metadata
tags:
- merge
- mergekit
- lazymergekit
base_model:
- mlabonne/AlphaMonarch-7B
- Nexusflow/Starling-LM-7B-beta
license: apache-2.0
language:
- en
StarMonarch-7B
Description
StarMonarch-7B is a merge of the following models using LazyMergekit:
This model uses a context window of 8k. Special thanks to mlabonne and Nexusflow for the models.
π Open LLM Leaderboard Evaluation Results
Metric | Value |
---|---|
Avg. | 74.45 |
AI2 Reasoning Challenge (25-Shot) | 71.25 |
HellaSwag (10-Shot) | 87.00 |
MMLU (5-Shot) | 65.48 |
TruthfulQA (0-shot) | 67.20 |
Winogrande (5-shot) | 82.16 |
GSM8k (5-shot) | 73.62 |
𧩠Configuration
slices:
- sources:
- model: mlabonne/AlphaMonarch-7B
layer_range: [0, 32]
- model: Nexusflow/Starling-LM-7B-beta
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Ppoyaa/StarMonarch-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"])