101 lines
4.4 KiB
Markdown
101 lines
4.4 KiB
Markdown
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# Prepare Models
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To support the evaluation of new models in OpenCompass, there are several ways:
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1. HuggingFace-based models
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2. API-based models
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3. Custom models
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## HuggingFace-based Models
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In OpenCompass, we support constructing evaluation models directly from HuggingFace's
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`AutoModel.from_pretrained` and `AutoModelForCausalLM.from_pretrained` interfaces. If the model to be
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evaluated follows the typical generation interface of HuggingFace models, there is no need to write code. You
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can simply specify the relevant configurations in the configuration file.
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Here is an example configuration file for a HuggingFace-based model:
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```python
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# Use `HuggingFace` to evaluate models supported by AutoModel.
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# Use `HuggingFaceCausalLM` to evaluate models supported by AutoModelForCausalLM.
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from opencompass.models import HuggingFaceCausalLM
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models = [
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dict(
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type=HuggingFaceCausalLM,
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# Parameters for `HuggingFaceCausalLM` initialization.
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path='huggyllama/llama-7b',
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tokenizer_path='huggyllama/llama-7b',
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tokenizer_kwargs=dict(padding_side='left', truncation_side='left'),
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max_seq_len=2048,
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batch_padding=False,
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# Common parameters shared by various models, not specific to `HuggingFaceCausalLM` initialization.
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abbr='llama-7b', # Model abbreviation used for result display.
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max_out_len=100, # Maximum number of generated tokens.
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batch_size=16, # The size of a batch during inference.
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run_cfg=dict(num_gpus=1), # Run configuration to specify resource requirements.
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)
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]
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```
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Explanation of some of the parameters:
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- `batch_padding=False`: If set to False, each sample in a batch will be inferred individually. If set to True,
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a batch of samples will be padded and inferred together. For some models, such padding may lead to
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unexpected results. If the model being evaluated supports sample padding, you can set this parameter to True
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to speed up inference.
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- `padding_side='left'`: Perform padding on the left side. Not all models support padding, and padding on the
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right side may interfere with the model's output.
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- `truncation_side='left'`: Perform truncation on the left side. The input prompt for evaluation usually
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consists of both the in-context examples prompt and the input prompt. If the right side of the input prompt
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is truncated, it may cause the input of the generation model to be inconsistent with the expected format.
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Therefore, if necessary, truncation should be performed on the left side.
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During evaluation, OpenCompass will instantiate the evaluation model based on the `type` and the
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initialization parameters specified in the configuration file. Other parameters are used for inference,
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summarization, and other processes related to the model. For example, in the above configuration file, we will
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instantiate the model as follows during evaluation:
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```python
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model = HuggingFaceCausalLM(
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path='huggyllama/llama-7b',
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tokenizer_path='huggyllama/llama-7b',
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tokenizer_kwargs=dict(padding_side='left', truncation_side='left'),
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max_seq_len=2048,
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)
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```
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## API-based Models
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Currently, OpenCompass supports API-based model inference for the following:
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- OpenAI (`opencompass.models.OpenAI`)
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- More coming soon
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Let's take the OpenAI configuration file as an example to see how API-based models are used in the
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configuration file.
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```python
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from opencompass.models import OpenAI
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models = [
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dict(
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type=OpenAI, # Using the OpenAI model
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# Parameters for `OpenAI` initialization
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path='gpt-4', # Specify the model type
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key='YOUR_OPENAI_KEY', # OpenAI API Key
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max_seq_len=2048, # The max input number of tokens
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# Common parameters shared by various models, not specific to `OpenAI` initialization.
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abbr='GPT-4', # Model abbreviation used for result display.
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max_out_len=512, # Maximum number of generated tokens.
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batch_size=1, # The size of a batch during inference.
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run_cfg=dict(num_gpus=0), # Resource requirements (no GPU needed)
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),
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]
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```
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## Custom Models
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If the above methods do not support your model evaluation requirements, you can refer to
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[Supporting New Models](../advanced_guides/new_model.md) to add support for new models in OpenCompass.
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