|
--- |
|
language: |
|
- en |
|
license: apache-2.0 |
|
tags: |
|
- gpt2 |
|
- dpo |
|
datasets: |
|
- HuggingFaceH4/ultrachat_200k |
|
- Intel/orca_dpo_pairs |
|
pipeline_tag: text-generation |
|
model-index: |
|
- name: chat_gpt2_dpo |
|
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: 23.98 |
|
name: normalized accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo |
|
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: 31.22 |
|
name: normalized accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo |
|
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: 24.95 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo |
|
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: 41.26 |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo |
|
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: 49.96 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo |
|
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: 0.0 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo |
|
name: Open LLM Leaderboard |
|
--- |
|
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 |
|
|
|
```python |
|
>>> 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 | |
|
|
|
```python |
|
{ |
|
"all": { |
|
"acc": 0.24915779048270345, |
|
"acc_stderr": 0.030509906389610868, |
|
"acc_norm": 0.25041231816215265, |
|
"acc_norm_stderr": 0.03132600249114931, |
|
"mc1": 0.2521419828641371, |
|
"mc1_stderr": 0.015201522246299965, |
|
"mc2": 0.41257163824244014, |
|
"mc2_stderr": 0.015127188811834062 |
|
}, |
|
"harness|arc:challenge|25": { |
|
"acc": 0.18686006825938567, |
|
"acc_stderr": 0.011391015649694391, |
|
"acc_norm": 0.23976109215017063, |
|
"acc_norm_stderr": 0.012476304127453954 |
|
}, |
|
"harness|hellaswag|10": { |
|
"acc": 0.28978291177056364, |
|
"acc_stderr": 0.004527343651130803, |
|
"acc_norm": 0.3121888070105557, |
|
"acc_norm_stderr": 0.0046243936909668975 |
|
}, |
|
"harness|hendrycksTest-abstract_algebra|5": { |
|
"acc": 0.22, |
|
"acc_stderr": 0.04163331998932268, |
|
"acc_norm": 0.22, |
|
"acc_norm_stderr": 0.04163331998932268 |
|
}, |
|
"harness|hendrycksTest-anatomy|5": { |
|
"acc": 0.3037037037037037, |
|
"acc_stderr": 0.039725528847851375, |
|
"acc_norm": 0.3037037037037037, |
|
"acc_norm_stderr": 0.039725528847851375 |
|
}, |
|
"harness|hendrycksTest-astronomy|5": { |
|
"acc": 0.17763157894736842, |
|
"acc_stderr": 0.031103182383123398, |
|
"acc_norm": 0.17763157894736842, |
|
"acc_norm_stderr": 0.031103182383123398 |
|
}, |
|
"harness|hendrycksTest-business_ethics|5": { |
|
"acc": 0.26, |
|
"acc_stderr": 0.0440844002276808, |
|
"acc_norm": 0.26, |
|
"acc_norm_stderr": 0.0440844002276808 |
|
}, |
|
"harness|hendrycksTest-clinical_knowledge|5": { |
|
"acc": 0.23018867924528302, |
|
"acc_stderr": 0.025907897122408173, |
|
"acc_norm": 0.23018867924528302, |
|
"acc_norm_stderr": 0.025907897122408173 |
|
}, |
|
"harness|hendrycksTest-college_biology|5": { |
|
"acc": 0.2569444444444444, |
|
"acc_stderr": 0.03653946969442099, |
|
"acc_norm": 0.2569444444444444, |
|
"acc_norm_stderr": 0.03653946969442099 |
|
}, |
|
"harness|hendrycksTest-college_chemistry|5": { |
|
"acc": 0.19, |
|
"acc_stderr": 0.039427724440366234, |
|
"acc_norm": 0.19, |
|
"acc_norm_stderr": 0.039427724440366234 |
|
}, |
|
"harness|hendrycksTest-college_computer_science|5": { |
|
"acc": 0.24, |
|
"acc_stderr": 0.04292346959909283, |
|
"acc_norm": 0.24, |
|
"acc_norm_stderr": 0.04292346959909283 |
|
}, |
|
"harness|hendrycksTest-college_mathematics|5": { |
