List models

GET

model

curl --request GET \
  --url https://aiapi.scope3.com/model \
  --header 'Authorization: Bearer <token>'

{
  "models": [
    {
      "id": "gpt-4-turbo",
      "aliases": [
        "claude-latest",
        "claude-3-sonnet-current"
      ],
      "name": "GPT-4 Turbo",
      "family": "gpt",
      "hugging_face_path": "EleutherAI/gpt-neo-2.7B",
      "benchmark_model_id": "GPTJ-6B",
      "total_params_billions": 175,
      "number_of_experts": 7,
      "params_per_expert_billions": 8,
      "tensor_parallelism": 1,
      "datatype": "fp8",
      "task": "text-generation",
      "training_usage_energy_kwh": 1013.1,
      "training_usage_emissions_kgco2e": 1013.1,
      "training_usage_water_l": 1013.1,
      "training_embodied_emissions_kgco2e": 11013.1,
      "training_embodied_water_l": 11013.1,
      "estimated_use_life_days": 1013.1,
      "estimated_requests_per_day": 1013.1,
      "fine_tuned_from_model_id": "llama_31_8b",
      "customer_id": 123,
      "created_at": "2023-11-07T05:31:56Z",
      "updated_at": "2023-11-07T05:31:56Z",
      "created_by": "<string>"
    }
  ]
}

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Query Parameters

family

enum<string>

Filter models by family

Available options:

claude,

gpt,

dall-e,

whisper,

gemini,

palm,

bert,

t5,

llama,

opt,

galactica,

phi,

stable-diffusion,

stable-lm,

mistral,

mixtral,

command,

embed,

falcon,

mpt,

pythia,

dolly,

bloom,

roberta,

gpt-neo,

gpt-j

Response

200

application/json

Model details retrieved successfully

models

object[]

required

models.id

string

required

Example:

"gpt-4-turbo"

models.aliases

string[]

required

List of aliases for this model; must be globally-unique with id

Example:

["claude-latest", "claude-3-sonnet-current"]

models.number_of_experts

integer

required

Example:

7

models.customer_id

integer

required

ID of the customer who owns this node (visible to admins only)

models.created_at

string

required

models.updated_at

string

required

models.created_by

string

required

ID of the user who created the node (admin or owner only)

models.name

string

Example:

"GPT-4 Turbo"

models.family

string

Example:

"gpt"

models.hugging_face_path

string

Example:

"EleutherAI/gpt-neo-2.7B"

models.benchmark_model_id

string

Example:

"GPTJ-6B"

models.total_params_billions

number

Example:

175

models.params_per_expert_billions

number

Example:

8

models.tensor_parallelism

integer

Example:

1

models.datatype

enum<string>

Available options:

fp8,

fp8-e4m3,

fp8-e5m2,

fp16,

tf32,

fp32,

fp64,

bfloat8,

bfloat16,

bf16,

int4,

int8,

int16,

int32,

int64,

uint4,

uint8,

uint16,

uint32,

uint64

models.task

enum<string>

Common types of AI/ML models and their primary functions:

Text-based models for natural language processing
Vision models for image analysis and generation
Audio models for speech and sound processing
Multimodal models that combine different types of inputs/outputs
Specialized models for specific use cases

Available options:

text-generation,

chat,

text-embedding,

text-classification,

sentiment-analysis,

named-entity-recognition,

question-answering,

summarization,

translation,

image-classification,

object-detection,

image-segmentation,

image-generation,

image-to-text,

text-to-image,

style-transfer,

face-detection,

facial-recognition,

speech-to-text,

text-to-speech,

speaker-identification,

audio-classification,

music-generation,

multimodal-embedding,

multimodal-generation,

visual-question-answering,

recommendation-system,

reinforcement-learning,

anomaly-detection,

time-series-forecasting,

clustering

models.training_usage_energy_kwh

number

Example:

1013.1

models.training_usage_emissions_kgco2e

number

Example:

1013.1

models.training_usage_water_l

number

Example:

1013.1

models.training_embodied_emissions_kgco2e

number

Example:

11013.1

models.training_embodied_water_l

number

Example:

11013.1

models.estimated_use_life_days

number

Example:

1013.1

models.estimated_requests_per_day

number

Example:

1013.1

models.fine_tuned_from_model_id

string

Example:

"llama_31_8b"

Calculate AI model impact metrics for BigQuery Create a model

curl --request GET \
  --url https://aiapi.scope3.com/model \
  --header 'Authorization: Bearer <token>'

{
  "models": [
    {
      "id": "gpt-4-turbo",
      "aliases": [
        "claude-latest",
        "claude-3-sonnet-current"
      ],
      "name": "GPT-4 Turbo",
      "family": "gpt",
      "hugging_face_path": "EleutherAI/gpt-neo-2.7B",
      "benchmark_model_id": "GPTJ-6B",
      "total_params_billions": 175,
      "number_of_experts": 7,
      "params_per_expert_billions": 8,
      "tensor_parallelism": 1,
      "datatype": "fp8",
      "task": "text-generation",
      "training_usage_energy_kwh": 1013.1,
      "training_usage_emissions_kgco2e": 1013.1,
      "training_usage_water_l": 1013.1,
      "training_embodied_emissions_kgco2e": 11013.1,
      "training_embodied_water_l": 11013.1,
      "estimated_use_life_days": 1013.1,
      "estimated_requests_per_day": 1013.1,
      "fine_tuned_from_model_id": "llama_31_8b",
      "customer_id": 123,
      "created_at": "2023-11-07T05:31:56Z",
      "updated_at": "2023-11-07T05:31:56Z",
      "created_by": "<string>"
    }
  ]
}

Guides

Integrations

API Reference

Authorizations

Query Parameters

Response