yunzai-cc/law2
Run yunzai-cc/law2 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
|
Prompt to send to Llama v2.
|
|
| max_new_tokens |
integer
|
500
Min: 1 |
Maximum number of tokens to generate. A word is generally 2-3 tokens
|
| temperature |
number
|
0.5
Min: 0.01 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
|
40
Min: 1 Max: 100 |
When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens
|
| repetition_penalty |
number
|
1
Min: 0.01 Max: 5 |
Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it.
|
| num_beams |
integer
|
4
Min: 1 |
Maximum number of tokens to generate. A word is generally 2-3 tokens
|
{
"type": "object",
"title": "Input",
"required": [
"prompt"
],
"properties": {
"top_k": {
"type": "integer",
"title": "Top K",
"default": 40,
"maximum": 100,
"minimum": 1,
"x-order": 4,
"description": "When decoding text, samples from the top p percentage of 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": 3,
"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",
"x-order": 0,
"description": "Prompt to send to Llama v2."
},
"num_beams": {
"type": "integer",
"title": "Num Beams",
"default": 4,
"minimum": 1,
"x-order": 6,
"description": "Maximum number of tokens to generate. A word is generally 2-3 tokens"
},
"temperature": {
"type": "number",
"title": "Temperature",
"default": 0.5,
"maximum": 5,
"minimum": 0.01,
"x-order": 2,
"description": "Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value."
},
"max_new_tokens": {
"type": "integer",
"title": "Max New Tokens",
"default": 500,
"minimum": 1,
"x-order": 1,
"description": "Maximum number of tokens to generate. A word is generally 2-3 tokens"
},
"repetition_penalty": {
"type": "number",
"title": "Repetition Penalty",
"default": 1,
"maximum": 5,
"minimum": 0.01,
"x-order": 5,
"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.
{
"type": "array",
"items": {
"type": "string"
},
"title": "Output",
"x-cog-array-type": "iterator",
"x-cog-array-display": "concatenate"
}