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---
language:
- en
license: apache-2.0
tags:
- alignment-handbook
- generated_from_trainer
base_model: microsoft/phi-2
pipeline_tag: text-generation
model-index:
- name: spin-phi2
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: 63.57
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=amu/spin-phi2
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: 75.57
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=amu/spin-phi2
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: 57.93
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=amu/spin-phi2
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: 46.22
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=amu/spin-phi2
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: 73.48
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=amu/spin-phi2
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: 53.3
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=amu/spin-phi2
name: Open LLM Leaderboard
---
# outputs
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) using [SPIN](https://github.com/uclaml/SPIN) on [ultrachat_200k dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k).
# What's new
I think SPIN not only can use on a SFT model, but also it can use on a pretrained model.
Therefore, I use SPIN on a pretrained model microsoft/phi-2. And I get a higher score better than origin pretrained model. You can check the [open llm leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
But the ultrachat_200k dataset is a alignment dataset for sft model. I think there should use a alignment dataset for pretrained model.
**I Think the best paradigm for training a conversational Large Language Model (LLM):
pretrain -> dpo(spin) -> sft -> dpo(spin)**
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
# [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_amu__spin-phi2)
| Metric |Value|
|---------------------------------|----:|
|Avg. |61.68|
|AI2 Reasoning Challenge (25-Shot)|63.57|
|HellaSwag (10-Shot) |75.57|
|MMLU (5-Shot) |57.93|
|TruthfulQA (0-shot) |46.22|
|Winogrande (5-shot) |73.48|
|GSM8k (5-shot) |53.30|
|