Beyonder-4x7B-v2

This model is a Mixure of Experts (MoE) made with mergekit (mixtral branch). It uses the following base models:

• openchat/openchat-3.5-1210
• beowolx/CodeNinja-1.0-OpenChat-7B
• maywell/PiVoT-0.1-Starling-LM-RP
• WizardLM/WizardMath-7B-V1.1

🧩 Configuration

base_model: mlabonne/Marcoro14-7B-slerp
experts:
 - source_model: openchat/openchat-3.5-1210
 positive_prompts:
 - "chat"
 - "assistant"
 - "tell me"
 - "explain"
 - source_model: beowolx/CodeNinja-1.0-OpenChat-7B
 positive_prompts:
 - "code"
 - "python"
 - "javascript"
 - "programming"
 - "algorithm"
 - source_model: maywell/PiVoT-0.1-Starling-LM-RP
 positive_prompts:
 - "storywriting"
 - "write"
 - "scene"
 - "story"
 - "character"
 - source_model: WizardLM/WizardMath-7B-V1.1
 positive_prompts:
 - "reason"
 - "math"
 - "mathematics"
 - "solve"
 - "count"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Beyonder-4x7B-v2"

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"])