CultriX_-_MoNeuTrix-7B-v1-4bits

Quantization made by Richard Erkhov.

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MoNeuTrix-7B-v1 - bnb 4bits

• Model creator: https://huggingface.co/CultriX/
• Original model: https://huggingface.co/CultriX/MoNeuTrix-7B-v1/

Original model description:

tags:

• merge
• mergekit
• lazymergekit
• Kukedlc/NeuralMaxime-7B-slerp
• mlabonne/Monarch-7B
• eren23/ogno-monarch-jaskier-merge-7b base_model:
• Kukedlc/NeuralMaxime-7B-slerp
• mlabonne/Monarch-7B
• eren23/ogno-monarch-jaskier-merge-7b license: apache-2.0

---

MoNeuTrix-7B-v1

MoNeuTrix-7B-v1 is a merge of the following models using LazyMergekit:

• Kukedlc/NeuralMaxime-7B-slerp
• mlabonne/Monarch-7B
• eren23/ogno-monarch-jaskier-merge-7b

🧩 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
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/MoNeuTrix-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"])