metadata
language:
- en
license: mit
base_model:
- mistralai/Mistral-7B-v0.1
datasets:
- argilla/ultrafeedback-binarized-preferences-cleaned
pipeline_tag: text-generation
model-index:
- name: Mistral-ORPO-β
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
name: normalized accuracy
value: 61.18
source:
name: Open LLM Leaderboard
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
- 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
name: normalized accuracy
value: 84.03
source:
name: Open LLM Leaderboard
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
- 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: 47.69
source:
name: Open LLM Leaderboard
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
- 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
name: accuracy
value: 39.8
source:
name: Open LLM Leaderboard
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
- 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
name: accuracy
value: 63.26
source:
name: Open LLM Leaderboard
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
- 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
name: accuracy
value: 79.24
source:
name: Open LLM Leaderboard
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
- task:
type: text-generation
dataset:
name: AlpacaEval 1
type: AlpacaEval
metrics:
- type: AlpacaEval 1.0
value: 91.16%
name: Win Rate
source:
url: https://tatsu-lab.github.io/alpaca_eval/
name: Leaderboard
- task:
type: text-generation
dataset:
name: AlpacaEval 2
type: AlpacaEval
metrics:
- type: AlpacaEval 2.0
value: 12.57%
name: Win Rate
source:
url: https://tatsu-lab.github.io/alpaca_eval/
name: Leaderboard
- task:
type: text-generation
dataset:
name: MT-Bench
type: MT-Bench
metrics:
- type: MT-Bench
value: 7.322
name: Score
source:
url: https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/
name: self-reported
quantized_by: bartowski
Exllama v2 Quantizations of mistral-orpo-beta
Using turboderp's ExLlamaV2 v0.0.15 for quantization.
The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/kaist-ai/mistral-orpo-beta
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
|---|---|---|---|---|---|---|
| 8_0 | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| 6_5 | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, recommended. |
| 5_0 | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| 4_25 | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| 3_5 | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
Download instructions
With git:
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/mistral-orpo-beta-exl2 mistral-orpo-beta-exl2-6_5
With huggingface hub (credit to TheBloke for instructions):
pip3 install huggingface-hub
To download the main (only useful if you only care about measurement.json) branch to a folder called mistral-orpo-beta-exl2:
mkdir mistral-orpo-beta-exl2
huggingface-cli download bartowski/mistral-orpo-beta-exl2 --local-dir mistral-orpo-beta-exl2 --local-dir-use-symlinks False
To download from a different branch, add the --revision parameter:
Linux:
mkdir mistral-orpo-beta-exl2-6_5
huggingface-cli download bartowski/mistral-orpo-beta-exl2 --revision 6_5 --local-dir mistral-orpo-beta-exl2-6_5 --local-dir-use-symlinks False
Windows (which apparently doesn't like _ in folders sometimes?):
mkdir mistral-orpo-beta-exl2-6.5
huggingface-cli download bartowski/mistral-orpo-beta-exl2 --revision 6_5 --local-dir mistral-orpo-beta-exl2-6.5 --local-dir-use-symlinks False
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski