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
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-4x7b-v2 | |
sixtyoneeighty-4x7b-v2 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"]) | |
``` |