metadata
tags:
- merge
- mergekit
- lazymergekit
- CultriX/MonaTrix-v4
- mlabonne/OmniTruthyBeagle-7B-v0
- Kukedlc/NeuralMaxime-7B-slerp
- CultriX/NeuralTrixlaser-bf16
base_model:
- CultriX/MonaTrix-v4
- mlabonne/OmniTruthyBeagle-7B-v0
- Kukedlc/NeuralMaxime-7B-slerp
- CultriX/NeuralTrixlaser-bf16
MergeCeption-7B-v1
MergeCeption-7B-v1 is a merge of the following models using LazyMergekit:
- CultriX/MonaTrix-v4
- mlabonne/OmniTruthyBeagle-7B-v0
- Kukedlc/NeuralMaxime-7B-slerp
- CultriX/NeuralTrixlaser-bf16
🧩 Configuration
base_model: CultriX/MonaTrix-v4
merge_method: dare_ties
models:
- model: CultriX/MonaTrix-v4
parameters:
density: 0.65
weight: 0.4
- model: mlabonne/OmniTruthyBeagle-7B-v0
parameters:
density: 0.65
weight: 0.4
- model: Kukedlc/NeuralMaxime-7B-slerp
parameters:
density: 0.65
weight: 0.4
- model: CultriX/NeuralTrixlaser-bf16
parameters:
density: 0.6
weight: 0.35
parameters:
int8_mask: true
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/MergeCeption-7B-v1"
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"])