|
"acc": 0.29, |
|
"acc_stderr": 0.04560480215720684, |
|
"acc_norm": 0.29, |
|
"acc_norm_stderr": 0.04560480215720684 |
|
}, |
|
"harness|hendrycksTest-college_medicine|5": { |
|
"acc": 0.2543352601156069, |
|
"acc_stderr": 0.0332055644308557, |
|
"acc_norm": 0.2543352601156069, |
|
"acc_norm_stderr": 0.0332055644308557 |
|
}, |
|
"harness|hendrycksTest-college_physics|5": { |
|
"acc": 0.21568627450980393, |
|
"acc_stderr": 0.04092563958237654, |
|
"acc_norm": 0.21568627450980393, |
|
"acc_norm_stderr": 0.04092563958237654 |
|
}, |
|
"harness|hendrycksTest-computer_security|5": { |
|
"acc": 0.34, |
|
"acc_stderr": 0.04760952285695236, |
|
"acc_norm": 0.34, |
|
"acc_norm_stderr": 0.04760952285695236 |
|
}, |
|
"harness|hendrycksTest-conceptual_physics|5": { |
|
"acc": 0.26382978723404255, |
|
"acc_stderr": 0.028809989854102973, |
|
"acc_norm": 0.26382978723404255, |
|
"acc_norm_stderr": 0.028809989854102973 |
|
}, |
|
"harness|hendrycksTest-econometrics|5": { |
|
"acc": 0.24561403508771928, |
|
"acc_stderr": 0.04049339297748142, |
|
"acc_norm": 0.24561403508771928, |
|
"acc_norm_stderr": 0.04049339297748142 |
|
}, |
|
"harness|hendrycksTest-electrical_engineering|5": { |
|
"acc": 0.2413793103448276, |
|
"acc_stderr": 0.03565998174135302, |
|
"acc_norm": 0.2413793103448276, |
|
"acc_norm_stderr": 0.03565998174135302 |
|
}, |
|
"harness|hendrycksTest-elementary_mathematics|5": { |
|
"acc": 0.24074074074074073, |
|
"acc_stderr": 0.02201908001221789, |
|
"acc_norm": 0.24074074074074073, |
|
"acc_norm_stderr": 0.02201908001221789 |
|
}, |
|
"harness|hendrycksTest-formal_logic|5": { |
|
"acc": 0.1349206349206349, |
|
"acc_stderr": 0.030557101589417515, |
|
"acc_norm": 0.1349206349206349, |
|
"acc_norm_stderr": 0.030557101589417515 |
|
}, |
|
"harness|hendrycksTest-global_facts|5": { |
|
"acc": 0.16, |
|
"acc_stderr": 0.03684529491774708, |
|
"acc_norm": 0.16, |
|
"acc_norm_stderr": 0.03684529491774708 |
|
}, |
|
"harness|hendrycksTest-high_school_biology|5": { |
|
"acc": 0.1774193548387097, |
|
"acc_stderr": 0.02173254068932927, |
|
"acc_norm": 0.1774193548387097, |
|
"acc_norm_stderr": 0.02173254068932927 |
|
}, |
|
"harness|hendrycksTest-high_school_chemistry|5": { |
|
"acc": 0.24630541871921183, |
|
"acc_stderr": 0.030315099285617736, |
|
"acc_norm": 0.24630541871921183, |
|
"acc_norm_stderr": 0.030315099285617736 |
|
}, |
|
"harness|hendrycksTest-high_school_computer_science|5": { |
|
"acc": 0.28, |
|
"acc_stderr": 0.04512608598542126, |
|
"acc_norm": 0.28, |
|
"acc_norm_stderr": 0.04512608598542126 |
|
}, |
|
"harness|hendrycksTest-high_school_european_history|5": { |
|
"acc": 0.21818181818181817, |
|
"acc_stderr": 0.03225078108306289, |
|
"acc_norm": 0.21818181818181817, |
|
"acc_norm_stderr": 0.03225078108306289 |
|
}, |
|
"harness|hendrycksTest-high_school_geography|5": { |
|
"acc": 0.3282828282828283, |
|
"acc_stderr": 0.03345678422756776, |
|
"acc_norm": 0.3282828282828283, |
|
"acc_norm_stderr": 0.03345678422756776 |
|
}, |
|
"harness|hendrycksTest-high_school_government_and_politics|5": { |
|
"acc": 0.37305699481865284, |
|
"acc_stderr": 0.03490205592048573, |
|
"acc_norm": 0.37305699481865284, |
|
"acc_norm_stderr": 0.03490205592048573 |
|
}, |
|
"harness|hendrycksTest-high_school_macroeconomics|5": { |
|
"acc": 0.26666666666666666, |
|
"acc_stderr": 0.02242127361292371, |
|
"acc_norm": 0.26666666666666666, |
|
"acc_norm_stderr": 0.02242127361292371 |
|
}, |
|
"harness|hendrycksTest-high_school_mathematics|5": { |
|
