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:
🧩 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
Metric | Value |
---|---|
Avg. | 76.43 |
AI2 Reasoning Challenge (25-Shot) | 73.04 |
HellaSwag (10-Shot) | 89.09 |
MMLU (5-Shot) | 64.78 |
TruthfulQA (0-shot) | 77.44 |
Winogrande (5-shot) | 84.77 |
GSM8k (5-shot) | 69.45 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard73.040
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard89.090
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.780
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard77.440
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.770
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard69.450