README.md

---
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 [email protected]
# [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|