Beyonder-2x7B-v2
Beyonder-2x7B-v2 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
🧩 Configuration
base_model: mlabonne/NeuralBeagle14-7B
gate_mode: random
experts:
- source_model: mlabonne/NeuralBeagle14-7B
positive_prompts: [""]
- source_model: mlabonne/NeuralDaredevil-7B
positive_prompts: [""]
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "shadowml/Beyonder-2x7B-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"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 74.49 |
AI2 Reasoning Challenge (25-Shot) | 72.01 |
HellaSwag (10-Shot) | 88.12 |
MMLU (5-Shot) | 64.51 |
TruthfulQA (0-shot) | 69.09 |
Winogrande (5-shot) | 82.72 |
GSM8k (5-shot) | 70.51 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard72.010
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.120
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.510
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard69.090
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard82.720
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.510