Try Jurassic-2 Instruct API here
Jurassic-2 Instruct models were developed to handle instruction-only prompts ("zero-shot") without requiring examples ("few-shot"). This approach provides a more intuitive way to interact with large language models, allowing users to obtain the best possible output for their task without any examples. With their specialized training, Jurassic-2 Instruct models excel at generating coherent and precise text based solely on instructions provided by the user.
Jurassic-2 Instruct Models
Jurassic-2 Instruct models come in two variations - Mid-Instruct and Ultra-Instruct.
Mid-Instruct: optimized for generating precise text based on minimal context, which makes it ideal for use cases such as chatbots and other conversational interfaces.
Ultra-Instruct: offers superior language understanding and response generation capabilities, making it ideal for advanced conversational interface needs.
Example API Request
fetch("https://api.ai21.com/studio/v1/j2-grande-instruct/complete", {
headers: {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
},
body: JSON.stringify({
"prompt": "Write a tweet about the future of NLP\n",
"numResults": 1,
"maxTokens": 50,
"temperature": 0.8,
"topKReturn": 0,
"topP":1,
"countPenalty": {
"scale": 0,
"applyToNumbers": false,
"applyToPunctuations": false,
"applyToStopwords": false,
"applyToWhitespaces": false,
"applyToEmojis": false
},
"frequencyPenalty": {
"scale": 0,
"applyToNumbers": false,
"applyToPunctuations": false,
"applyToStopwords": false,
"applyToWhitespaces": false,
"applyToEmojis": false
},
"presencePenalty": {
"scale": 0,
"applyToNumbers": false,
"applyToPunctuations": false,
"applyToStopwords": false,
"applyToWhitespaces": false,
"applyToEmojis": false
},
"stopSequences":["##"]
}),
method: "POST"
});
Example Response
{
"id": "029cd08c-f65b-06de-49d9-60b47c15e23a",
"prompt": {
"text": "Write a tweet about the future of NLP\\n",
"tokens": [
{
"generatedToken": {
"token": "▁Write",
"logprob": -9.832908630371094,
"raw_logprob": -9.832908630371094
},
"topTokens": null,
"textRange": {
"start": 0,
"end": 5
}
},
{
"generatedToken": {
"token": "▁a▁tweet",
"logprob": -9.605907440185547,
"raw_logprob": -9.605907440185547
},
"topTokens": null,
"textRange": {
"start": 5,
"end": 13
}
},
{
"generatedToken": {
"token": "▁about▁the▁future▁of",
"logprob": -11.053253173828125,
"raw_logprob": -11.053253173828125
},
"topTokens": null,
"textRange": {
"start": 13,
"end": 33
}
},
{
"generatedToken": {
"token": "▁NLP",
"logprob": -9.101167678833008,
"raw_logprob": -9.101167678833008
},
"topTokens": null,
"textRange": {
"start": 33,
"end": 37
}
},
{
"generatedToken": {
"token": "\\n",
"logprob": -12.08486557006836,
"raw_logprob": -12.08486557006836
},
"topTokens": null,
"textRange": {
"start": 37,
"end": 39
}
}
]
},
"completions": [
{
"data": {
"text": "The future of NLP is bright. Using NLP, we can train machines to understand language, reason about knowledge, and interact with humans naturally. #END",
"tokens": [
{
"generatedToken": {
"token": "▁",
"logprob": -0.009251699782907963,
"raw_logprob": -0.025562729686498642
},
"topTokens": null,
"textRange": {
"start": 0,
"end": 1
}
},
{
"generatedToken": {
"token": "▁I▁think",
"logprob": -2.7593750953674316,
"raw_logprob": -2.778052806854248
},
"topTokens": null,
"textRange": {
"start": 1,
"end": 9
}
},
{
"generatedToken": {
"token": "▁the▁future▁of",
"logprob": -0.01882636919617653,
"raw_logprob": -0.0687691792845726
},
"topTokens": null,
