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Quantization made by Richard Erkhov.
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[Github](https://github.com/RichardErkhov)
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[Discord](https://discord.gg/pvy7H8DZMG)
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[Request more models](https://github.com/RichardErkhov/quant_request)
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suzume-llama-3-8B-multilingual-orpo-borda-top25 - GGUF
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- Model creator: https://huggingface.co/lightblue/
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- Original model: https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25/
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q2_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q2_K.gguf) | Q2_K | 2.96GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ3_S.gguf) | IQ3_S | 3.43GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ3_M.gguf) | IQ3_M | 3.52GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q3_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q3_K.gguf) | Q3_K | 3.74GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_0.gguf) | Q4_0 | 4.34GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K.gguf) | Q4_K | 4.58GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_1.gguf) | Q4_1 | 4.78GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_0.gguf) | Q5_0 | 5.21GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_K.gguf) | Q5_K | 5.34GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_1.gguf) | Q5_1 | 5.65GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q6_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q6_K.gguf) | Q6_K | 6.14GB |
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| [suzume-llama-3-8B-multilingual-orpo-borda-top25.Q8_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/blob/main/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q8_0.gguf) | Q8_0 | 7.95GB |
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Original model description:
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---
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license: cc-by-nc-4.0
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tags:
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- generated_from_trainer
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base_model: lightblue/suzume-llama-3-8B-multilingual
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model-index:
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- name: workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_top25_borda
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results: []
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---
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# Suzume ORPO
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<p align="center">
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<img width=500 src="https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kWQSu02YfgYdUQqv4s5lq.png" alt="Suzume with Mitsu - a Japanese tree sparrow with honey on it"/>
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</p>
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[[Paper]](https://arxiv.org/abs/2405.18952) [[Dataset]](https://huggingface.co/datasets/lightblue/mitsu)
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This is Suzume ORPO, an ORPO trained fine-tune of the [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) model using our [lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu) dataset.
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We have trained several versions of this model using ORPO and so recommend that you use the best performing model from our tests, [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half).
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Note that this model has a non-commerical license as we used the Command R and Command R+ models to generate our training data for this model ([lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu)).
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We are currently working on a developing a commerically usable model, so stay tuned for that!
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# Model list
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We have ORPO trained the following models using different proportions of the [lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu) dataset:
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* Trained on the top/bottom responses of all prompts in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full)
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* Trained on the top/bottom responses of the prompts of the 75\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75)
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* Trained on the top/bottom responses of the prompts of the 50\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half)
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* Trained on the top/bottom responses of the prompts of the 25\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25)
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# Model results
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We compare the MT-Bench scores across 6 languages for our 4 ORPO trained models, as well as some baselines:
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* [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) - The foundation model that our models are ultimately built upon
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* [Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) - The highest performing open model on the Chatbot arena that is of a similar size to ours
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* gpt-3.5-turbo - A fairly high quality (although not state-of-the-art) proprietary LLM
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* [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) - The base model which we train our ORPO finetunes from
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| **MT-Bench language** | **meta-llama/Meta-Llama-3-8B-Instruct** | **Nexusflow/Starling-LM-7B-beta** | **gpt-3.5-turbo** | **lightblue/suzume-llama-3-8B-multilingual** | **lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full** | **lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75** | **lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half** | **lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25** |
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|-----------------------|-----------------------------------------|-----------------------------------|-------------------|----------------------------------------------|--------------------------------------------------------------|---------------------------------------------------------------|--------------------------------------------------------------|---------------------------------------------------------------|
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| **Chinese 🇨🇳** | NaN | 6.97 | 7.55 | 7.11 | 7.65 | **7.77** | 7.74 | 7.44 |
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| **English 🇺🇸** | 7.98 | 7.92 | **8.26** | 7.73 | 7.98 | 7.94 | 7.98 | 8.22 |
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| **French 🇫🇷** | NaN | 7.29 | 7.74 | 7.66 | **7.84** | 7.46 | 7.78 | 7.81 |
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| **German 🇩🇪** | NaN | 6.99 | 7.68 | 7.26 | 7.28 | 7.64 | 7.7 | **7.71** |
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| **Japanese 🇯🇵** | NaN | 6.22 | **7.84** | 6.56 | 7.2 | 7.12 | 7.34 | 7.04 |
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| **Russian 🇷🇺** | NaN | 8.28 | 7.94 | 8.19 | 8.3 | 8.74 | **8.94** | 8.81 |
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We can see noticable improvement on most languages compared to the base model. We also find that our ORPO models achieve the highest score out of all the models we evaluated for a number of languages.
