merge_method: model_stock
dtype: float16
base_model: Kaoeiri/Keiana-L3-Test5.75-8B-13.5
models:
  - model: Kaoeiri/Keiana-L3-Test5.4-8B-10
    parameters:
      weight: .2
      density: .25
  - model: Kaoeiri/Keiana-L3-Test4.7-8B-3
    parameters:
      weight: .25
      density: .5
  - model: Kaoeiri/Keiana-L3-Test6-8B-16
    parameters:
      weight: .2
      density: .35
parameters:
  int8_mask: true

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kaoeiri/Keiana-L3-Test6.2-8B-18"
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"])

RichardErkhov/Kaoeiri_-_Keiana-L3-Test6.2-8B-18-4bits

Original model description:

Keiana-L3-Test6.2-8B-18

Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.

🧩 Configuration

💻 Usage

RichardErkhov/
Kaoeiri_-_Keiana-L3-Test6.2-8B-18-4bits