--- language: - en license: apache-2.0 datasets: - Intel/orca_dpo_pairs model-index: - name: mistral-7b-dpo-v6 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: 72.53 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mncai/mistral-7b-dpo-v6 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: 88.1 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mncai/mistral-7b-dpo-v6 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: 64.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mncai/mistral-7b-dpo-v6 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: 68.24 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mncai/mistral-7b-dpo-v6 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: 82.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mncai/mistral-7b-dpo-v6 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: 70.89 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mncai/mistral-7b-dpo-v6 name: Open LLM Leaderboard --- # Model Card for mncai/mistral-7b-dpo-v6 ### Introduction of MindsAndCompany https://mnc.ai/ We create various AI models and develop solutions that can be applied to businesses. And as for generative AI, we are developing products like Code Assistant, TOD Chatbot, LLMOps, and are in the process of developing Enterprise AGI (Artificial General Intelligence). ### Model Summary based mistral-7b, dpo tuned. ### Detail first step ties merge. ``` models: - model: AIDC-ai-business/Marcoroni-7B-v3 # no parameters necessary for base model - model: GreenNode/GreenNodeLM-7B-v1olet # psmathur/orca_mini_v3_13b parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: viethq188/LeoScorpius-7B-Chat-DPO parameters: density: 0.5 weight: [0, 0.3, 0.7, 1] # weight gradient - model: mncai/mistral-7b-dpo-v5 parameters: density: 0.33 weight: - filter: mlp value: 0.5 - value: 0 merge_method: ties base_model: AIDC-ai-business/Marcoroni-7B-v3 parameters: normalize: true int8_mask: true dtype: float16 ``` second step dpo. ```python # Training arguments training_args = TrainingArguments( per_device_train_batch_size=5, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-6, lr_scheduler_type="cosine", max_steps=1000, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) # Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, # peft_config=peft_config, beta=0.1, max_prompt_length=1024, max_length=2048, ) # Fine-tune model with DPO dpo_trainer.train() ``` ### How to Use Here give some examples of how to use our model. ```python from transformers import AutoConfig, AutoModel, AutoTokenizer import transformers import torch hf_model = 'mncai/mistral-7b-dpo-v6' message = "<|user|>\n두 개의 구가 있는데 각각 지름이 1, 2일때 구의 부피는 몇배 차이가 나지? 설명도 같이 해줘.\n<|assistant|>\n" sequences = pipeline( message, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=2048, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ### Warnings Currently, the leaderboard is overfitted. It is inevitable because, unlike Kaggle, where there's private scoring followed by the end of the competition, here the scores are continuously open. Even among my models, some received lower scores in internal data evaluations. mncai/agiin-13.6B-v0.1 > mncai/agiin-11.1B-v0.1 > mncai/mistral-7b-dpo-v6. However, on the leaderboard, mncai/mistral-7b-dpo-v6 has the highest score. When choosing a model to use on the open LLM leaderboard, it would be best to evaluate with your own private dataset that is not publicly available. ### Detect-Pretrain-Code-Contamination Result Share use https://github.com/Mihaiii/detect-pretrain-code-contamination DATASET=truthful_qa python src/run.py --target_model mncai/mistral-7b-dpo-v6  --data $DATASET --output_dir out/$DATASET --ratio_gen 0.4 result < 0.1, %: 0.76 ### Contact If you have any questions, please raise an issue or contact us at dwmyoung@mnc.ai # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__mistral-7b-dpo-v6) | Metric |Value| |---------------------------------|----:| |Avg. |74.50| |AI2 Reasoning Challenge (25-Shot)|72.53| |HellaSwag (10-Shot) |88.10| |MMLU (5-Shot) |64.68| |TruthfulQA (0-shot) |68.24| |Winogrande (5-shot) |82.56| |GSM8k (5-shot) |70.89|