metadata

license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
library_name: transformers
base_model: google/gemma-2b
tags:
  - trl
  - orpo
  - generated_from_trainer
model-index:
  - name: gemma-2b-orpo
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 49.15
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=anakin87%2Fgemma-2b-orpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 73.72
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=anakin87%2Fgemma-2b-orpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 38.52
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=anakin87%2Fgemma-2b-orpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 44.53
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=anakin87%2Fgemma-2b-orpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 64.33
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=anakin87%2Fgemma-2b-orpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 13.87
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=anakin87%2Fgemma-2b-orpo
          name: Open LLM Leaderboard
datasets:
  - alvarobartt/dpo-mix-7k-simplified
language:
  - en

gemma-2b-orpo

This is an ORPO fine-tune of google/gemma-2b with alvarobartt/dpo-mix-7k-simplified.

⚡ Quantized version (GGUF): https://huggingface.co/anakin87/gemma-2b-orpo-GGUF

ORPO

ORPO (Odds Ratio Preference Optimization) is a new training paradigm that combines the usually separated phases of SFT (Supervised Fine-Tuning) and Preference Alignment (usually performed with RLHF or simpler methods like DPO).

Faster training
Less memory usage (no reference model needed)
Good results!

🏆 Evaluation

Nous

gemma-2b-orpo performs well for its size on Nous' benchmark suite.

(evaluation conducted using LLM AutoEval).

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
anakin87/gemma-2b-orpo 📄	39.45	23.76	58.25	44.47	31.32
mlabonne/Gemmalpaca-2B 📄	38.39	24.48	51.22	47.02	30.85
google/gemma-2b-it 📄	36.1	23.76	43.6	47.64	29.41
google/gemma-2b 📄	34.26	22.7	43.35	39.96	31.03

Open LLM Leaderboard

Detailed results can be found here.

By comparison, on the Open LLM Leaderboard, google/gemma-2b-it has an average of 42.75.

Metric	Value
Avg.	47.35
AI2 Reasoning Challenge (25-Shot)	49.15
HellaSwag (10-Shot)	73.72
MMLU (5-Shot)	38.52
TruthfulQA (0-shot)	44.53
Winogrande (5-shot)	64.33
GSM8k (5-shot)	13.87

🙏 Dataset

alvarobartt/dpo-mix-7k-simplified is a simplified version of argilla/dpo-mix-7k. You can find more information in the dataset card.

🎮 Model in action

Usage notebook

📓 Chat and RAG using Haystack

Simple text generation with Transformers

The model is small, so it runs smoothly on Colab. It is also fine to load the model using quantization.

# pip install transformers accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="anakin87/gemma-2b-orpo", torch_dtype=torch.bfloat16, device_map="auto")
messages = [{"role": "user", "content": "Write a rap song on Vim vs VSCode."}]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False)
outputs = pipe(prompt, max_new_tokens=500, do_sample=True, temperature=0.7,  top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Training

The model was trained using HF TRL. 📓 Training notebook

Framework versions

Transformers 4.39.1
Pytorch 2.2.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2