README.md

metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - Kukedlc/NeuralKrishna-7B-v3
  - Kukedlc/NeuralMarioMonarch-7B-slerp
  - liminerity/M7-7b
base_model:
  - Kukedlc/NeuralKrishna-7B-v3
  - Kukedlc/NeuralMarioMonarch-7B-slerp
  - liminerity/M7-7b
license: apache-2.0

NeuralKrishna-7B-v4

NeuralKrishna-7B-v4 is a merge of the following models using LazyMergekit:

• Kukedlc/NeuralKrishna-7B-v3
• Kukedlc/NeuralMarioMonarch-7B-slerp
• liminerity/M7-7b

🧩 Configuration

models:
  - model: Kukedlc/NeuralKrishna-7B-v3
    # no parameters necessary for base model
  - model: Kukedlc/NeuralKrishna-7B-v3
    parameters:
      density: 0.65
      weight: 0.36
  - model: Kukedlc/NeuralMarioMonarch-7B-slerp
    parameters:
      density: 0.6
      weight: 0.34
  - model: liminerity/M7-7b
    parameters:
      density: 0.4
      weight: 0.3
merge_method: dare_ties
base_model: Kukedlc/NeuralKrishna-7B-v3
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/NeuralKrishna-7B-v4"
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"])