SevereNeuralBeagleTrix-7B/README.md

tags, base_model, license

tags	base_model	license
merge mergekit lazymergekit PetroGPT/WestSeverus-7B-DPO CultriX/MergeTrix-7B-v2 mlabonne/NeuralBeagle14-7B	PetroGPT/WestSeverus-7B-DPO CultriX/MergeTrix-7B-v2 mlabonne/NeuralBeagle14-7B	apache-2.0

EDIT:

Always check my space for the latest benchmark results for my models!

• https://huggingface.co/spaces/CultriX/Yet_Another_LLM_Leaderboard

SevereNeuralBeagleTrix-7B

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

• PetroGPT/WestSeverus-7B-DPO
• CultriX/MergeTrix-7B-v2
• mlabonne/NeuralBeagle14-7B

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # No parameters necessary for base model
  - model: PetroGPT/WestSeverus-7B-DPO
    parameters:
      density: 0.53
      weight: 0.3
  - model: CultriX/MergeTrix-7B-v2
    parameters:
      density: 0.53
      weight: 0.4
  - model: mlabonne/NeuralBeagle14-7B
    parameters:
      density: 0.53
      weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "CultriX/SevereNeuralBeagleTrix-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"])