+
+```
+```
+Example 2:
+
+User:
+
+Lumosia:
+
+```
+
+## 🧩 Configuration
+
+```
+yaml
+base_model: DopeorNope/SOLARC-M-10.7B
+gate_mode: hidden
+dtype: bfloat16
+
+experts:
+  - source_model: DopeorNope/SOLARC-M-10.7B
+    positive_prompts:
+    
+    negative_prompts:
+
+  - source_model: Sao10K/Fimbulvetr-10.7B-v1 [Updated]
+    positive_prompts:
+    
+    negative_prompts:
+
+  - source_model: jeonsworld/CarbonVillain-en-10.7B-v4 [Updated]
+    positive_prompts:
+    
+    negative_prompts:
+
+  - source_model: kyujinpy/Sakura-SOLAR-Instruct
+    positive_prompts:
+    
+    negative_prompts:
+```
+
+## 💻 Usage
+
+```
+python
+!pip install -qU transformers bitsandbytes accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "Steelskull/Lumosia-v2-MoE-4x10.7"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
+)
+
+messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
+prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+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"])
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Steelskull__Lumosia-v2-MoE-4x10.7)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |73.75|
+|AI2 Reasoning Challenge (25-Shot)|70.39|
+|HellaSwag (10-Shot)              |87.87|
+|MMLU (5-Shot)                    |66.45|
+|TruthfulQA (0-shot)              |68.48|
+|Winogrande (5-shot)              |84.21|
+|GSM8k (5-shot)                   |65.13|
+
+
+
+***
+
+Quantization of Model [Steelskull/Lumosia-v2-MoE-4x10.7](https://huggingface.co/Steelskull/Lumosia-v2-MoE-4x10.7).
+Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline

test.log

@@ -0,0 +1,12 @@
+ What is a Large Language Model?
+Large language models (LLMs) are AI systems that use deep learning techniques to generate human-like text, speech, or images. They are trained on large datasets of text,  and,
+sometimes called generative pre- ...
+Question:
+are increasingly powerful tools in
+,
+naturally generated content,
+
+
+ai text or voice andquot;language models that
+­—text, —or other multimprose to
+207820 Comments are used fora type of ­–––––such as a large-­—and can generate human- /******/