NeuralShiva-7B-DT/README.md

tags, base_model, license

tags	base_model	license
merge mergekit lazymergekit automerger/YamShadow-7B mlabonne/AlphaMonarch-7B automerger/OgnoExperiment27-7B Kukedlc/Jupiter-k-7B-slerp	automerger/YamShadow-7B mlabonne/AlphaMonarch-7B automerger/OgnoExperiment27-7B Kukedlc/Jupiter-k-7B-slerp	apache-2.0

NeuralShiva-7B-DT

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

• automerger/YamShadow-7B
• mlabonne/AlphaMonarch-7B
• automerger/OgnoExperiment27-7B
• Kukedlc/Jupiter-k-7B-slerp

🧬 Model Family

🧩 Configuration

models:
  - model: liminerity/M7-7b
    # no parameters necessary for base model
  - model: automerger/YamShadow-7B 
    parameters:
      weight: 0.3
      density: 0.5
  - model: mlabonne/AlphaMonarch-7B 
    parameters:
      weight: 0.2
      density: 0.5
  - model: automerger/OgnoExperiment27-7B 
    parameters:
      weight: 0.2
      density: 0.5
  - model: Kukedlc/Jupiter-k-7B-slerp
    parameters:
      weight: 0.3
      density: 0.5
merge_method: dare_ties
base_model: liminerity/M7-7b

parameters:
  int8_mask: true
  normalize: true
dtype: bfloat16

💻 Usage - Stream

# Requirements
!pip install -qU transformers accelerate bitsandbytes

# Imports & settings
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import warnings
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
warnings.filterwarnings('ignore')

# Model & Tokenizer
MODEL_NAME = "Kukedlc/NeuralShiva-7B-DT"
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:1', load_in_4bit=True)
tok = AutoTokenizer.from_pretrained(MODEL_NAME)

# Inference
prompt = "I want you to generate a theory that unites quantum mechanics with the theory of relativity and cosmic consciousness"
inputs = tok([prompt], return_tensors="pt").to('cuda')
streamer = TextStreamer(tok)

# Despite returning the usual output, the streamer will also print the generated text to stdout.
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, do_sample=True, num_beams=1, top_p=0.9, temperature=0.7)

💻 Usage - Clasic

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralShiva-7B-DT"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])