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--- |
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license: mit |
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base_model: vicgalle/gpt2-open-instruct-v1 |
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tags: |
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- generated_from_trainer |
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- Transformers |
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- GPT2 |
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model-index: |
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- name: hh-rlhf |
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results: [] |
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datasets: |
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- Anthropic/hh-rlhf |
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- hakurei/open-instruct-v1 |
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tokenizers: |
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- GPT2Tokenizer |
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language: |
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- en |
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library_name: transformers |
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metrics: |
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- bleu |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# hh-rlhf |
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This model is a fine-tuned version of [vicgalle/gpt2-open-instruct-v1](https://huggingface.co/vicgalle/gpt2-open-instruct-v1) on an subset (15k) of the Anthropic/hh-rlhf dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1534 |
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This model responds to the 'Human:' or 'Assistant:' prompt pretty well in conversation situations. |
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The shorter responses are better suited. Keep generation length to a reasonable subset. Left to its own devices it will have some pretty esoteric responses. |
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These include fairly uncensored remarks and at times violent outbursts. Especially if asking questions. |
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Needs vetting for other textual uses. |
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``` |
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Human: Insane clown posse says... |
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Human: Should we look for a woman? |
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Assistant: It’s okay if you’re having a tough time finding what you are looking for. It’s a common question people might come up with for an argument or misunderstanding. What are you looking for, and what kind of woman would you have? |
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Human: Are you trying to find someone to argue |
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``` |
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## Model description |
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GPT2 open instruct was trained on the open-instruct dataset fully. The reimagines one LM head as a partial rhlf adapter, with subtle reinforcements. |
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## Intended uses & limitations |
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Intended to study the intersection of instruct models and prompting that focuses on subtle exchanges of prompting. This probably needs to be refined substantially at this point. |
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## Training and evaluation data |
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```python |
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Train dataset size: 15000 |
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Test dataset size: 500 |
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Dataset({ |
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features: ['chosen', 'rejected'], |
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num_rows: 15000 |
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}) |
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Dataset({ |
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features: ['chosen', 'rejected'], |
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num_rows: 500 |
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}) |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 2.3108 | 1.0 | 7500 | 2.1799 | |
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| 2.265 | 2.0 | 15000 | 2.1632 | |
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| 2.2507 | 3.0 | 22500 | 2.1567 | |
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| 2.2519 | 4.0 | 30000 | 2.1534 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
