Quantization made by Richard Erkhov.

gemma-2b-orpo - GGUF

Model creator: https://huggingface.co/anakin87/
Original model: https://huggingface.co/anakin87/gemma-2b-orpo/

Name	Quant method	Size
gemma-2b-orpo.Q2_K.gguf	Q2_K	1.08GB
gemma-2b-orpo.IQ3_XS.gguf	IQ3_XS	1.16GB
gemma-2b-orpo.IQ3_S.gguf	IQ3_S	1.2GB
gemma-2b-orpo.Q3_K_S.gguf	Q3_K_S	1.2GB
gemma-2b-orpo.IQ3_M.gguf	IQ3_M	1.22GB
gemma-2b-orpo.Q3_K.gguf	Q3_K	1.29GB
gemma-2b-orpo.Q3_K_M.gguf	Q3_K_M	1.29GB
gemma-2b-orpo.Q3_K_L.gguf	Q3_K_L	1.36GB
gemma-2b-orpo.IQ4_XS.gguf	IQ4_XS	1.4GB
gemma-2b-orpo.Q4_0.gguf	Q4_0	1.44GB
gemma-2b-orpo.IQ4_NL.gguf	IQ4_NL	1.45GB
gemma-2b-orpo.Q4_K_S.gguf	Q4_K_S	1.45GB
gemma-2b-orpo.Q4_K.gguf	Q4_K	1.52GB
gemma-2b-orpo.Q4_K_M.gguf	Q4_K_M	1.52GB
gemma-2b-orpo.Q4_1.gguf	Q4_1	1.56GB
gemma-2b-orpo.Q5_0.gguf	Q5_0	1.68GB
gemma-2b-orpo.Q5_K_S.gguf	Q5_K_S	1.68GB
gemma-2b-orpo.Q5_K.gguf	Q5_K	1.71GB
gemma-2b-orpo.Q5_K_M.gguf	Q5_K_M	1.71GB
gemma-2b-orpo.Q5_1.gguf	Q5_1	1.79GB
gemma-2b-orpo.Q6_K.gguf	Q6_K	1.92GB
gemma-2b-orpo.Q8_0.gguf	Q8_0	2.49GB

Original model description:

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