license: gpl-3.0
model-index:
- name: 34b-beta
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 30.43
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CausalLM/34b-beta
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 36.68
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CausalLM/34b-beta
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 4.15
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CausalLM/34b-beta
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 12.86
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CausalLM/34b-beta
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 6.92
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CausalLM/34b-beta
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 48.06
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CausalLM/34b-beta
name: Open LLM Leaderboard
CausalLM 34B β
Demo: 
PROMPT FORMAT:
There are some issues with the model weights in terms of precision. In the next version update, we will roll back some progress and retrain to fix these issues as soon as possible.
Please note: Do not use "accelerated inference frameworks" like VLLM temporarily. Instead, use Transformers for inference. Otherwise, due to precision issues, the output quality will be significantly degraded. If you need faster inference, you can consider using the q8_0 quantization (faster and better than bf16 vllm for this model only) with llama.cpp temporarily or wait for the official version. To be fixed in the upcoming next version update.
no repetition_penalty!
Please do not use wikitext for quantization calibration because all wikitext have been re-aligned on synthetic dataset, and its distribution differs significantly from the original wikitext.
MT-Bench: 8.5
Some contamination detection if you want to check:
| Models | MMLU (ref: llama7b) | TBA |
|---|---|---|
| microsoft/Orca-2-7b | 0.77 | |
| mistralai/Mistral-7B-v0.1 | 0.46 | |
| CausalLM/34b-beta | 0.38 | |
| 01-ai/Yi-6B-200K | 0.3 |
data from https://huggingface.co/spaces/Yeyito/llm_contamination_detector
It should be safe. It was not trained on the benchmark, but the contamination of the training dataset is unavoidable due to cost constraints.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 23.18 |
| IFEval (0-Shot) | 30.43 |
| BBH (3-Shot) | 36.68 |
| MATH Lvl 5 (4-Shot) | 4.15 |
| GPQA (0-shot) | 12.86 |
| MuSR (0-shot) | 6.92 |
| MMLU-PRO (5-shot) | 48.06 |