--- license: apache-2.0 language: - ru - en base_model: - jinaai/jina-embeddings-v3 --- ## **JinaJudge: Proxy Judgement for Russian LLM Arena** ### **Description** This model is trained to replicate the judgement patterns of GPT-4-1106-Preview in the [Russian LLM Arena](https://huggingface.co./spaces/Vikhrmodels/arenahardlb), designed for faster and more cost-effective evaluation of language models. While the model's focus is on Russian LLM evaluation, it can also be used for English-centric models. --- ### **Model Details** This is a small upgrade to the [kaleinaNyan/jina-v3-rullmarena-judge](https://huggingface.co./kaleinaNyan/jina-v3-rullmarena-judge) model: - Number of decoder blocks increased from 4 to 5. - Hidden activations dimensionality reduced from 1024 to 512 (via a projection layer after JINA encoder). - The resulting model size went from 614M params to 589M params. - I also tweaked some training hyperparameters, but training data composition is the same. Surprisingly, these changes gave a tangible performance improvement, so I decided to upload the model. As it turned out (after evaluation on the train set), previous model was not expressive enough. --- ### **Evaluation** The validation process was based on **existing judgements** from the Russian LLM Arena, which were already available. These judgements were filtered and simplified to match the three-class structure used in training. NOTE: values in parenthesis show relative improvement compared to previous model. **Models evaluated**: - **gemma-2-9b-it-sppo-iter3** - **glm-4-9b-chat** - **gpt-3.5-turbo-1106** - **mistral-7b-instruct-v0.3** - **storm-7b** **Validation Performance**: - **Accuracy**: 80.76% (+2.67) - **Precision**: 78.56% (+2.74) - **Recall**: 79.48% (+2.71) - **F1-score**: 79.00% (+2.73) For the **test** phase, new judgements were generated using GPT-4 for the `kolibri-mistral-0427-upd` model. **Test Performance**: - **Accuracy**: 82.72% (+2.64) - **Precision**: 80.11% (+3.43) - **Recall**: 82.42% (+4.69) - **F1-score**: 81.18% (+4.10) --- ### **Usage Example** ```python from transformers import AutoModel jina = AutoModel.from_pretrained("kaleinaNyan/jina-v3-rullmarena-judge-300924", trust_remote_code=True) prompt_template = """ {user_prompt} {assistant_a} {assistant_b} """.strip() prompt = "your prompt" assistant_a = "assistant a response" assistant_b = "assistant b response" example = prompt_template.format( user_prompt=user_prompt, assistant_a=assistant_a, assistant_b=assistant_b, ) judgement = jina([example])[0].argmax() judgement_map = { 0: "A is better than B", 1: "A == B", 2: "B is better than A" } print(judgement_map[judgement]) ``` --- ### **Generated ranking** The ranking was obtained using a modified [Russian LLM Arena code](https://github.com/oKatanaaa/ru_llm_arena). All judgements were regenerated using the jina-judge model. | Model | Score | 95% CI | Average #Tokens | |--------------------------------------|-------|----------------------|-----------------| | gpt-4-1106-preview | 81.6 | (-2.3, 3.0) | 541 | | gpt-4.0-mini | 76.0 | (-2.7, 2.4) | 448 | | qwen-2.5-72b-it | 72.5 | (-3.6, 3.6) | 557 | | gemma-2-9b-it-sppo-iter3 | 72.1 | (-3.7, 3.6) | 569 | | gemma-2-27b-it | 71.1 | (-3.3, 3.2) | 482 | | gemma-2-9b-it | 70.8 | (-3.4, 3.5) | 569 | | t-lite-instruct-0.1 | 68.3 | (-3.8, 4.5) | 810 | | suzume-llama-3-8b-multilingual-orpo | 62.9 | (-3.9, 4.0) | 682 | | glm-4-9b-chat | 60.5 | (-3.9, 4.0) | 516 | | sfr-iterative-dpo-llama-3-8b-r | 59.9 | (-4.0, 4.3) | 682 | | c4ai-command-r-v01 | 56.9 | (-4.2, 3.8) | 516 | | phi-3-medium-4k-instruct | 56.4 | (-2.8, 3.3) | 566 | | mistral-nemo-instruct-2407 | 56.1 | (-2.9, 3.4) | 682 | | yandex_gpt_pro | 51.7 | (-3.4, 3.4) | 345 | | suzume-llama-3-8b-multilingual | 51.3 | (-3.4, 4.0) | 489 | | hermes-2-theta-llama-3-8b | 50.9 | (-3.2, 3.4) | 485 | | starling-1m-7b-beta | 50.2 | (-3.3, 3.4) | 495 | | gpt-3.5-turbo-0125 | 50.0 | (0.0, 0.0) | 220 | | llama-3-instruct-8b-sppo-iter3 | 49.8 | (-3.4, 4.0) | 763 | | llama-3-8b-saiga-suzume-ties | 48.2 | (-4.1, 3.9) | 569 | | llama-3-smaug-8b | 46.6 | (-3.9, 3.8) | 763 | | vikhr-it-5.4-fp16-orpo-v2 | 46.6 | (-3.7, 4.0) | 379 | | aya-23-8b | 46.3 | (-3.8, 3.9) | 571 | | saiga-llama3-8b_v6 | 45.5 | (-3.8, 3.9) | 471 | | vikhr-it-5.2-fp16-cp | 43.8 | (-3.9, 4.0) | 543 | | qwen2-7b-instruct | 43.7 | (-2.5, 2.7) | 492 | | opencchat-3.5-0106 | 43.4 | (-3.3, 3.7) | 485 | | gpt-3.5-turbo-1106 | 41.7 | (-2.9, 3.5) | 220 | | kolibri-mistral-0427-upd | 41.5 | (-3.2, 3.5) | 551 | | paralex-llama-3-8b-sft | 40.6 | (-3.8, 3.3) | 688 | | mistral-7b-instruct-v0.3 | 40.3 | (-3.3, 3.4) | 469 | | llama-3-instruct-8b-simpo | 40.2 | (-2.9, 3.7) | 551 | | gigachat_pro | 40.2 | (-3.2, 3.5) | 294 | | hermes-2-pro-llama-3-8b | 39.5 | (-2.9, 3.4) | 689 | | vikhr-it-5.3-fp16-32k | 39.5 | (-2.8, 3.2) | 519 | | opencchat-3.6-8b-2204522 | 37.7 | (-3.3, 3.7) | 409 | | meta-llama-3-8b-instruct | 37.5 | (-3.1, 3.5) | 450 | | kolibri-vikhr-mistral-0427 | 37.1 | (-3.1, 3.8) | 488 | | neural-chat-v3.3 | 36.5 | (-2.7, 3.6) | 523 | | vikhr-it-5.1-fp16 | 36.4 | (-3.5, 3.5) | 448 | | gigachat-lite | 36.0 | (-2.8, 3.0) | 523 | | saiga-7b | 25.9 | (-3.1, 3.7) | 927 | | storm-7b | 25.1 | (-3.6, 4.1) | 419 | | snorkel-mistral-pairrm-dpo | 16.5 | (-3.8, 3.2) | 773 |