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Quantization made by Richard Erkhov.
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gemma-2b-orpo - bnb 8bits
- Model creator: https://huggingface.co/anakin87/
- Original model: https://huggingface.co/anakin87/gemma-2b-orpo/
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
---
<img src="./assets/gemma-2b-orpo.png" width="450"></img>
# gemma-2b-orpo
This is an ORPO fine-tune of [google/gemma-2b](https://huggingface.co/google/gemma-2b) with
[`alvarobartt/dpo-mix-7k-simplified`](https://huggingface.co/datasets/alvarobartt/dpo-mix-7k-simplified).
**⚡ Quantized version (GGUF)**: https://huggingface.co/anakin87/gemma-2b-orpo-GGUF
## ORPO
[ORPO (Odds Ratio Preference Optimization)](https://arxiv.org/abs/2403.07691) 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](https://github.com/mlabonne/llm-autoeval)).
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---:|---:|---:|---:|---:|
| [**anakin87/gemma-2b-orpo**](https://huggingface.co/anakin87/gemma-2b-orpo) [📄](./assets/gemma-2b-orpo-Nous.md) | **39.45** | 23.76 | 58.25 | 44.47 | 31.32 |
| [mlabonne/Gemmalpaca-2B](https://huggingface.co/mlabonne/Gemmalpaca-2B) [📄](https://gist.github.com/mlabonne/4b638752fc3227df566f9562064cb864) | 38.39 | 24.48 | 51.22 | 47.02 | 30.85 |
| [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) [📄](https://gist.github.com/mlabonne/db0761e74175573292acf497da9e5d95) | 36.1 | 23.76 | 43.6 | 47.64 | 29.41 |
| [google/gemma-2b](https://huggingface.co/google/gemma-2b) [📄](https://gist.github.com/mlabonne/7df1f238c515a5f63a750c8792cef59e) | 34.26 | 22.7 | 43.35 | 39.96 | 31.03 |
### [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_anakin87__gemma-2b-orpo).
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`](https://huggingface.co/datasets/alvarobartt/dpo-mix-7k-simplified)
is a simplified version of [`argilla/dpo-mix-7k`](https://huggingface.co/datasets/argilla/dpo-mix-7k).
You can find more information [in the dataset card](https://huggingface.co/datasets/alvarobartt/dpo-mix-7k-simplified).
## 🎮 Model in action
### Usage notebook
[📓 Chat and RAG using Haystack](./notebooks/usage.ipynb)
### 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*.
```python
# 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](./notebooks/training.ipynb)
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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