README.md

metadata

license: apache-2.0
tags:
  - merge
  - mergekit
  - lazymergekit
  - fblgit/UNA-TheBeagle-7b-v1
  - udkai/Turdus

Marcoroni-7b-DPO-Merge

Marcoroni-7b-DPO-Merge is a merge of the following models using mergekit and inspired by Maxime Labonne's work:

• fblgit/UNA-TheBeagle-7b-v1
• udkai/Turdus

🧩 Configuration

models:
  - model: madatnlp/marcoroni-7b-v3-safetensor
    # no parameters necessary for base model
  - model: fblgit/UNA-TheBeagle-7b-v1
    parameters:
      density: 0.3
      weight: 0.5
  - model: udkai/Turdus
    parameters:
      density: 0.7
      weight: 0.3
merge_method: ties
base_model: madatnlp/marcoroni-7b-v3-safetensor
parameters:
  normalize: true
dtype: float16

💻 Example Python Code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "nfaheem/Marcoroni-7b-DPO-Merge"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''{prompt}
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])