README.md

metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - mlabonne/ChimeraLlama-3-8B-v2
  - nbeerbower/llama-3-stella-8B
  - uygarkurt/llama-3-merged-linear
base_model:
  - mlabonne/ChimeraLlama-3-8B-v2
  - nbeerbower/llama-3-stella-8B
  - uygarkurt/llama-3-merged-linear

NeuralLLaMa-3-8b-DT-v0.1

NeuralLLaMa-3-8b-DT-v0.1 is a merge of the following models using LazyMergekit:

• mlabonne/ChimeraLlama-3-8B-v2
• nbeerbower/llama-3-stella-8B
• uygarkurt/llama-3-merged-linear

🧩 Configuration

models:
  - model: NousResearch/Meta-Llama-3-8B
    # No parameters necessary for base model
  - model: mlabonne/ChimeraLlama-3-8B-v2
    parameters:
      density: 0.33
      weight: 0.2
  - model: nbeerbower/llama-3-stella-8B
    parameters:
      density: 0.44
      weight: 0.4
  - model: uygarkurt/llama-3-merged-linear
    parameters:
      density: 0.55
      weight: 0.4
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralLLaMa-3-8b-DT-v0.1"
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"])