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---
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|
|