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using mistral with DJL / published by https://github.com/dacr/code-examples-manager #389e67ca-de9a-4f47-a1c0-504564fb2dbe/dc1585e1c2f63304e369f338f63a03ac9263c8e3
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// summary : using mistral with DJL | |
// keywords : djl, machine-learning, llm, mistral, ai, @testable | |
// publish : gist | |
// authors : David Crosson | |
// license : Apache NON-AI License Version 2.0 (https://raw.githubusercontent.com/non-ai-licenses/non-ai-licenses/main/NON-AI-APACHE2) | |
// id : 389e67ca-de9a-4f47-a1c0-504564fb2dbe | |
// created-on : 2024-02-03T14:34:48+01:00 | |
// managed-by : https://github.com/dacr/code-examples-manager | |
// run-with : scala-cli $file | |
// --------------------- | |
//> using scala "3.4.2" | |
//> using dep "org.slf4j:slf4j-api:2.0.13" | |
//> using dep "org.slf4j:slf4j-simple:2.0.13" | |
//> using dep "net.java.dev.jna:jna:5.14.0" | |
//> using dep "ai.djl:api:0.28.0" | |
//> using dep "ai.djl:basicdataset:0.28.0" | |
//> using dep "ai.djl.llama:llama:0.28.0" | |
//> using dep "ai.djl.pytorch:pytorch-engine:0.28.0" | |
//> using objectWrapper | |
// --------------------- | |
/* Thank Scala.IO and NuMind and of course DJL ! | |
https://github.com/numind-tech/scalaio_2024/blob/main/src/main/scala/chatbot/Chatbot.scala | |
------ | |
djl://ai.djl.huggingface.gguf/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/0.0.1/Q4_K_M, ai.djl.huggingface.gguf/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/0.0.1/Q4_K_M | |
https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf?download=true | |
*/ | |
System.setProperty("org.slf4j.simpleLogger.defaultLogLevel", "error") | |
import ai.djl.repository.zoo.Criteria | |
import ai.djl.training.util.ProgressBar | |
import ai.djl.llama.engine.LlamaInput | |
import ai.djl.llama.engine.LlamaTranslatorFactory | |
import ai.djl.llama.jni.Token | |
import ai.djl.llama.jni.TokenIterator | |
import scala.jdk.CollectionConverters.* | |
import scala.util.chaining.* | |
import scala.io.AnsiColor.{BLUE, BOLD, CYAN, GREEN, MAGENTA, RED, RESET, UNDERLINED, YELLOW} | |
val name = "LLM" | |
val modelId = "TheBloke/Mistral-7B-Instruct-v0.2-GGUF" | |
val quantMethod = "Q4_K_M" | |
val url = s"djl://ai.djl.huggingface.gguf/$modelId/0.0.1/$quantMethod" | |
val criteria = | |
Criteria.builder | |
.setTypes(classOf[LlamaInput], classOf[TokenIterator]) | |
.optModelUrls(url) | |
.optOption("number_gpu_layers", "43") | |
.optTranslatorFactory(new LlamaTranslatorFactory()) | |
.optProgress(new ProgressBar) | |
.build | |
val model = criteria.loadModel() | |
val predictor = model.newPredictor() | |
val param = new LlamaInput.Parameters() | |
param.setTemperature(0.7f) | |
param.setPenalizeNl(true) | |
param.setMirostat(2) | |
param.setAntiPrompt(Array("User: ")) | |
val in = new LlamaInput() | |
in.setParameters(param) | |
val systemPrompt = | |
s"""As a computer science teacher, I make my best to help my students to become software experts. | |
| | |
|$name: How may I help you today ?""".stripMargin | |
val prompt = StringBuilder(systemPrompt) | |
def interact(nextInput: String): String = { | |
val morePrompt = s"\nUser: $nextInput\n$name: " | |
print(s"${BLUE}$morePrompt$RESET") | |
prompt.append(morePrompt) | |
in.setInputs(prompt.toString()) | |
val it = predictor.predict(in) | |
val tokens = it.asScala.map(_.getText.tap(print)).toList | |
prompt.append(tokens.mkString) | |
tokens.mkString | |
} | |
interact("What is a monad ?") | |
interact("Could you give me a scala example ?") | |
interact("Thank you very much teacher !") | |
println() |
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