NeuralDareBeagle-7B-slerp
NeuralDareBeagle-7B-slerp is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: mlabonne/NeuralBeagle14-7B
layer_range: [0, 32]
- model: mlabonne/DareBeagle-7B-v2
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/DareBeagle-7B-v2
parameters:
t:
- filter: self_attn
value: [0.5, 0.7, 0.3, 0.7, 1]
- filter: mlp
value: [0.5, 0.3, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "eren23/NeuralDareBeagle-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
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. | 74.60 |
AI2 Reasoning Challenge (25-Shot) | 72.10 |
HellaSwag (10-Shot) | 88.20 |
MMLU (5-Shot) | 64.99 |
TruthfulQA (0-shot) | 69.18 |
Winogrande (5-shot) | 82.56 |
GSM8k (5-shot) | 70.58 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard72.100
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.200
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.990
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard69.180
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard82.560
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.580