metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - bardsai/jaskier-7b-dpo-v3.3
  - Kquant03/NeuralTrix-7B-dpo-laser
  - CultriX/NeuralTrix-v4-bf16
  - CultriX/NeuralTrix-V2
base_model:
  - bardsai/jaskier-7b-dpo-v3.3
  - Kquant03/NeuralTrix-7B-dpo-laser
  - CultriX/NeuralTrix-v4-bf16
  - CultriX/NeuralTrix-V2

NeuralTrixlaser-bf16

NeuralTrixlaser-bf16 is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: eren23/dpo-binarized-NeuralTrix-7B
    # no parameters necessary for base model
  - model: bardsai/jaskier-7b-dpo-v3.3
    parameters:
      density: 0.65
      weight: 0.4
  - model: Kquant03/NeuralTrix-7B-dpo-laser
    parameters:
      density: 0.6
      weight: 0.35
  - model: CultriX/NeuralTrix-v4-bf16
    parameters:
      density: 0.55
      weight: 0.15
  - model: CultriX/NeuralTrix-V2
    parameters:
      density: 0.55
      weight: 0.15
merge_method: dare_ties
base_model: eren23/dpo-binarized-NeuralTrix-7B
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "CultriX/"
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"])