metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - allknowingroger/LimyQstar-7B-slerp
  - allknowingroger/JaskierMistral-7B-slerp
  - allknowingroger/LimmyAutomerge-7B-slerp
base_model:
  - allknowingroger/LimyQstar-7B-slerp
  - allknowingroger/JaskierMistral-7B-slerp
  - allknowingroger/LimmyAutomerge-7B-slerp
license: apache-2.0

TripleMerge2-7B-Ties

TripleMerge2-7B-Ties is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: allknowingroger/LimyQstar-7B-slerp
    parameters:
      density: [1, 0.7, 0.1] # density gradient
      weight: 1.0
  - model: allknowingroger/JaskierMistral-7B-slerp
    parameters:
      density: 0.5
      weight: [0, 0.3, 0.7, 1] # weight gradient
  - model: allknowingroger/LimmyAutomerge-7B-slerp
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: allknowingroger/LimyQstar-7B-slerp
parameters:
  normalize: true
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "allknowingroger/TripleMerge2-7B-Ties"
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"])