README.md

---
license: mit
model-index:
- name: BruinsV2-OpHermesNeu-11B
  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: 68.09
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ba2han/BruinsV2-OpHermesNeu-11B
      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: 84.7
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ba2han/BruinsV2-OpHermesNeu-11B
      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: 64.19
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ba2han/BruinsV2-OpHermesNeu-11B
      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: 62.76
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ba2han/BruinsV2-OpHermesNeu-11B
      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: 79.48
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ba2han/BruinsV2-OpHermesNeu-11B
      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: 60.05
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ba2han/BruinsV2-OpHermesNeu-11B
      name: Open LLM Leaderboard
---

|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.6527|±  |0.0139|
|             |       |acc_norm|0.6869|±  |0.0136|

**Warning! This model may or may not be contaminated [See discussion 474](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/474). What a shame. It still does perform well though**

A passthrough merge of OpenHermes-2.5-neural-chat-7b-v3-1 and Bruins-V2. To be updated.

Template: ChatML

My settings:

Temperature: 0.7-0.8

Min_p: 0.12

Top_K: 0

Repetition Penalty: 1.16

Mirostat Tau: 2.5-3

Mirostat Eta: 0.12

Personal Thoughts:

- The model sometimes throws wrong tags, you can add those to "Custom stopping strings" in Oobabooga.
- Output with Mirostat consistently felt smarter than a set Top_K rate.

Note: The model is hallucinating hard in chat mode for me in some instances, like writing adblocker messages. Kind of funny. 

I am not sure which dataset involved was poisoned.
# [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_Ba2han__BruinsV2-OpHermesNeu-11B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.88|
|AI2 Reasoning Challenge (25-Shot)|68.09|
|HellaSwag (10-Shot)              |84.70|
|MMLU (5-Shot)                    |64.19|
|TruthfulQA (0-shot)              |62.76|
|Winogrande (5-shot)              |79.48|
|GSM8k (5-shot)                   |60.05|