---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- 222gate/Blurdus-7b-v0.1
- 222gate/Blurred-Beagle-7b-slerp
- liminerity/Blur-7b-v1.21
- liminerity/Blur-7B-slerp-v0.1
base_model:
- 222gate/Blurdus-7b-v0.1
- 222gate/Blurred-Beagle-7b-slerp
- liminerity/Blur-7b-v1.21
- liminerity/Blur-7B-slerp-v0.1
model-index:
- name: Blur-4x7b-MOE-v0.1
  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: 72.27
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blur-4x7b-MOE-v0.1
      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: 88.14
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blur-4x7b-MOE-v0.1
      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: 65.05
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blur-4x7b-MOE-v0.1
      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: 68.82
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blur-4x7b-MOE-v0.1
      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: 82.56
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blur-4x7b-MOE-v0.1
      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: 68.92
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blur-4x7b-MOE-v0.1
      name: Open LLM Leaderboard
---

# Blur-4x7b-MOE-v0.1

Blur-4x7b-MOE-v0.1 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [222gate/Blurdus-7b-v0.1](https://huggingface.co/222gate/Blurdus-7b-v0.1)
* [222gate/Blurred-Beagle-7b-slerp](https://huggingface.co/222gate/Blurred-Beagle-7b-slerp)
* [liminerity/Blur-7b-v1.21](https://huggingface.co/liminerity/Blur-7b-v1.21)
* [liminerity/Blur-7B-slerp-v0.1](https://huggingface.co/liminerity/Blur-7B-slerp-v0.1)

## 🧩 Configuration

```yaml
base_model: 222gate/BrurryDog-7b-v0.1
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: 222gate/Blurdus-7b-v0.1
    positive_prompts:
    - "versatile"
    - "helpful"
    - "factual"
    - "integrated"
    - "adaptive"
    - "comprehensive"
    - "balanced"
    negative_prompts:
    - "specialized"
    - "narrow"
    - "focused"
    - "limited"
    - "specific"

  - source_model: 222gate/Blurred-Beagle-7b-slerp
    positive_prompts:
    - "creative"
    - "chat"
    - "discuss"
    - "culture"
    - "world"
    - "expressive"
    - "detailed"
    - "imaginative"
    - "engaging"
    negative_prompts:
    - "sorry"
    - "cannot"
    - "factual"
    - "concise"
    - "straightforward"
    - "objective"
    - "dry"

  - source_model: liminerity/Blur-7b-v1.21
    positive_prompts:
    - "analytical"
    - "accurate"
    - "logical"
    - "knowledgeable"
    - "precise"
    - "calculate"
    - "compute"
    - "solve"
    - "work"
    - "python"
    - "javascript"
    - "programming"
    - "algorithm"
    - "tell me"
    - "assistant"
    negative_prompts:
    - "creative"
    - "abstract"
    - "imaginative"
    - "artistic"
    - "emotional"
    - "mistake"
    - "inaccurate"

  - source_model: liminerity/Blur-7B-slerp-v0.1
    positive_prompts:
    - "instructive"
    - "clear"
    - "directive"
    - "helpful"
    - "informative"
    negative_prompts:
    - "exploratory"
    - "open-ended"
    - "narrative"
    - "speculative"
    - "artistic"

```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "222gate/Blur-4x7b-MOE-v0.1"

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_222gate__Blur-4x7b-MOE-v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |74.29|
|AI2 Reasoning Challenge (25-Shot)|72.27|
|HellaSwag (10-Shot)              |88.14|
|MMLU (5-Shot)                    |65.05|
|TruthfulQA (0-shot)              |68.82|
|Winogrande (5-shot)              |82.56|
|GSM8k (5-shot)                   |68.92|