"textRange": {
"start": 9,
"end": 23
}
},
{
"generatedToken": {
"token": "▁NLP",
"logprob": -0.0019030333496630192,
"raw_logprob": -0.007231252733618021
},
"topTokens": null,
"textRange": {
"start": 23,
"end": 27
}
},
{
"generatedToken": {
"token": "▁is",
"logprob": -0.05679452046751976,
"raw_logprob": -0.12373676151037216
},
"topTokens": null,
"textRange": {
"start": 27,
"end": 30
}
},
{
"generatedToken": {
"token": "▁in▁building",
"logprob": -2.596593141555786,
"raw_logprob": -3.1734254360198975
},
"topTokens": null,
"textRange": {
"start": 30,
"end": 42
}
},
{
"generatedToken": {
"token": "▁systems▁that",
"logprob": -1.0248959064483643,
"raw_logprob": -1.5406049489974976
},
"topTokens": null,
"textRange": {
"start": 42,
"end": 55
}
},
{
"generatedToken": {
"token": "▁understand",
"logprob": -0.4593545198440552,
"raw_logprob": -0.8558947443962097
},
"topTokens": null,
"textRange": {
"start": 55,
"end": 66
}
},
{
"generatedToken": {
"token": "▁human",
"logprob": -1.0440826416015625,
"raw_logprob": -1.5726295709609985
},
"topTokens": null,
"textRange": {
"start": 66,
"end": 72
}
},
{
"generatedToken": {
"token": "▁language",
"logprob": -0.20479987561702728,
"raw_logprob": -0.4235517382621765
},
"topTokens": null,
"textRange": {
"start": 72,
"end": 81
}
},
{
"generatedToken": {
"token": "▁as▁well▁as",
"logprob": -0.2055591642856598,
"raw_logprob": -0.457123339176178
},
"topTokens": null,
"textRange": {
"start": 81,
"end": 92
}
},
{
"generatedToken": {
"token": "▁humans",
"logprob": -0.3632097840309143,
"raw_logprob": -0.5173041224479675
},
"topTokens": null,
"textRange": {
"start": 92,
"end": 99
}
},
{
"generatedToken": {
"token": "▁do",
"logprob": -0.0010070496937260032,
"raw_logprob": -0.0054183765314519405
},
"topTokens": null,
"textRange": {
"start": 99,
"end": 102
}
},
{
"generatedToken": {
"token": ".",
"logprob": -0.003985914401710033,
"raw_logprob": -0.014302950352430344
},
"topTokens": null,
"textRange": {
"start": 102,
"end": 103
}
},
{
"generatedToken": {
"token": "<|endoftext|>",
"logprob": -0.5593628287315369,
"raw_logprob": -0.5923886299133301
},
"topTokens": null,
"textRange": {
"start": 103,
"end": 103
}
}
]
},
"finishReason": {
"reason": "endoftext"
}
}
]
}
fetch("https://api.ai21.com/studio/v1/j2-grande-instruct/complete", {
headers: {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
},
body: JSON.stringify({
"prompt": "Write a tweet about the future of NLP\n",
"numResults": 1,
"maxTokens": 50,
"temperature": 0.8,
"topKReturn": 0,
"topP":1,
"countPenalty": {
"scale": 0,
"applyToNumbers": false,
"applyToPunctuations": false,
"applyToStopwords": false,
"applyToWhitespaces": false,
"applyToEmojis": false
},
"frequencyPenalty": {
"scale": 0,
"applyToNumbers": false,
"applyToPunctuations": false,
"applyToStopwords": false,
"applyToWhitespaces": false,
"applyToEmojis": false
},
"presencePenalty": {
"scale": 0,
"applyToNumbers": false,
"applyToPunctuations": false,
"applyToStopwords": false,
"applyToWhitespaces": false,
"applyToEmojis": false
},
"stopSequences":["##"]
}),
method: "POST"
});
Example Response
{
"id": "029cd08c-f65b-06de-49d9-60b47c15e23a",
"prompt": {
"text": "Write a tweet about the future of NLP\\n",
"tokens": [
{
"generatedToken": {
"token": "▁Write",
"logprob": -9.832908630371094,
"raw_logprob": -9.832908630371094
},
"topTokens": null,
"textRange": {
"start": 0,
"end": 5
}
},
{
"generatedToken": {
"token": "▁a▁tweet",
"logprob": -9.605907440185547,
"raw_logprob": -9.605907440185547
},
