Language Models

Language models are used to generate agent responses to questions and can be specified when running a survey. API keys are required in order to access the available models, and should be stored in your private .env file. See the API Keys page for instructions on storing your API keys.

Available services

We can see all of the available services (model providers) by calling the services() method of the Model class:

from edsl import Model

Model.services()

This will return a list of the services we can choose from:

['openai', 'anthropic', 'deep_infra', 'google']

Available models

We can see all of the available models by calling the available() method of the Model class:

from edsl import Model

Model.available()

This will return a list of the models we can choose from:

[['01-ai/Yi-34B-Chat', 'deep_infra', 0],
['Austism/chronos-hermes-13b-v2', 'deep_infra', 1],
['Gryphe/MythoMax-L2-13b', 'deep_infra', 2],
['Gryphe/MythoMax-L2-13b-turbo', 'deep_infra', 3],
['HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1', 'deep_infra', 4],
['Phind/Phind-CodeLlama-34B-v2', 'deep_infra', 5],
['Qwen/Qwen2-72B-Instruct', 'deep_infra', 6],
['Qwen/Qwen2-7B-Instruct', 'deep_infra', 7],
['bigcode/starcoder2-15b', 'deep_infra', 8],
['bigcode/starcoder2-15b-instruct-v0.1', 'deep_infra', 9],
['claude-3-5-sonnet-20240620', 'anthropic', 10],
['claude-3-haiku-20240307', 'anthropic', 11],
['claude-3-opus-20240229', 'anthropic', 12],
['claude-3-sonnet-20240229', 'anthropic', 13],
['codellama/CodeLlama-34b-Instruct-hf', 'deep_infra', 14],
['codellama/CodeLlama-70b-Instruct-hf', 'deep_infra', 15],
['cognitivecomputations/dolphin-2.6-mixtral-8x7b', 'deep_infra', 16],
['databricks/dbrx-instruct', 'deep_infra', 17],
['deepinfra/airoboros-70b', 'deep_infra', 18],
['gemini-pro', 'google', 19],
['google/codegemma-7b-it', 'deep_infra', 20],
['google/gemma-1.1-7b-it', 'deep_infra', 21],
['gpt-3.5-turbo', 'openai', 22],
['gpt-3.5-turbo-0125', 'openai', 23],
['gpt-3.5-turbo-0301', 'openai', 24],
['gpt-3.5-turbo-0613', 'openai', 25],
['gpt-3.5-turbo-1106', 'openai', 26],
['gpt-3.5-turbo-16k', 'openai', 27],
['gpt-3.5-turbo-16k-0613', 'openai', 28],
['gpt-3.5-turbo-instruct', 'openai', 29],
['gpt-3.5-turbo-instruct-0914', 'openai', 30],
['gpt-4', 'openai', 31],
['gpt-4-0125-preview', 'openai', 32],
['gpt-4-0613', 'openai', 33],
['gpt-4-1106-preview', 'openai', 34],
['gpt-4-1106-vision-preview', 'openai', 35],
['gpt-4-turbo', 'openai', 36],
['gpt-4-turbo-2024-04-09', 'openai', 37],
['gpt-4-turbo-preview', 'openai', 38],
['gpt-4-vision-preview', 'openai', 39],
['gpt-4o', 'openai', 40],
['gpt-4o-2024-05-13', 'openai', 41],
['lizpreciatior/lzlv_70b_fp16_hf', 'deep_infra', 42],
['llava-hf/llava-1.5-7b-hf', 'deep_infra', 43],
['meta-llama/Llama-2-13b-chat-hf', 'deep_infra', 44],
['meta-llama/Llama-2-70b-chat-hf', 'deep_infra', 45],
['meta-llama/Llama-2-7b-chat-hf', 'deep_infra', 46],
['meta-llama/Meta-Llama-3-70B-Instruct', 'deep_infra', 47],
['meta-llama/Meta-Llama-3-8B-Instruct', 'deep_infra', 48],
['microsoft/Phi-3-medium-4k-instruct', 'deep_infra', 49],
['microsoft/WizardLM-2-7B', 'deep_infra', 50],
['microsoft/WizardLM-2-8x22B', 'deep_infra', 51],
['mistralai/Mistral-7B-Instruct-v0.1', 'deep_infra', 52],
['mistralai/Mistral-7B-Instruct-v0.2', 'deep_infra', 53],
['mistralai/Mistral-7B-Instruct-v0.3', 'deep_infra', 54],
['mistralai/Mixtral-8x22B-Instruct-v0.1', 'deep_infra', 55],
['mistralai/Mixtral-8x22B-v0.1', 'deep_infra', 56],
['mistralai/Mixtral-8x7B-Instruct-v0.1', 'deep_infra', 57],
['nvidia/Nemotron-4-340B-Instruct', 'deep_infra', 58],
['openchat/openchat-3.6-8b', 'deep_infra', 59],
['openchat/openchat_3.5', 'deep_infra', 60]]

Adding a model

Newly available models for these services are added automatically. A current list is also viewable at edsl.enums.LanguageModelType. If you do not see a publicly available model that you want to work with, please send us a feature request to add it or add it yourself by calling the add_model() method:

from edsl import Model

Model.add_model(service_name = "anthropic", model_name = "new_model")

This will add the model new_model to the anthropic service. You can then see the model in the list of available models, and search by service name:

Model.available("anthropic")

Output:

[['claude-3-5-sonnet-20240620', 'anthropic', 10],
['claude-3-haiku-20240307', 'anthropic', 11],
['claude-3-opus-20240229', 'anthropic', 12],
['claude-3-sonnet-20240229', 'anthropic', 13],
['new_model', 'anthropic', 61]]

Check models

We can check the models that for which we have already properly stored API keys by calling the check_models() method:

Model.check_models()

This will return a list of the available models and a confirmation message whether a valid key exists. The output will look like this (note that the keys are not shown):

Checking all available models...

