Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
Open-Orca/Mistral-7B-OpenOrca
NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
S-miguel/The-Trinity-Coder-7B
chihoonlee10/T3Q-Mistral-Orca-Math-DPO
conversational
text-generation-inference
Inference Endpoints
File size: 3,496 Bytes
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---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Open-Orca/Mistral-7B-OpenOrca
- NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
- S-miguel/The-Trinity-Coder-7B
- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
base_model:
- Open-Orca/Mistral-7B-OpenOrca
- NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
- S-miguel/The-Trinity-Coder-7B
- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
---
# sixtyoneeighty-7b-MOE
sixtyoneeighty-7b-MOE is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
* [NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story](https://huggingface.co/NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story)
* [S-miguel/The-Trinity-Coder-7B](https://huggingface.co/S-miguel/The-Trinity-Coder-7B)
* [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO)
## 🧩 Configuration
```yaml
base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
gate_mode: hidden
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: Open-Orca/Mistral-7B-OpenOrca
positive_prompts:
- "What are some fun activities to do in Seattle?"
- "What are some fun historical facts about New York City?"
negative_prompts:
- "Write a Python script to scrape data from a website."
- "Explain the key differences between Bayesian and frequentist statistics."
- source_model: NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
positive_prompts:
- "Write me a fictional story about dragons and wizards?"
- "From now on take on the role of Dwayne Johnson"
negative_prompts:
- "When is the next solar eclipse."
- "What year did World War II end?"
- source_model: S-miguel/The-Trinity-Coder-7B
positive_prompts:
- "Can you review my JavaScript code and suggest ways to optimize it for better performance?"
- "I'm getting an 'undefined variable' error in my Python script. Here's the code: [code snippet]"
negative_prompts:
- "What are some effective strategies for managing stress and anxiety?"
- "Compare and contrast the themes in 'The Great Gatsby' and 'The Catcher in the Rye'."
- source_model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
positive_prompts:
- "What's a square root of 1337?"
- "Find the midpoint of the line segment with the given end points (-5,7) and (-2,1)"
negative_prompts:
- "What are some effective strategies for managing stress and anxiety?"
- "Compare and contrast the themes in 'The Great Gatsby' and 'The Catcher in the Rye'."
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jambroz/sixtyoneeighty-7b-MOE"
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"])
``` |