"acc": 0.21481481481481482, |
|
"acc_stderr": 0.025040443877000683, |
|
"acc_norm": 0.21481481481481482, |
|
"acc_norm_stderr": 0.025040443877000683 |
|
}, |
|
"harness|hendrycksTest-high_school_microeconomics|5": { |
|
"acc": 0.22268907563025211, |
|
"acc_stderr": 0.027025433498882364, |
|
"acc_norm": 0.22268907563025211, |
|
"acc_norm_stderr": 0.027025433498882364 |
|
}, |
|
"harness|hendrycksTest-high_school_physics|5": { |
|
"acc": 0.23178807947019867, |
|
"acc_stderr": 0.034454062719870546, |
|
"acc_norm": 0.23178807947019867, |
|
"acc_norm_stderr": 0.034454062719870546 |
|
}, |
|
"harness|hendrycksTest-high_school_psychology|5": { |
|
"acc": 0.3302752293577982, |
|
"acc_stderr": 0.02016446633634298, |
|
"acc_norm": 0.3302752293577982, |
|
"acc_norm_stderr": 0.02016446633634298 |
|
}, |
|
"harness|hendrycksTest-high_school_statistics|5": { |
|
"acc": 0.19444444444444445, |
|
"acc_stderr": 0.026991454502036733, |
|
"acc_norm": 0.19444444444444445, |
|
"acc_norm_stderr": 0.026991454502036733 |
|
}, |
|
"harness|hendrycksTest-high_school_us_history|5": { |
|
"acc": 0.25, |
|
"acc_stderr": 0.03039153369274154, |
|
"acc_norm": 0.25, |
|
"acc_norm_stderr": 0.03039153369274154 |
|
}, |
|
"harness|hendrycksTest-high_school_world_history|5": { |
|
"acc": 0.26582278481012656, |
|
"acc_stderr": 0.028756799629658342, |
|
"acc_norm": 0.26582278481012656, |
|
"acc_norm_stderr": 0.028756799629658342 |
|
}, |
|
"harness|hendrycksTest-human_aging|5": { |
|
"acc": 0.17937219730941703, |
|
"acc_stderr": 0.0257498195691928, |
|
"acc_norm": 0.17937219730941703, |
|
"acc_norm_stderr": 0.0257498195691928 |
|
}, |
|
"harness|hendrycksTest-human_sexuality|5": { |
|
"acc": 0.2366412213740458, |
|
"acc_stderr": 0.037276735755969174, |
|
"acc_norm": 0.2366412213740458, |
|
"acc_norm_stderr": 0.037276735755969174 |
|
}, |
|
"harness|hendrycksTest-international_law|5": { |
|
"acc": 0.35537190082644626, |
|
"acc_stderr": 0.04369236326573981, |
|
"acc_norm": 0.35537190082644626, |
|
"acc_norm_stderr": 0.04369236326573981 |
|
}, |
|
"harness|hendrycksTest-jurisprudence|5": { |
|
"acc": 0.25925925925925924, |
|
"acc_stderr": 0.042365112580946336, |
|
"acc_norm": 0.25925925925925924, |
|
"acc_norm_stderr": 0.042365112580946336 |
|
}, |
|
"harness|hendrycksTest-logical_fallacies|5": { |
|
"acc": 0.2822085889570552, |
|
"acc_stderr": 0.03536117886664742, |
|
"acc_norm": 0.2822085889570552, |
|
"acc_norm_stderr": 0.03536117886664742 |
|
}, |
|
"harness|hendrycksTest-machine_learning|5": { |
|
"acc": 0.32142857142857145, |
|
"acc_stderr": 0.04432804055291519, |
|
"acc_norm": 0.32142857142857145, |
|
"acc_norm_stderr": 0.04432804055291519 |
|
}, |
|
"harness|hendrycksTest-management|5": { |
|
"acc": 0.1941747572815534, |
|
"acc_stderr": 0.03916667762822585, |
|
"acc_norm": 0.1941747572815534, |
|
"acc_norm_stderr": 0.03916667762822585 |
|
}, |
|
"harness|hendrycksTest-marketing|5": { |
|
"acc": 0.2905982905982906, |
|
"acc_stderr": 0.02974504857267404, |
|
"acc_norm": 0.2905982905982906, |
|
"acc_norm_stderr": 0.02974504857267404 |
|
}, |
|
"harness|hendrycksTest-medical_genetics|5": { |
|
"acc": 0.25, |
|
"acc_stderr": 0.04351941398892446, |
|
"acc_norm": 0.25, |
|
"acc_norm_stderr": 0.04351941398892446 |
|
}, |
|
"harness|hendrycksTest-miscellaneous|5": { |
|
"acc": 0.23627075351213284, |
|
"acc_stderr": 0.015190473717037497, |
|
"acc_norm": 0.23627075351213284, |
|
"acc_norm_stderr": 0.015190473717037497 |
|
}, |
|
"harness|hendrycksTest-moral_disputes|5": { |
|
"acc": 0.24566473988439305, |
