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"use server"
import { HfInference, HfInferenceEndpoint } from "@huggingface/inference"
import { LLMEngine } from "@/types"
export async function predict(inputs: string, nbPanels: number): Promise<string> {
const hf = new HfInference(process.env.AUTH_HF_API_TOKEN)
const llmEngine = `${process.env.LLM_ENGINE || ""}` as LLMEngine
const inferenceEndpoint = `${process.env.LLM_HF_INFERENCE_ENDPOINT_URL || ""}`
const inferenceModel = `${process.env.LLM_HF_INFERENCE_API_MODEL || ""}`
let hfie: HfInferenceEndpoint = hf
// we don't require a lot of token for our task
// but to be safe, let's count ~110 tokens per panel
const nbMaxNewTokens = nbPanels * 130 // 110 isn't enough anymore for long dialogues
switch (llmEngine) {
case "INFERENCE_ENDPOINT":
if (inferenceEndpoint) {
// console.log("Using a custom HF Inference Endpoint")
hfie = hf.endpoint(inferenceEndpoint)
} else {
const error = "No Inference Endpoint URL defined"
console.error(error)
throw new Error(error)
}
break;
case "INFERENCE_API":
if (inferenceModel) {
// console.log("Using an HF Inference API Model")
} else {
const error = "No Inference API model defined"
console.error(error)
throw new Error(error)
}
break;
default:
const error = "Please check your Hugging Face Inference API or Inference Endpoint settings"
console.error(error)
throw new Error(error)
}
const api = llmEngine === "INFERENCE_ENDPOINT" ? hfie : hf
let instructions = ""
try {
for await (const output of api.textGenerationStream({
model: llmEngine === "INFERENCE_ENDPOINT" ? undefined : (inferenceModel || undefined),
inputs,
parameters: {
do_sample: true,
max_new_tokens: nbMaxNewTokens,
return_full_text: false,
}
})) {
instructions += output.token.text
// process.stdout.write(output.token.text)
if (
instructions.includes("</s>") ||
instructions.includes("<s>") ||
instructions.includes("/s>") ||
instructions.includes("[INST]") ||
instructions.includes("[/INST]") ||
instructions.includes("<SYS>") ||
instructions.includes("<<SYS>>") ||
instructions.includes("</SYS>") ||
instructions.includes("<</SYS>>") ||
instructions.includes("<|user|>") ||
instructions.includes("<|end|>") ||
instructions.includes("<|system|>") ||
instructions.includes("<|assistant|>")
) {
break
}
}
} catch (err) {
// console.error(`error during generation: ${err}`)
// a common issue with Llama-2 might be that the model receives too many requests
if (`${err}` === "Error: Model is overloaded") {
instructions = ``
}
}
// need to do some cleanup of the garbage the LLM might have gave us
return (
instructions
.replaceAll("<|end|>", "")
.replaceAll("<s>", "")
.replaceAll("</s>", "")
.replaceAll("/s>", "")
.replaceAll("[INST]", "")
.replaceAll("[/INST]", "")
.replaceAll("<SYS>", "")
.replaceAll("<<SYS>>", "")
.replaceAll("</SYS>", "")
.replaceAll("<</SYS>>", "")
.replaceAll("<|system|>", "")
.replaceAll("<|user|>", "")
.replaceAll("<|all|>", "")
.replaceAll("<|assistant|>", "")
.replaceAll('""', '"')
)
}
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