CultriX / MergeTrix-7B-GGUF

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

• abideen/NexoNimbus-7B
• fblgit/UNA-TheBeagle-7b-v1
• argilla/distilabeled-Marcoro14-7B-slerp

MergeTrix-7B-GGUF

Quantisized versions of MergeTrix-7B. Supports:

• mergetrix-7b.Q4_K_M.gguf (4.37GB): medium, balanced quality
• mergetrix-7b.Q5_K_S.gguf (5 GB): large, low quality loss
• mergetrix-7b.Q5_K_M.gguf (5.13 GB): large, very low quality loss
• mergetrix-7b.Q6_K.gguf (5.94 GB): very large, extremely low quality loss

🧩 Configuration

models:
  - model: udkai/Turdus
    # No parameters necessary for base model
  - model: abideen/NexoNimbus-7B
    parameters:
      density: 0.53
      weight: 0.4
  - model: fblgit/UNA-TheBeagle-7b-v1
    parameters:
      density: 0.53
      weight: 0.3
  - model: argilla/distilabeled-Marcoro14-7B-slerp
    parameters:
      density: 0.53
      weight: 0.3
merge_method: dare_ties
base_model: udkai/Turdus
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "CultriX/MergeTrix-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"])