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yatinece
/
yk_8x7b_model_v1

Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
mlabonne/AlphaMonarch-7B
beowolx/CodeNinja-1.0-OpenChat-7B
SanjiWatsuki/Kunoichi-DPO-v2-7B
mlabonne/NeuralDaredevil-7B
HuggingFaceH4/zephyr-7b-beta
mistralai/Mistral-7B-Instruct-v0.2
teknium/OpenHermes-2.5-Mistral-7B
meta-math/MetaMath-Mistral-7B
conversational
text-generation-inference
Model card Files
xet
Community
1
yk_8x7b_model_v1
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---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mlabonne/AlphaMonarch-7B
- beowolx/CodeNinja-1.0-OpenChat-7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- mlabonne/NeuralDaredevil-7B
- HuggingFaceH4/zephyr-7b-beta
- mistralai/Mistral-7B-Instruct-v0.2
- teknium/OpenHermes-2.5-Mistral-7B
- meta-math/MetaMath-Mistral-7B
base_model:
- mlabonne/AlphaMonarch-7B
- beowolx/CodeNinja-1.0-OpenChat-7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- mlabonne/NeuralDaredevil-7B
- HuggingFaceH4/zephyr-7b-beta
- mistralai/Mistral-7B-Instruct-v0.2
- teknium/OpenHermes-2.5-Mistral-7B
- meta-math/MetaMath-Mistral-7B
---

# yk_8x7b_model

yk_8x7b_model is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
* [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)
* [mlabonne/NeuralDaredevil-7B](https://huggingface.co/mlabonne/NeuralDaredevil-7B)
* [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
* [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)

## 🧩 Configuration

```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
dtype: float16
gate_mode: hidden
experts:
  - source_model: mlabonne/AlphaMonarch-7B
    positive_prompts:
      - "chat"
      - "assistant"
      - "tell me"
      - "explain"
      - "I want"
      - "help"
  - source_model: beowolx/CodeNinja-1.0-OpenChat-7B
    positive_prompts:
      - "code"
      - "python"
      - "javascript"
      - "programming"
      - "algorithm"
      - "coding"
  - source_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
    positive_prompts:
      - "storywriting"
      - "write"
      - "scene"
      - "story"
      - "character"
      - "creative"
  - source_model: mlabonne/NeuralDaredevil-7B
    positive_prompts:
      - "reason"
      - "math"
      - "mathematics"
      - "solve"
      - "count"
      - "logic"
  - source_model: HuggingFaceH4/zephyr-7b-beta
    positive_prompts:
      - "You are an helpful general-purpose assistant."
      - "assist"
      - "helpful"
      - "support"
      - "guide"
  - source_model: mistralai/Mistral-7B-Instruct-v0.2
    positive_prompts:
      - "You are helpful assistant."
      - "aid"
      - "assist"
      - "guide"
      - "support"
  - source_model: teknium/OpenHermes-2.5-Mistral-7B
    positive_prompts:
      - "You are helpful a coding assistant."
      - "code"
      - "programming"
      - "debug"
      - "scripting"
      - "coding"
  - source_model: meta-math/MetaMath-Mistral-7B
    positive_prompts:
      - "You are an assistant good at math."
      - "mathematics"
      - "calculation"
      - "problem solving"
      - "arithmetics"
      - "math"

```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "yatinece/yk_8x7b_model"

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"])
```