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