POST
/
model
curl --request POST \
  --url https://aiapi.scope3.com/model \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "id": "gpt-4-turbo",
  "name": "GPT-4 Turbo",
  "family": "gpt",
  "hugging_face_path": "EleutherAI/gpt-neo-2.7B",
  "benchmark_model_id": "gpt-4-turbo",
  "estimated_use_life_days": 365,
  "estimated_requests_per_day": 1000,
  "fine_tuned_from_model_id": "gpt-4-turbo",
  "aliases": [
    "claude-latest"
  ],
  "total_params_billions": 175,
  "number_of_experts": 7,
  "params_per_expert_billions": 8,
  "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
}'
{
  "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.

Body

application/json

Create a new model

​
id
string
required
Example:

"gpt-4-turbo"

​
number_of_experts
integer
required
Example:

7

​
name
string
Example:

"GPT-4 Turbo"

​
family
string
Example:

"gpt"

​
hugging_face_path
string
Example:

"EleutherAI/gpt-neo-2.7B"

​
benchmark_model_id
string
Example:

"gpt-4-turbo"

​
estimated_use_life_days
number
Example:

365

​
estimated_requests_per_day
number
Example:

1000

​
fine_tuned_from_model_id
string
Example:

"gpt-4-turbo"

​
aliases
string[]
Example:
["claude-latest"]
​
total_params_billions
number
Example:

175

​
params_per_expert_billions
number
Example:

8

​
training_usage_energy_kwh
number
Example:

1013.1

​
training_usage_emissions_kgco2e
number
Example:

1013.1

​
training_usage_water_l
number
Example:

1013.1

​
training_embodied_emissions_kgco2e
number
Example:

11013.1

​
training_embodied_water_l
number
Example:

11013.1

Response

201
application/json
Model created successfully
​
id
string
required
Example:

"gpt-4-turbo"

​
aliases
string[]
required

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

Example:
["claude-latest", "claude-3-sonnet-current"]
​
number_of_experts
integer
required
Example:

7

​
customer_id
integer
required

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

​
created_at
string
required
​
updated_at
string
required
​
created_by
string
required

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

​
name
string
Example:

"GPT-4 Turbo"

​
family
string
Example:

"gpt"

​
hugging_face_path
string
Example:

"EleutherAI/gpt-neo-2.7B"

​
benchmark_model_id
string
Example:

"GPTJ-6B"

​
total_params_billions
number
Example:

175

​
params_per_expert_billions
number
Example:

8

​
tensor_parallelism
integer
Example:

1

​
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
​
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
​
training_usage_energy_kwh
number
Example:

1013.1

​
training_usage_emissions_kgco2e
number
Example:

1013.1

​
training_usage_water_l
number
Example:

1013.1

​
training_embodied_emissions_kgco2e
number
Example:

11013.1

​
training_embodied_water_l
number
Example:

11013.1

​
estimated_use_life_days
number
Example:

1013.1

​
estimated_requests_per_day
number
Example:

1013.1

​
fine_tuned_from_model_id
string
Example:

"llama_31_8b"