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Model: jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf
Source: Original Platform

README.md

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+---
+license: mit
+base_model: vicgalle/gpt2-open-instruct-v1
+tags:
+- generated_from_trainer
+- Transformers
+- GPT2
+model-index:
+- name: hh-rlhf
+  results: []
+datasets:
+- Anthropic/hh-rlhf
+- hakurei/open-instruct-v1
+tokenizers:
+- GPT2Tokenizer
+language:
+- en
+library_name: transformers
+metrics:
+- bleu
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# hh-rlhf
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 2.1534
+
+This model responds to the 'Human:' or 'Assistant:' prompt pretty well in conversation situations. 
+
+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. 
+
+These include fairly uncensored remarks and at times violent outbursts. Especially if asking questions. 
+
+Needs vetting for other textual uses.
+
+```
+Human: Insane clown posse says...
+
+Human: Should we look for a woman?
+
+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?
+
+Human: Are you trying to find someone to argue
+```
+## Model description
+
+GPT2 open instruct was trained on the open-instruct dataset fully. The reimagines one LM head as a partial rhlf adapter, with subtle reinforcements. 
+## Intended uses & limitations
+
+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.
+
+## Training and evaluation data
+```python
+Train dataset size: 15000
+Test dataset size: 500
+Dataset({
+    features: ['chosen', 'rejected'],
+    num_rows: 15000
+})
+Dataset({
+    features: ['chosen', 'rejected'],
+    num_rows: 500
+})
+```
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0005
+- train_batch_size: 2
+- eval_batch_size: 1
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 4
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss |
+|:-------------:|:-----:|:-----:|:---------------:|
+| 2.3108        | 1.0   | 7500  | 2.1799          |
+| 2.265         | 2.0   | 15000 | 2.1632          |
+| 2.2507        | 3.0   | 22500 | 2.1567          |
+| 2.2519        | 4.0   | 30000 | 2.1534          |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3