Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) 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 --- # 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