  models:
    - model: timpal0l/Mistral-7B-v0.1-flashback-v2
      # No parameters necessary for base model
    - model: mlabonne/NeuralBeagle14-7B
      parameters:
        density: 0.53
        weight: 0.6
  merge_method: dare_ties
  base_model: timpal0l/Mistral-7B-v0.1-flashback-v2
  parameters:
    int8_mask: true
  dtype: bfloat16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "birgermoell/NeuralBeagle-Flashback"
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"])