---
license: apache-2.0
model-index:
- name: Delexa-Instruct-V0.1-7b
  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: 66.38
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b
      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: 85.9
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b
      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: 63.79
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b
      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: 61.73
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b
      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: 78.37
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b
      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: 62.93
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b
      name: Open LLM Leaderboard
---
## Delexa-V0.1-Instruct-7b: Our Newest and Best Model Yet!

We are excited to announce the release of Delexa-V0.1-Instruct-7b, our newest and best model yet! Delexa-V0.1-Instruct-7b has shown excellent performance on a variety of tasks, and we are confident that it will be a valuable asset to the research community.

### Eval Results

Delexa-V0.1-Instruct-7b was evaluated on a dataset of question-answer pairs. The model was given a single question and three different answer choices, and it was tasked with selecting the best answer. Delexa-V0.1-Instruct-7b achieved an average score of 8.27 on this task.

Here is a table showing the detailed eval results:

| Model | Turn 1 | Turn 2 | Average |
|---|---|---|---|
| gpt-4 | 8.95625 | 9.0250 | 8.990625 |
| Delexa-V0.1-Instruct-7b | 8.57500 | 7.9500 | 8.268750 |
| claude-v1 | 8.15000 | 7.6500 | 7.900000 |
| gpt-3.5-turbo | 8.07500 | 7.8125 | 7.943750 |
| vicuna-13b-v1.3 | 6.81250 | 5.9625 | 6.387500 |
| palm-2-chat-bison-001 | 6.71250 | 6.0875 | 6.400000 |

### Technique

One of the key factors that contributed to Delexa-V0.1-Instruct-7b's success is the technique of training the model with one question and three different answers. This technique allows the model to take into account different perspectives and viewpoints, which leads to more robust and accurate results.

### Future Work

We are excited to continue working on Delexa and to see how it can be further improved. We are currently working on an Instruct model, which is a type of model that can be fine-tuned on specific tasks. We believe that Instruct models have the potential to be even more powerful than Delexa-V0.1-7b, and we are eager to see the results of our ongoing research.

We would like to thank the entire team for their hard work on Delexa-V0.1-Instruct-7b. We are confident that this model will be a valuable asset to the research community.

### Guardrails:

This Model allows 18+ content and lewd content, but it wont let any illegal content through (unless you jailbreak it).

### Support Our Work and Join our Community!

[Our Patreon](https://patreon.com/Lex_Hue?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink)

[Our Twitter](https://twitter.com/lex_hue)

# [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_lex-hue__Delexa-Instruct-V0.1-7b)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.85|
|AI2 Reasoning Challenge (25-Shot)|66.38|
|HellaSwag (10-Shot)              |85.90|
|MMLU (5-Shot)                    |63.79|
|TruthfulQA (0-shot)              |61.73|
|Winogrande (5-shot)              |78.37|
|GSM8k (5-shot)                   |62.93|