README.md

metadata

base_model:
  - macadeliccc/MBX-7B-v3-DPO
  - mlabonne/OmniBeagle-7B
tags:
  - mergekit
  - merge

OmniCorso-7B

This model is a finetune of flemmingmiguel/MBX-7B-v3 using jondurbin/truthy-dpo-v0.1

Code Example

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/MBX-7B-v3-DPO")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/MBX-7B-v3-DPO")

messages = [
    {"role": "system", "content": "Respond to the users request like a pirate"},
    {"role": "user", "content": "Can you write me a quicksort algorithm?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")

The following models were included in the merge:

• macadeliccc/MBX-7B-v3-DPO
• mlabonne/OmniBeagle-7B

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: mlabonne/OmniBeagle-7B
        layer_range: [0, 32]
      - model: macadeliccc/MBX-7B-v3-DPO
        layer_range: [0, 32]
merge_method: slerp
base_model: macadeliccc/MBX-7B-v3-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