language:
- en
license: apache-2.0
tags:
- human feedback
- rlhf
- preferences
- alignment
- HALO
- halos
- dpo
- rl
datasets:
- stanfordnlp/SHP
- Anthropic/hh-rlhf
- OpenAssistant/oasst1
metrics:
- accuracy
model-index:
- name: archangel_sft-kto_llama13b
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: 56.14
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ContextualAI/archangel_sft-kto_llama13b
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: 80.8
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ContextualAI/archangel_sft-kto_llama13b
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: 47.84
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ContextualAI/archangel_sft-kto_llama13b
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: 39.42
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ContextualAI/archangel_sft-kto_llama13b
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: 76.16
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ContextualAI/archangel_sft-kto_llama13b
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: 16.83
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ContextualAI/archangel_sft-kto_llama13b
name: Open LLM Leaderboard
This repo contains the model checkpoints for:
- model family llama13b
- optimized with the loss SFT+KTO
- aligned using the SHP, Anthropic HH and Open Assistant datasets.
To prompt Archangel models, ensure that the format is consistent with that of TuluV2.
For example, a prompt should be formatted as follows, where <|user|> corresponds to the human's role and <|assistant|> corresponds to the LLM's role.
The human should speak first:
<|user|>
Hi! I'm looking for a cake recipe.
<|assistant|>
What kind of cake?
<|user|>
Chocolate cake.
<|assistant|>
Note that a beginning-of-sequence (BOS) token is automatically added by all Archangel models during tokenization and does not have to be added by you. No end-of-sequence (EOS) token is added to the prompt.
For models trained with our conditional SFT model, the tokenizers have additional tokens <|good|> and <|bad|> included in the embeddings.
To generate with these control tokens in the context, postpend either to the prompt.
Please refer to our code repository or blog which contains intructions for training your own HALOs and links to our model cards.
If you find this repo or the technical paper useful in your research, please feel free to cite our work:
@techreport{ethayarajh2023halos,
author = {Ethayarajh, Kawin and Xu, Winnie, and Jurafsky, Dan and Kiela, Douwe},
title = {Human-Centered Loss Functions (HALOs)},
institution = {Contextual AI},
note = {https://github.com/ContextualAI/HALOs/blob/main/assets/report.pdf},
year = {2023},
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 52.87 |
| AI2 Reasoning Challenge (25-Shot) | 56.14 |
| HellaSwag (10-Shot) | 80.80 |
| MMLU (5-Shot) | 47.84 |
| TruthfulQA (0-shot) | 39.42 |
| Winogrande (5-shot) | 76.16 |
| GSM8k (5-shot) | 16.83 |