README.md

metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - Kukedlc/NeuralSoTa-7b-v0.1
  - Kukedlc/NeuralSynthesis-7B-v0.3
  - Kukedlc/NeuralSirKrishna-7b
base_model:
  - Kukedlc/NeuralSoTa-7b-v0.1
  - Kukedlc/NeuralSynthesis-7B-v0.3
  - Kukedlc/NeuralSirKrishna-7b

NeuralSOTA-7B-slerp

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

• Kukedlc/NeuralSoTa-7b-v0.1
• Kukedlc/NeuralSynthesis-7B-v0.3
• Kukedlc/NeuralSirKrishna-7b

🧩 Configuration

models:
  - model: Kukedlc/NeuralSirKrishna-7b
    # no parameters necessary for base model
  - model: Kukedlc/NeuralSoTa-7b-v0.1
    parameters:
      density: 0.55
      weight: 0.3
  - model: Kukedlc/NeuralSynthesis-7B-v0.3
    parameters:
      density: 0.55
      weight: 0.35
  - model: Kukedlc/NeuralSirKrishna-7b
    parameters:
      density: 0.55
      weight: 0.35
merge_method: dare_ties
base_model: Kukedlc/NeuralSirKrishna-7b
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralSOTA-7B-slerp"
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"])