---
language:
- en
license: mit
library_name: transformers
model-index:
- name: facebook-opt-125m-qcqa-ub-6-best-for-q-loss
  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: 23.29
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss
      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: 25.57
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss
      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: 23.15
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss
      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: 49.03
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss
      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: 49.17
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss
      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: 0.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=xformAI/facebook-opt-125m-qcqa-ub-6-best-for-q-loss
      name: Open LLM Leaderboard
---

This is a QCQA version of the original model facebook/opt-125m. In this version, the original MHA architecture is preserved but instead of having a single K/V head, different K/V heads corresponding to the same group have the same mean-pooled K or V values. It has upto 6 groups of KV heads per layer instead of original 12 KV heads in the MHA implementation. This implementation is supposed to more efficient than corresponding GQA one. This has been optimized for quality loss.
# [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_xformAI__facebook-opt-125m-qcqa-ub-6-best-for-q-loss)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |28.37|
|AI2 Reasoning Challenge (25-Shot)|23.29|
|HellaSwag (10-Shot)              |25.57|
|MMLU (5-Shot)                    |23.15|
|TruthfulQA (0-shot)              |49.03|
|Winogrande (5-shot)              |49.17|
|GSM8k (5-shot)                   | 0.00|