transformers safetensors mixtral text-generation moe frankenmoe merge mergekit lazymergekit mistralai/Mistral-7B-Instruct-v0.2 macadeliccc/WestLake-7B-v2-laser-truthy-dpo conversational base_model:mistralai/Mistral-7B-Instruct-v0.2 base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- macadeliccc/WestLake-7B-v2-laser-truthy-dpo
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- macadeliccc/WestLake-7B-v2-laser-truthy-dpo
---

# RandomMergeNoNormWEIGHTED-7B-2x7BMOE

RandomMergeNoNormWEIGHTED-7B-2x7BMOE is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [macadeliccc/WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo)

## 🧩 Configuration

```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
gate_mode: cheap_embed
dtype: float16
experts:
  - source_model: mistralai/Mistral-7B-Instruct-v0.2
    positive_prompts: ["science, logic, math"]
  - source_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
    positive_prompts: ["roleplay, creativity, fiction"]
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/RandomMergeNoNormWEIGHTED-7B-2x7BMOE"

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"])
```