zsxkib
/
llm-prototype-model
- Public
- 2 runs
Run zsxkib/llm-prototype-model with an API
Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.
Input schema
The fields you can use to run this model with an API. If you don't give a value for a field its default value will be used.
| Field | Type | Default value | Description |
|---|---|---|---|
| prompt |
string
|
Give me a short introduction to large language model.
|
Input prompt
|
| system_prompt |
string
|
You are a helpful assistant.
|
System prompt
|
| max_new_tokens |
integer
|
512
Min: 1 Max: 32768 |
The maximum number of tokens to generate
|
| temperature |
number
|
1
Min: 0.1 Max: 5 |
Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.
|
| top_p |
number
|
1
Min: 0.01 Max: 1 |
When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens.
|
| top_k |
integer
|
1
|
When decoding text, samples from the top k most likely tokens; lower to ignore less likely tokens.
|
| repetition_penalty |
number
|
1
Min: 0.01 Max: 10 |
Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it.
|
| seed |
integer
|
The seed for the random number generator
|
{
"type": "object",
"title": "Input",
"properties": {
"seed": {
"type": "integer",
"title": "Seed",
"x-order": 7,
"description": "The seed for the random number generator"
},
"top_k": {
"type": "integer",
"title": "Top K",
"default": 1,
"x-order": 5,
"description": "When decoding text, samples from the top k most likely tokens; lower to ignore less likely tokens."
},
"top_p": {
"type": "number",
"title": "Top P",
"default": 1,
"maximum": 1,
"minimum": 0.01,
"x-order": 4,
"description": "When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens."
},
"prompt": {
"type": "string",
"title": "Prompt",
"default": "Give me a short introduction to large language model.",
"x-order": 0,
"description": "Input prompt"
},
"temperature": {
"type": "number",
"title": "Temperature",
"default": 1,
"maximum": 5,
"minimum": 0.1,
"x-order": 3,
"description": "Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value."
},
"system_prompt": {
"type": "string",
"title": "System Prompt",
"default": "You are a helpful assistant.",
"x-order": 1,
"description": "System prompt"
},
"max_new_tokens": {
"type": "integer",
"title": "Max New Tokens",
"default": 512,
"maximum": 32768,
"minimum": 1,
"x-order": 2,
"description": "The maximum number of tokens to generate"
},
"repetition_penalty": {
"type": "number",
"title": "Repetition Penalty",
"default": 1,
"maximum": 10,
"minimum": 0.01,
"x-order": 6,
"description": "Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it."
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
Schema
{
"type": "array",
"items": {
"type": "string"
},
"title": "Output",
"x-cog-array-type": "iterator",
"x-cog-array-display": "concatenate"
}