"topTokens": null,
"textRange": {
"start": 5,
"end": 13
}
},
{
"generatedToken": {
"token": "▁about▁the▁future▁of",
"logprob": -11.053253173828125,
"raw_logprob": -11.053253173828125
},
"topTokens": null,
"textRange": {
"start": 13,
"end": 33
}
},
{
"generatedToken": {
"token": "▁NLP",
"logprob": -9.101167678833008,
"raw_logprob": -9.101167678833008
},
"topTokens": null,
"textRange": {
"start": 33,
"end": 37
}
},
{
"generatedToken": {
"token": "\\n",
"logprob": -12.08486557006836,
"raw_logprob": -12.08486557006836
},
"topTokens": null,
"textRange": {
"start": 37,
"end": 39
}
}
]
},
"completions": [
{
"data": {
"text": "The future of NLP is bright. Using NLP, we can train machines to understand language, reason about knowledge, and interact with humans naturally. #END",
"tokens": [
{
"generatedToken": {
"token": "▁",
"logprob": -0.009251699782907963,
"raw_logprob": -0.025562729686498642
},
"topTokens": null,
"textRange": {
"start": 0,
"end": 1
}
},
{
"generatedToken": {
"token": "▁I▁think",
"logprob": -2.7593750953674316,
"raw_logprob": -2.778052806854248
},
"topTokens": null,
"textRange": {
"start": 1,
"end": 9
}
},
{
"generatedToken": {
"token": "▁the▁future▁of",
"logprob": -0.01882636919617653,
"raw_logprob": -0.0687691792845726
},
"topTokens": null,
"textRange": {
"start": 9,
"end": 23
}
},
{
"generatedToken": {
"token": "▁NLP",
"logprob": -0.0019030333496630192,
"raw_logprob": -0.007231252733618021
},
"topTokens": null,
"textRange": {
"start": 23,
"end": 27
}
},
{
"generatedToken": {
"token": "▁is",
"logprob": -0.05679452046751976,
"raw_logprob": -0.12373676151037216
},
"topTokens": null,
"textRange": {
"start": 27,
"end": 30
}
},
{
"generatedToken": {
"token": "▁in▁building",
"logprob": -2.596593141555786,
"raw_logprob": -3.1734254360198975
},
"topTokens": null,
"textRange": {
"start": 30,
"end": 42
}
},
{
"generatedToken": {
"token": "▁systems▁that",
"logprob": -1.0248959064483643,
"raw_logprob": -1.5406049489974976
},
"topTokens": null,
"textRange": {
"start": 42,
"end": 55
}
},
{
"generatedToken": {
"token": "▁understand",
"logprob": -0.4593545198440552,
"raw_logprob": -0.8558947443962097
},
"topTokens": null,
"textRange": {
"start": 55,
"end": 66
}
},
{
"generatedToken": {
"token": "▁human",
"logprob": -1.0440826416015625,
"raw_logprob": -1.5726295709609985
},
"topTokens": null,
"textRange": {
"start": 66,
"end": 72
}
},
{
"generatedToken": {
"token": "▁language",
"logprob": -0.20479987561702728,
"raw_logprob": -0.4235517382621765
},
"topTokens": null,
"textRange": {
"start": 72,
"end": 81
}
},
{
"generatedToken": {
"token": "▁as▁well▁as",
"logprob": -0.2055591642856598,
"raw_logprob": -0.457123339176178
},
"topTokens": null,
"textRange": {
"start": 81,
"end": 92
}
},
{
"generatedToken": {
"token": "▁humans",
"logprob": -0.3632097840309143,
"raw_logprob": -0.5173041224479675
},
"topTokens": null,
"textRange": {
"start": 92,
"end": 99
}
},
{
"generatedToken": {
"token": "▁do",
"logprob": -0.0010070496937260032,
"raw_logprob": -0.0054183765314519405
},
"topTokens": null,
"textRange": {
"start": 99,
"end": 102
}
},
{
"generatedToken": {
"token": ".",
"logprob": -0.003985914401710033,
"raw_logprob": -0.014302950352430344
},
"topTokens": null,
"textRange": {
"start": 102,
"end": 103
}
},
{
"generatedToken": {
"token": "<|endoftext|>",
"logprob": -0.5593628287315369,
"raw_logprob": -0.5923886299133301
},
"topTokens": null,
"textRange": {
"start": 103,
"end": 103
}
}
]
},
"finishReason": {
"reason": "endoftext"
}
}
]
}