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llama.swiftui : add bench functionality (#4483)

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* llama.swiftui : add bench button

* llama.swiftui : initial bench functionality

* force to use n_gpu_layers on simulator

* add download buttons & expose llamaState.loadModel

* update project.pbxproj

* comment #Preview & fix editorconfig check

* gitignore : xcode stuff

* llama.swiftui : UX improvements

* llama.swiftui : avoid data copy via "downloadTask"

* llama.swiftui : remove model from project

* llama : remove "mostly" from model infos

* llama.swiftui : improve bench

---------

Co-authored-by: jhen <developer@jhen.me>
This commit is contained in:
Georgi Gerganov 2023-12-17 19:38:41 +02:00 committed by GitHub
parent f7f468a97d
commit 800a489e4a
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GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 895 additions and 510 deletions
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3
.editorconfig
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@ -23,3 +23,6 @@ insert_final_newline = unset
[examples/server/public/*] [examples/server/public/*]
indent_size = 2 indent_size = 2
[examples/llama.swiftui/llama.swiftui.xcodeproj/*]
indent_style = tab

1
examples/llama.swiftui/.gitignore vendored
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@ -1 +1,2 @@
xcuserdata xcuserdata
xcshareddata

182
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
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@ -6,16 +6,34 @@ enum LlamaError: Error {
case couldNotInitializeContext case couldNotInitializeContext
} }
func llama_batch_clear(_ batch: inout llama_batch) {
batch.n_tokens = 0
}
func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama_pos, _ seq_ids: [llama_seq_id], _ logits: Bool) {
batch.token [Int(batch.n_tokens)] = id
batch.pos [Int(batch.n_tokens)] = pos
batch.n_seq_id[Int(batch.n_tokens)] = Int32(seq_ids.count)
for i in 0..<seq_ids.count {
batch.seq_id[Int(batch.n_tokens)]![Int(i)] = seq_ids[i]
}
batch.logits [Int(batch.n_tokens)] = logits ? 1 : 0
batch.n_tokens += 1
}
actor LlamaContext { actor LlamaContext {
private var model: OpaquePointer private var model: OpaquePointer
private var context: OpaquePointer private var context: OpaquePointer
private var batch: llama_batch private var batch: llama_batch
private var tokens_list: [llama_token] private var tokens_list: [llama_token]
/// This variable is used to store temporarily invalid cchars /// This variable is used to store temporarily invalid cchars
private var temporary_invalid_cchars: [CChar] private var temporary_invalid_cchars: [CChar]
var n_len: Int32 = 512 var n_len: Int32 = 64
var n_cur: Int32 = 0 var n_cur: Int32 = 0
var n_decode: Int32 = 0 var n_decode: Int32 = 0
init(model: OpaquePointer, context: OpaquePointer) { init(model: OpaquePointer, context: OpaquePointer) {
@ -27,25 +45,34 @@ actor LlamaContext {
} }
deinit { deinit {
llama_batch_free(batch)
llama_free(context) llama_free(context)
llama_free_model(model) llama_free_model(model)
llama_backend_free() llama_backend_free()
} }
static func createContext(path: String) throws -> LlamaContext { static func create_context(path: String) throws -> LlamaContext {
llama_backend_init(false) llama_backend_init(false)
let model_params = llama_model_default_params() var model_params = llama_model_default_params()
#if targetEnvironment(simulator)
model_params.n_gpu_layers = 0
print("Running on simulator, force use n_gpu_layers = 0")
#endif
let model = llama_load_model_from_file(path, model_params) let model = llama_load_model_from_file(path, model_params)
guard let model else { guard let model else {
print("Could not load model at \(path)") print("Could not load model at \(path)")
throw LlamaError.couldNotInitializeContext throw LlamaError.couldNotInitializeContext
} }
let n_threads = max(1, min(8, ProcessInfo.processInfo.processorCount - 2))
print("Using \(n_threads) threads")
var ctx_params = llama_context_default_params() var ctx_params = llama_context_default_params()
ctx_params.seed = 1234 ctx_params.seed = 1234
ctx_params.n_ctx = 2048 ctx_params.n_ctx = 2048
ctx_params.n_threads = 8 ctx_params.n_threads = UInt32(n_threads)
ctx_params.n_threads_batch = 8 ctx_params.n_threads_batch = UInt32(n_threads)
let context = llama_new_context_with_model(model, ctx_params) let context = llama_new_context_with_model(model, ctx_params)
guard let context else { guard let context else {
@ -56,6 +83,26 @@ actor LlamaContext {
return LlamaContext(model: model, context: context) return LlamaContext(model: model, context: context)
} }
func model_info() -> String {
let result = UnsafeMutablePointer<Int8>.allocate(capacity: 256)
result.initialize(repeating: Int8(0), count: 256)
defer {
result.deallocate()
}
