---
license: apache-2.0
model-index:
- name: COKAL-v1-70B
  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: 87.46
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/COKAL-v1-70B
      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: 83.29
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/COKAL-v1-70B
      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: 68.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/COKAL-v1-70B
      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: 72.79
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/COKAL-v1-70B
      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: 80.27
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/COKAL-v1-70B
      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: 39.27
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=DopeorNope/COKAL-v1-70B
      name: Open LLM Leaderboard
---
# **🐻‍❄️COKAL-v1_70B🐻‍❄️**  
![img](./COKAL-DPO_bear.png)  

## Model Details

**Model Developers** Seungyoo Lee (DopeorNope)

**Input** Models input text only.

**Output** Models generate text only.

**Model Architecture**  
COKAL-v1_70B is an auto-regressive 70B language model based on the LLaMA2 transformer architecture.

**Base Model**  



**Training Dataset**  

- SFT training dataset: [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)

  
**Training**  
I developed the model in an environment with A100 x 8 


# Implementation Code
```python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "DopeorNope/COKAL-v1_70B"
model = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
model_tokenizer = AutoTokenizer.from_pretrained(repo)
```

---
# [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_DopeorNope__COKAL-v1-70B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |71.87|
|AI2 Reasoning Challenge (25-Shot)|87.46|
|HellaSwag (10-Shot)              |83.29|
|MMLU (5-Shot)                    |68.13|
|TruthfulQA (0-shot)              |72.79|
|Winogrande (5-shot)              |80.27|
|GSM8k (5-shot)                   |39.27|