Now checking: 01-ai/Yi-34B-Chat
OK!

Specifying a model

We specify a model to use with a survey by creating a Model object and passing it the name of an available model. We can optionally set other model parameters as well (temperature, etc.). For example, the following code creates a Model object for Claude 3.5 Sonnet with default model parameters:

from edsl import Model

model = Model('claude-3-5-sonnet-20240620')

We can see that the object consists of a model name and a dictionary of parameters:

model

This will show the default parameters of the model:

{
   "model": "claude-3-5-sonnet-20240620",
   "parameters": {
      "temperature": 0.5,
      "max_tokens": 1000,
      "top_p": 1,
      "frequency_penalty": 0,
      "presence_penalty": 0,
      "logprobs": false,
      "top_logprobs": 3
   }
}

Running a survey with models

Similar to how we specify Agents and Scenarios in running a survey, we specify the models to use by adding them to a survey with the by() method when the survey is run. We can pass either a single Model object or a list of models to the by() method. If multiple models are to be used they are passed as a list or as a ModelList object. For example, the following code specifies that a survey be run with each of GPT 4 and Gemini Pro:

from edsl import Model

models = [Model('gpt-4'), Model('gemini-pro')]

from edsl import Survey

survey = Survey.example()

results = survey.by(models).run()

This code uses ModelList instead of a list of Model objects:

from edsl import Model, ModelList

models = ModelList([Model('gpt-4'), Model('gemini-pro')])

from edsl import Survey

survey = Survey.example()

results = survey.by(models).run()

This will generate a result for each question in the survey with each model. If agents and/or scenarios are also specified, the responses will be generated for each combination of agents, scenarios and models. Each component is added with its own by() method, the order of which does not matter. The following commands are equivalent:

results = survey.by(scenarios).by(agents).by(models).run()

results = survey.by(models).by(agents).by(scenarios).run()

Default model

If no model is specified, a survey is automatically run with the default model (GPT 4) (if an API key for OpenAI has been stored). For example, the following code runs a survey with the default model (and no agents or scenarios) without needing to import the Model class:

from edsl import Survey

results = survey.run()

Inspecting model details in results

After running a survey, we can inspect the models used by calling the models method on the result object. For example, we can verify the default model when running a survey without specifying a model:

from edsl import Survey

survey = Survey.example()

results = survey.run()

results.models

This will return the following information about the default model that was used:

{
   "model": "gpt-4-1106-preview",
   "parameters": {
      "temperature": 0.5,
      "max_tokens": 1000,
      "top_p": 1,
      "frequency_penalty": 0,
      "presence_penalty": 0,
      "logprobs": false,
      "top_logprobs": 3
   }
}

To learn more about all the components of a Results object, please see the Results section.

Printing model attributes

If multiple models were used to generate results, we can print the attributes in a table. For example, the following code prints a table of the model names and temperatures for some results:

from edsl import Model

models = [Model('gpt-4-1106-preview'), Model('llama-2-70b-chat-hf')]

from edsl.questions import QuestionMultipleChoice, QuestionFreeText

q1 = QuestionMultipleChoice(
   question_name = "favorite_day",
   question_text = "What is your favorite day of the week?",
   question_options = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
)

q2 = QuestionFreeText(
   question_name = "favorite_color",
   question_text = "What is your favorite color?"
)

from edsl import Survey

survey = Survey([q1, q2])

results = survey.by(models).run()

results.select("model.model", "model.temperature").print()

The table will look like this:

model.model	model.temperature
gpt-4-1106-preview	0.5
llama-2-70b-chat-hf	0.5

We can also print model attributes together with other components of results. We can see a list of all components by calling the columns method on the results:

results.columns

For the above example, this will display the following list of components (note that no agents were specified, so there are no agent fields listed other than the default agent_name that is generated when a job is run):

['agent.agent_name',
'answer.favorite_color',
'answer.favorite_day',
'answer.favorite_day_comment',
'iteration.iteration',
'model.frequency_penalty',
'model.logprobs',
'model.max_new_tokens',
'model.max_tokens',
'model.model',
'model.presence_penalty',
'model.stopSequences',
'model.temperature',
'model.top_k',
'model.top_logprobs',
'model.top_p',
'prompt.favorite_color_system_prompt',
'prompt.favorite_color_user_prompt',
'prompt.favorite_day_system_prompt',
'prompt.favorite_day_user_prompt',
'raw_model_response.favorite_color_raw_model_response',
'raw_model_response.favorite_day_raw_model_response']

The following code will display a table of the model names together with the simulated answers:

(results
.select("model.model", "answer.favorite_day", "answer.favorite_color")
.print()
)