|
"acc_stderr": 0.02317629820399201, |
|
"acc_norm": 0.24566473988439305, |
|
"acc_norm_stderr": 0.02317629820399201 |
|
}, |
|
"harness|hendrycksTest-moral_scenarios|5": { |
|
"acc": 0.24581005586592178, |
|
"acc_stderr": 0.014400296429225587, |
|
"acc_norm": 0.24581005586592178, |
|
"acc_norm_stderr": 0.014400296429225587 |
|
}, |
|
"harness|hendrycksTest-nutrition|5": { |
|
"acc": 0.25163398692810457, |
|
"acc_stderr": 0.024848018263875195, |
|
"acc_norm": 0.25163398692810457, |
|
"acc_norm_stderr": 0.024848018263875195 |
|
}, |
|
"harness|hendrycksTest-philosophy|5": { |
|
"acc": 0.18006430868167203, |
|
"acc_stderr": 0.021823422857744953, |
|
"acc_norm": 0.18006430868167203, |
|
"acc_norm_stderr": 0.021823422857744953 |
|
}, |
|
"harness|hendrycksTest-prehistory|5": { |
|
"acc": 0.25617283950617287, |
|
"acc_stderr": 0.024288533637726095, |
|
"acc_norm": 0.25617283950617287, |
|
"acc_norm_stderr": 0.024288533637726095 |
|
}, |
|
"harness|hendrycksTest-professional_accounting|5": { |
|
"acc": 0.2801418439716312, |
|
"acc_stderr": 0.02678917235114023, |
|
"acc_norm": 0.2801418439716312, |
|
"acc_norm_stderr": 0.02678917235114023 |
|
}, |
|
"harness|hendrycksTest-professional_law|5": { |
|
"acc": 0.24837027379400262, |
|
"acc_stderr": 0.011035212598034503, |
|
"acc_norm": 0.24837027379400262, |
|
"acc_norm_stderr": 0.011035212598034503 |
|
}, |
|
"harness|hendrycksTest-professional_medicine|5": { |
|
"acc": 0.3125, |
|
"acc_stderr": 0.02815637344037142, |
|
"acc_norm": 0.3125, |
|
"acc_norm_stderr": 0.02815637344037142 |
|
}, |
|
"harness|hendrycksTest-professional_psychology|5": { |
|
"acc": 0.25, |
|
"acc_stderr": 0.01751781884501444, |
|
"acc_norm": 0.25, |
|
"acc_norm_stderr": 0.01751781884501444 |
|
}, |
|
"harness|hendrycksTest-public_relations|5": { |
|
"acc": 0.18181818181818182, |
|
"acc_stderr": 0.03694284335337801, |
|
"acc_norm": 0.18181818181818182, |
|
"acc_norm_stderr": 0.03694284335337801 |
|
}, |
|
"harness|hendrycksTest-security_studies|5": { |
|
"acc": 0.31020408163265306, |
|
"acc_stderr": 0.029613459872484378, |
|
"acc_norm": 0.31020408163265306, |
|
"acc_norm_stderr": 0.029613459872484378 |
|
}, |
|
"harness|hendrycksTest-sociology|5": { |
|
"acc": 0.24875621890547264, |
|
"acc_stderr": 0.030567675938916707, |
|
"acc_norm": 0.24875621890547264, |
|
"acc_norm_stderr": 0.030567675938916707 |
|
}, |
|
"harness|hendrycksTest-us_foreign_policy|5": { |
|
"acc": 0.25, |
|
"acc_stderr": 0.04351941398892446, |
|
"acc_norm": 0.25, |
|
"acc_norm_stderr": 0.04351941398892446 |
|
}, |
|
"harness|hendrycksTest-virology|5": { |
|
"acc": 0.19879518072289157, |
|
"acc_stderr": 0.03106939026078942, |
|
"acc_norm": 0.19879518072289157, |
|
"acc_norm_stderr": 0.03106939026078942 |
|
}, |
|
"harness|hendrycksTest-world_religions|5": { |
|
"acc": 0.29239766081871343, |
|
"acc_stderr": 0.034886477134579215, |
|
"acc_norm": 0.29239766081871343, |
|
"acc_norm_stderr": 0.034886477134579215 |
|
}, |
|
"harness|truthfulqa:mc|0": { |
|
"mc1": 0.2521419828641371, |
|
"mc1_stderr": 0.015201522246299965, |
|
"mc2": 0.41257163824244014, |
|
"mc2_stderr": 0.015127188811834062 |
|
}, |
|
"harness|winogrande|5": { |
|
"acc": 0.4996053670086819, |
|
"acc_stderr": 0.014052481306049512 |
|
}, |
|
"harness|gsm8k|5": { |
|
"acc": 0.0, |
|
"acc_stderr": 0.0 |
|
} |
|
} |
|
|
|
``` |
|
# [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_Sharathhebbar24__chat_gpt2_dpo) |
|
|
|
| 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| |
|
|
|
|
