Generation

Generation Results¶

The trustllm toolkit currently supports the generation of over a dozen models. You can use the trustllm toolkit to generate output results for specified models on the trustllm benchmark.

Supported LLMs¶

Baichuan-13b
Baichuan2-13b
Yi-34b
ChatGLM2 - 6B
ChatGLM3 -6B
Vicuna-13b
Vicuna-7b
Vicuna-33b
Llama2-7b
Llama2-13b
Llama2-70b
Koala-13b
Oasst-12b
Wizardlm-13b
Mixtral-8x7B
Mistral-7b
Dolly-12b
bison-001-text
ERNIE
ChatGPT (gpt-3.5-turbo)
GPT-4
Claude-2
Gemini-pro
other LLMs in huggingface

Start Your Generation¶

The LLMGeneration class is designed for result generation, supporting the use of both local and online models. It is used for evaluating the performance of models in different tasks such as ethics, privacy, fairness, truthfulness, robustness, and safety.

Dataset

You should firstly download TrustLLM dataset (details) and the downloaded dataset dict has the following structure:

|-TrustLLM
    |-Safety
        |-Json_File_A
        |-Json_File_B
        ...
    |-Truthfulness
        |-Json_File_A
        |-Json_File_B
        ...
    ...

API setting:

If you need to evaluate an API LLM, please set the following API according to your requirements.

from trustllm import config

config.deepinfra_api = "deepinfra api"

config.claude_api = "claude api"

config.openai_key = "openai api"

config.palm_api = "palm api"

config.ernie_client_id = "ernie client id"

config.ernie_client_secret = "ernie client secret"

config.ernie_api = "ernie api"

Generation template:

from trustllm.generation.generation import LLMGeneration

llm_gen = LLMGeneration(
    model_path="your model name", 
    test_type="test section", 
    data_path="your dataset file path",
    model_name="", 
    online_model=False, 
    use_deepinfra=False,
    use_replicate=False,
    repetition_penalty=1.0,
    num_gpus=1, 
    max_new_tokens=512, 
    debug=False,
    device='cuda:0'
)

llm_gen.generation_results()

Args:

model_path (Required, str): Path to the local model. LLM list:

If you're using locally public model (huggingface) or use deepinfra online models:

'baichuan-inc/Baichuan-13B-Chat', 
'baichuan-inc/Baichuan2-13B-chat', 
'01-ai/Yi-34B-Chat', 
'THUDM/chatglm2-6b', 
'THUDM/chatglm3-6b', 
'lmsys/vicuna-13b-v1.3', 
'lmsys/vicuna-7b-v1.3', 
'lmsys/vicuna-33b-v1.3', 
'meta-llama/Llama-2-7b-chat-hf', 
'meta-llama/Llama-2-13b-chat-hf', 
'TheBloke/koala-13B-HF', 
'OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5', 
'WizardLM/WizardLM-13B-V1.2', 
'mistralai/Mixtral-8x7B-Instruct-v0.1', 
'meta-llama/Llama-2-70b-chat-hf', 
'mistralai/Mistral-7B-Instruct-v0.1', 
'databricks/dolly-v2-12b', 
'bison-001', 
'ernie', 
'chatgpt', 
'gpt-4', 
'claude-2'
... (other LLMs in huggingface)

If you're using use online models in replicate, You can find model_path in this link:

'meta/llama-2-70b-chat',
'meta/llama-2-13b-chat',
'meta/llama-2-7b-chat',
'mistralai/mistral-7b-instruct-v0.1',
'replicate/vicuna-13b',
... (other LLMs in replicate)

test_type (Required, str): Type of evaluation task, including 'robustness', 'truthfulness', 'fairness', 'ethics', 'safety', 'privacy'.
data_path (Required, str): Path to the root dataset, default is 'TrustLLM'.
online_model (Optional, bool): Whether to use an online model, default is False.
use_deepinfra (Optional, bool): Whether to use an online model in deepinfra, default is False. (Only work when oneline_model=True)
usr_replicate (Optional, bool): Whether to use an online model in replicate, default is False. (Only work when oneline_model=True)
repetition_penalty (Optional, float): Repetition penalty setting, default is 1.0.
num_gpus (Optional, int): Number of GPUs to use, default is 1.
max_new_tokens (Optional, int): Maximum number of new tokens in generated text, default is 512.
debug (Optional, bool): Enable debug mode, default is False.
device (Optional, str): Specify the device to use, default is 'cuda:0'.

Here is a usage example:

from trustllm.generation.generation import LLMGeneration

llm_gen = LLMGeneration(
    model_path="meta-llama/Llama-2-7b-chat-hf", 
    test_type="safety", 
    data_path="TrustLLM"
)

llm_gen.generation_results()

Don't have enough computing resource?¶

If you don't have sufficient computing resources to run HuggingFace models locally, we recommend using online models. We provide an online model interface through deepinfra or replicate.

Currently supported online models in deepinfra include:

llama2-70b
mistral-7b
dolly-12b
llama2-13b
mixtral-8x7B
yi-34b

Currently supported online models in replicate can be found in this link.

Before using online model to generate your results, you should set your deepinfra API or replicate API:

from trustllm import config

config.deepinfra_api = "deepinfra api"

config.replicate_api = "replicate api"

Then, you should set oneline_model=True:

from trustllm.generation.generation import LLMGeneration

llm_gen = LLMGeneration(
    model_path="meta-llama/Llama-2-7b-chat-hf", 
    test_type="safety", 
    data_path="TrustLLM",
    online_model=True
)

llm_gen.generation_results()

Temperature Setting¶

We categorize the tasks in the benchmark into two main groups: Generation and Classification.

We employ a temperature setting of 0 for classification tasks to ensure more precise outputs. Conversely, for generation tasks, we set the temperature to 1, fostering a more diverse range of results and exploring potential worst-case scenarios.

The temperature setting of each dataset file is as follows:

file_config = {
        "disparagement.json": 1.0,
        "preference_force.json": 1.0,
        "preference_plain.json": 1.0,
        "stereotype_agreement.json": 1.0,
        "stereotype_stereoset_classification.json": 0.0,
        "stereotype_query_test.json":1.0,
        "stereotype_recognition.json":0.0,
        "external.json": 0.0,
        "hallucination.json": 0.0,
        "golden_advfactuality.json": 1.0,
        "internal.json": 1.0,
        "sycophancy.json": 1.0,
        "ood_detection.json":1.0,
        "ood_generalization.json":0.0,
        "AdvGLUE.json":0.0,
        "AdvInstruction.json":1.0,
        "jailbreak.json":1.0,
        "exaggerated_safety.json": 1.0,
        "misuse.json":1.0,
        "privacy_awareness_confAIde.json":0.0,
        "privacy_awareness_query.json": 1.0,
        "privacy_leakage.json": 1.0,
        "awareness.json": 0.0,
        "implicit_ETHICS.json": 0.0,
        "implicit_SocialChemistry101.json": 0.0
    }