As an experiment to find the best base merge to further fine-tuning, expect a lot of experiments named using parts of the component models until a clear winner emerges in the benchmarks

In this case merging 2 DPOs of the same model

🧩 Configuration

yaml

slices:
  - sources:
      - model:  argilla/distilabeled-Marcoro14-7B-slerp
        layer_range: [0, 32]
      - model: mlabonne/NeuralMarcoro14-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/NeuralMarcoro14-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "flemmingmiguel/NeuDist-Ro-7B"
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/flemmingmiguel_-_NeuDist-Ro-7B-4bits

Original model description:

NeuDist-Ro-7B

🧩 Configuration

💻 Usage

RichardErkhov/
flemmingmiguel_-_NeuDist-Ro-7B-4bits