// TODO: this is probably very stupid way to get the string from C
let nChars = llama_model_desc(model, result, 256)
let bufferPointer = UnsafeBufferPointer(start: result, count: Int(nChars))
var SwiftString = ""
for char in bufferPointer {
SwiftString.append(Character(UnicodeScalar(UInt8(char))))
}
return SwiftString
}
func get_n_tokens() -> Int32 { func get_n_tokens() -> Int32 {
return batch.n_tokens; return batch.n_tokens;
} }
@ -79,16 +126,11 @@ actor LlamaContext {
print(String(cString: token_to_piece(token: id) + [0])) print(String(cString: token_to_piece(token: id) + [0]))
} }
// batch = llama_batch_init(512, 0) // done in init() llama_batch_clear(&batch)
batch.n_tokens = Int32(tokens_list.count)
for i1 in 0..<batch.n_tokens { for i1 in 0..<tokens_list.count {
let i = Int(i1) let i = Int(i1)
batch.token[i] = tokens_list[i] llama_batch_add(&batch, tokens_list[i], Int32(i), [0], false)
batch.pos[i] = i1
batch.n_seq_id[Int(i)] = 1
batch.seq_id[Int(i)]![0] = 0
batch.logits[i] = 0
} }
batch.logits[Int(batch.n_tokens) - 1] = 1 // true batch.logits[Int(batch.n_tokens) - 1] = 1 // true
@ -141,18 +183,11 @@ actor LlamaContext {
print(new_token_str) print(new_token_str)
// tokens_list.append(new_token_id) // tokens_list.append(new_token_id)
batch.n_tokens = 0 llama_batch_clear(&batch)
llama_batch_add(&batch, new_token_id, n_cur, [0], true)
batch.token[Int(batch.n_tokens)] = new_token_id
batch.pos[Int(batch.n_tokens)] = n_cur
batch.n_seq_id[Int(batch.n_tokens)] = 1
batch.seq_id[Int(batch.n_tokens)]![0] = 0
batch.logits[Int(batch.n_tokens)] = 1 // true
batch.n_tokens += 1
n_decode += 1 n_decode += 1
n_cur += 1
n_cur += 1
if llama_decode(context, batch) != 0 { if llama_decode(context, batch) != 0 {
print("failed to evaluate llama!") print("failed to evaluate llama!")
@ -161,14 +196,111 @@ actor LlamaContext {
return new_token_str return new_token_str
} }
func bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) -> String {
var pp_avg: Double = 0
var tg_avg: Double = 0
var pp_std: Double = 0
var tg_std: Double = 0
for r in 0..<nr {
// bench prompt processing
llama_batch_clear(&batch)
let n_tokens = pp
for i in 0..<n_tokens {
llama_batch_add(&batch, 0, Int32(i), [0], false)
}
batch.logits[Int(batch.n_tokens) - 1] = 1 // true
llama_kv_cache_clear(context)
let t_pp_start = ggml_time_us()
if llama_decode(context, batch) != 0 {
print("llama_decode() failed during prompt")
}
let t_pp_end = ggml_time_us()
// bench text generation
llama_kv_cache_clear(context)
let t_tg_start = ggml_time_us()
for i in 0..<tg {
llama_batch_clear(&batch)
for j in 0..<pl {
llama_batch_add(&batch, 0, Int32(i), [Int32(j)], true)
}
if llama_decode(context, batch) != 0 {
print("llama_decode() failed during text generation")
}
}
let t_tg_end = ggml_time_us()
llama_kv_cache_clear(context)
let t_pp = Double(t_pp_end - t_pp_start) / 1000000.0
let t_tg = Double(t_tg_end - t_tg_start) / 1000000.0
let speed_pp = Double(pp) / t_pp
let speed_tg = Double(pl*tg) / t_tg
pp_avg += speed_pp
tg_avg += speed_tg
pp_std += speed_pp * speed_pp
tg_std += speed_tg * speed_tg
print("pp \(speed_pp) t/s, tg \(speed_tg) t/s")
}
pp_avg /= Double(nr)
tg_avg /= Double(nr)
if nr > 1 {
pp_std = sqrt(pp_std / Double(nr - 1) - pp_avg * pp_avg * Double(nr) / Double(nr - 1))
tg_std = sqrt(tg_std / Double(nr - 1) - tg_avg * tg_avg * Double(nr) / Double(nr - 1))
} else {
pp_std = 0
tg_std = 0
}
let model_desc = model_info();
let model_size = String(format: "%.2f GiB", Double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0);
let model_n_params = String(format: "%.2f B", Double(llama_model_n_params(model)) / 1e9);
let backend = "Metal";
let pp_avg_str = String(format: "%.2f", pp_avg);
let tg_avg_str = String(format: "%.2f", tg_avg);
let pp_std_str = String(format: "%.2f", pp_std);
let tg_std_str = String(format: "%.2f", tg_std);
var result = ""
result += String("| model | size | params | backend | test | t/s |\n")
result += String("| --- | --- | --- | --- | --- | --- |\n")
result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | pp \(pp) | \(pp_avg_str) ± \(pp_std_str) |\n")
result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | tg \(tg) | \(tg_avg_str) ± \(tg_std_str) |\n")
return result;
}
func clear() { func clear() {
tokens_list.removeAll() tokens_list.removeAll()
temporary_invalid_cchars.removeAll() temporary_invalid_cchars.removeAll()
llama_kv_cache_clear(context)
} }
private func tokenize(text: String, add_bos: Bool) -> [llama_token] { private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
let utf8Count = text.utf8.count let utf8Count = text.utf8.count
let n_tokens = utf8Count + (add_bos ? 1 : 0) let n_tokens = utf8Count + (add_bos ? 1 : 0) + 1
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens) let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false) let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)