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# Training data
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We trained this model using the [lightblue/mitsu_full_borda](https://huggingface.co/datasets/lightblue/mitsu_full_borda) dataset.
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# Training configuration
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.0`
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```yaml
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base_model: lightblue/suzume-llama-3-8B-multilingual
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer # PreTrainedTokenizerFast
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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rl: orpo
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orpo_alpha: 0.1
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remove_unused_columns: false
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chat_template: chatml
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datasets:
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- path: lightblue/mitsu_top25_borda
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type: orpo.chat_template
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conversation: llama-3
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dataset_prepared_path: /workspace/llm_training/axolotl/llama3-multilingual-orpo/prepared_mitsu_top25_borda
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val_set_size: 0.02
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output_dir: /workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_top25_borda
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sequence_len: 8192
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sample_packing: false
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pad_to_sequence_len: true
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use_wandb: true
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wandb_project: axolotl
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wandb_entity: peterd
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wandb_name: mitsu_top25_borda
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gradient_accumulation_steps: 8
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micro_batch_size: 1
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num_epochs: 1
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optimizer: paged_adamw_8bit
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lr_scheduler: cosine
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learning_rate: 8e-6
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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early_stopping_patience:
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resume_from_checkpoint:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_steps: 10
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evals_per_epoch: 20
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eval_table_size:
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saves_per_epoch: 1
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debug:
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deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
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weight_decay: 0.0
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special_tokens:
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pad_token: <|end_of_text|>
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```
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</details><br>
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# workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_top25_borda
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This model is a fine-tuned version of [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0818
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 8e-06
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- total_eval_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 7.6328 | 0.05 | 1 | 7.7812 |
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| 7.7158 | 0.1 | 2 | 7.2589 |
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| 7.2588 | 0.15 | 3 | 4.0580 |
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| 4.0068 | 0.19 | 4 | 2.4598 |
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| 2.4438 | 0.24 | 5 | 0.6504 |
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| 0.6586 | 0.29 | 6 | 0.1129 |
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| 0.1235 | 0.34 | 7 | 0.1066 |
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| 0.1273 | 0.39 | 8 | 0.1041 |
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| 0.1076 | 0.44 | 9 | 0.0987 |
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| 0.1009 | 0.48 | 10 | 0.0940 |
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| 0.1172 | 0.53 | 11 | 0.0885 |
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| 0.1016 | 0.58 | 12 | 0.0867 |
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| 0.1088 | 0.63 | 13 | 0.0859 |
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| 0.095 | 0.68 | 14 | 0.0846 |
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| 0.1101 | 0.73 | 15 | 0.0839 |
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| 0.0969 | 0.78 | 16 | 0.0832 |
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| 0.0864 | 0.82 | 17 | 0.0825 |
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| 0.0918 | 0.87 | 18 | 0.0821 |
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| 0.0927 | 0.92 | 19 | 0.0819 |
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| 0.0967 | 0.97 | 20 | 0.0818 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.0
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# How to cite
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```tex
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@article{devine2024sure,
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title={Are You Sure? Rank Them Again: Repeated Ranking For Better Preference Datasets},
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author={Devine, Peter},
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journal={arXiv preprint arXiv:2405.18952},
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year={2024}
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}
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```
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# Developer
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Peter Devine - ([ptrdvn](https://huggingface.co/ptrdvn))
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ModelHub XC