DPO Fine-tuned version

I DPO finetuned this model later to obtain a slightly better model (open llm leaderboard benchmark performance) https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO

ogno-monarch-jaskier-merge-7b

ogno-monarch-jaskier-merge-7b is a merge of the following models using LazyMergekit:

• mlabonne/Monarch-7B
• paulml/OGNO-7B
• bardsai/jaskier-7b-dpo-v5.6

🧩 Configuration

models:
  - model: eren23/dpo-binarized-NeutrixOmnibe-7B
    # No parameters necessary for base model
  - model: mlabonne/Monarch-7B
    #Emphasize the beginning of Vicuna format models
    parameters:
      weight: 0.6
      density: 0.59
  - model: paulml/OGNO-7B
    parameters:
      weight: 0.1
      density: 0.55
  # Vicuna format
  - model: bardsai/jaskier-7b-dpo-v5.6
    parameters:
      weight: 0.3
      density: 0.55

merge_method: dare_ties
base_model: eren23/dpo-binarized-NeutrixOmnibe-7B
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "eren23/ogno-monarch-jaskier-merge-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"])

GGUF Version: https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-GGUF

Open LLM Leaderboard Evaluation Results

Detailed results can be found here