metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - Kukedlc/NeuralMaxime-7B-slerp
  - mlabonne/Monarch-7B
  - eren23/ogno-monarch-jaskier-merge-7b
  - CultriX/NeuralTrix-bf16
base_model:
  - Kukedlc/NeuralMaxime-7B-slerp
  - mlabonne/Monarch-7B
  - eren23/ogno-monarch-jaskier-merge-7b
  - CultriX/NeuralTrix-bf16

NeMoTrix-v1

NeMoTrix-v1 is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: CultriX/MonaTrix-v4
    # No parameters necessary for base model
  - model: Kukedlc/NeuralMaxime-7B-slerp
    parameters:
      weight: 0.36
      density: 0.65
  - model: mlabonne/Monarch-7B
    parameters:
      weight: 0.34
      density: 0.6
  - model: eren23/ogno-monarch-jaskier-merge-7b
    parameters:
      weight: 0.3
      density: 0.6
  - model: CultriX/NeuralTrix-bf16
    parameters:
      weight: 0.4
      density: 0.7
merge_method: dare_ties
base_model: CultriX/MonaTrix-v4
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "CultriX/NeMoTrix-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"])