Amu_-_spin-phi2-gguf

Quantization made by Richard Erkhov.

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spin-phi2 - GGUF

• Model creator: https://huggingface.co/Amu/
• Original model: https://huggingface.co/Amu/spin-phi2/

Original model description:

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

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outputs

This model is a fine-tuned version of microsoft/phi-2 using SPIN on ultrachat_200k dataset.

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.

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

Detailed results can be found here