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chat_gpt2_dpo
Sharathhebbar24/chat_gpt2_dpo
chat_gpt2_dpo is a text generation model from Sharathhebbar24. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
This model is a finetuned version of using on DPO
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.safetensors498 MB · 99%
At a glance
- Task
- Text Generation
- Library
- transformers
- License
- apache-2.0
- Model type
- gpt2
- Access
- Public
- Created
- Jan 24, 2024
- Updated
- May 9, 2024
- SHA
- f4a41f2c
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Model card
From the Hugging Face model README
This model is a finetuned version of Sharathhebbar24/chat_gpt2 using Intel/orca_dpo_pairs on DPO
Model description
GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the
predictions for the token i only use the inputs from 1 to i but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a prompt.
To use this model
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model_name = "Sharathhebbar24/chat_gpt2_dpo"
>>> model = AutoModelForCausalLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> def generate_text(prompt):
>>> inputs = tokenizer.encode(prompt, return_tensors='pt')
>>> outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
>>> generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>> return generated[:generated.rfind(".")+1]
>>> prompt = """
>>> user: what are you?
>>> assistant: I am a Chatbot intended to give a python program
>>> user: hmm, can you write a python program to print Hii Heloo
>>> assistant: Sure Here is a python code.\n print("Hii Heloo")
>>> user: Can you write a Linear search program in python
>>> """
>>> res = generate_text(prompt)
>>> res
Benchmark / Evaluation
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8k |
|---|---|---|---|---|---|---|---|
| Sharathhebbar24/chat_gpt2_dpo | 28.56 | 23.98 | 31.22 | 24.95 | 41.26 | 49.96 | 0 |
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Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.56 |
| AI2 Reasoning Challenge (25-Shot) | 23.98 |
| HellaSwag (10-Shot) | 31.22 |
| MMLU (5-Shot) | 24.95 |
| TruthfulQA (0-shot) | 41.26 |
| Winogrande (5-shot) | 49.96 |
| GSM8k (5-shot) | 0.00 |
- Task
- Text Generation
- Library
- transformers
- Type
- gpt2
- License
- apache-2.0
- Languages
- en
- Created
- Jan 24, 2024
- Updated
- May 9, 2024