OmniBeagle-7B
OmniBeagle-7B is a merge of the following models using LazyMergekit:
🧩 Configuration
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: shadowml/BeagleSempra-7B
parameters:
density: 0.65
weight: 0.4
- model: shadowml/BeagSake-7B
parameters:
density: 0.6
weight: 0.35
- model: shadowml/WestBeagle-7B
parameters:
density: 0.6
weight: 0.35
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: float16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/OmniBeagle-7B"
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. | 75.66 |
AI2 Reasoning Challenge (25-Shot) | 72.61 |
HellaSwag (10-Shot) | 88.93 |
MMLU (5-Shot) | 64.80 |
TruthfulQA (0-shot) | 74.45 |
Winogrande (5-shot) | 83.11 |
GSM8k (5-shot) | 70.05 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard72.610
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.930
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.800
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard74.450
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.110
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.050