Experimental Scala 3 bindings for llama.cpp using Slinc.
Add llm4s to your build.sbt:
libraryDependencies += "com.donderom" %% "llm4s" % "0.10.0"For JDK 17 add .jvmopts file in the project root:
--add-modules=jdk.incubator.foreign
--enable-native-access=ALL-UNNAMED
Version compatibility:
| llm4s | Scala | JDK | llama.cpp (commit hash) |
|---|---|---|---|
| 0.10+ | 3.3.0 | 17, 19 | 49e7cb5 (Jul 31) |
Older versions
| llm4s | Scala | JDK | llama.cpp (commit hash) |
|---|---|---|---|
| 0.6+ | --- | --- | 49e7cb5 (Jul 31) |
| 0.4+ | --- | --- | 70d26ac (Jul 23) |
| 0.3+ | --- | --- | a6803ca (Jul 14) |
| 0.1+ | 3.3.0-RC3 | 17, 19 | 447ccbe (Jun 25) |
import java.nio.file.Paths
import com.donderom.llm4s.*
System.load("path/to/libllama.so")
val model = Paths.get("path/to/ggml-model.bin")
val contextParams = ContextParams(threads = 6)
val prompt = "Deep learning is "val llm = Llm(model = model, params = contextParams)
val params = LlmParams(context = contextParams, predictTokens = 256)
// To print generation as it goes
llm(prompt, params).foreach: stream =>
stream.foreach: token =>
print(token)
// Or build a string
llm(prompt, params).foreach(stream => println(stream.mkString))
llm.close()val embedding = Embedding(model = model, params = contextParams)
embedding(prompt, contextParams).foreach: embeddings =>
embeddings.foreach: embd =>
print(embd)
print(' ')
embedding.close()