metadata

tags:
  - merge
  - mergekit
  - '#dpo'
  - MaximeLabonne
  - '#mergeofmerge'
base_model:
  - CultriX/NeuralTrix-7B-dpo
  - paulml/OmniBeagleSquaredMBX-v3-7B-v2
license: apache-2.0

This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him!

Labonne LLM Course

NeuTrixOmniBe-DPO

NeuTrix7000-7b-DPO is a merge of the following models using LazyMergekit:

🧩 Configuration

MODEL_NAME = "NeuTrix7000-7b-DPO"
yaml_config = """
slices:
  - sources:
      - model: CultriX/NeuralTrix-7B-dpo
        layer_range: [0, 32]
      - model: paulml/OmniBeagleSquaredMBX-v3-7B-v2
        layer_range: [0, 32]
merge_method: slerp
base_model: CultriX/NeuralTrix-7B-dpo
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
"""

It was then trained with DPO using:

Intel/orca_dpo_pairs

💻 Usage

!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuTrix7000-7b-DPO"
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=128, do_sample=True, temperature=0.5, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])