898
examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
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@ -1,481 +1,483 @@
// !$*UTF8*$! // !$*UTF8*$!
{ {
archiveVersion = 1; archiveVersion = 1;
classes = { classes = {
}; };
objectVersion = 56; objectVersion = 56;
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MTL_FAST_MATH = YES; MTL_FAST_MATH = YES;
SDKROOT = iphoneos; SDKROOT = iphoneos;
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VALIDATE_PRODUCT = YES; VALIDATE_PRODUCT = YES;
}; };
name = Release; name = Release;
}; };
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52
examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
View File

@ -3,24 +3,26 @@ import Foundation
@MainActor @MainActor
class LlamaState: ObservableObject { class LlamaState: ObservableObject {
@Published var messageLog = "" @Published var messageLog = ""
@Published var cacheCleared = false
private var llamaContext: LlamaContext? private var llamaContext: LlamaContext?
private var modelUrl: URL? { private var defaultModelUrl: URL? {
Bundle.main.url(forResource: "q8_0", withExtension: "gguf", subdirectory: "models") Bundle.main.url(forResource: "ggml-model", withExtension: "gguf", subdirectory: "models")
// Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models") // Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models")
} }
init() { init() {
do { do {
try loadModel() try loadModel(modelUrl: defaultModelUrl)
} catch { } catch {
messageLog += "Error!\n" messageLog += "Error!\n"
} }
} }
private func loadModel() throws { func loadModel(modelUrl: URL?) throws {
messageLog += "Loading model...\n" messageLog += "Loading model...\n"
if let modelUrl { if let modelUrl {
llamaContext = try LlamaContext.createContext(path: modelUrl.path()) llamaContext = try LlamaContext.create_context(path: modelUrl.path())
messageLog += "Loaded model \(modelUrl.lastPathComponent)\n" messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
} else { } else {
messageLog += "Could not locate model\n" messageLog += "Could not locate model\n"
@ -31,7 +33,7 @@ class LlamaState: ObservableObject {
guard let llamaContext else { guard let llamaContext else {
return return
} }
messageLog += "Attempting to complete text...\n"
await llamaContext.completion_init(text: text) await llamaContext.completion_init(text: text)
messageLog += "\(text)" messageLog += "\(text)"
@ -42,4 +44,42 @@ class LlamaState: ObservableObject {
await llamaContext.clear() await llamaContext.clear()
messageLog += "\n\ndone\n" messageLog += "\n\ndone\n"
} }
func bench() async {
guard let llamaContext else {
return
}
messageLog += "\n"
messageLog += "Running benchmark...\n"
messageLog += "Model info: "
messageLog += await llamaContext.model_info() + "\n"
let t_start = DispatchTime.now().uptimeNanoseconds
await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up
let t_end = DispatchTime.now().uptimeNanoseconds
let t_heat = Double(t_end - t_start) / 1_000_000_000.0
messageLog += "Heat up time: \(t_heat) seconds, please wait...\n"
// if more than 5 seconds, then we're probably running on a slow device
if t_heat > 5.0 {
messageLog += "Heat up time is too long, aborting benchmark\n"
return
}
let result = await llamaContext.bench(pp: 512, tg: 128, pl: 1, nr: 3)
messageLog += "\(result)"
messageLog += "\n"
}
func clear() async {
guard let llamaContext else {
return
}
await llamaContext.clear()
messageLog = ""
}
} }

114
examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
View File

@ -5,24 +5,97 @@ struct ContentView: View {
@State private var multiLineText = "" @State private var multiLineText = ""
private static func cleanupModelCaches() {
// Delete all models (*.gguf)
let fileManager = FileManager.default
let documentsUrl = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0]
do {
let fileURLs = try fileManager.contentsOfDirectory(at: documentsUrl, includingPropertiesForKeys: nil)
for fileURL in fileURLs {
if fileURL.pathExtension == "gguf" {
try fileManager.removeItem(at: fileURL)
}
}
} catch {
print("Error while enumerating files \(documentsUrl.path): \(error.localizedDescription)")
}
}
var body: some View { var body: some View {
VStack { VStack {
ScrollView(.vertical) { ScrollView(.vertical, showsIndicators: true) {
Text(llamaState.messageLog) Text(llamaState.messageLog)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
.padding()
.onTapGesture {
UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil)
}
} }
TextEditor(text: $multiLineText) TextEditor(text: $multiLineText)
.frame(height: 200) .frame(height: 80)
.padding() .padding()
.border(Color.gray, width: 0.5) .border(Color.gray, width: 0.5)
Button(action: {
sendText() HStack {
}) { Button("Send") {
Text("Send") sendText()
.padding() }
.background(Color.blue) .padding(8)
.foregroundColor(.white) .background(Color.blue)
.cornerRadius(8) .foregroundColor(.white)
.cornerRadius(8)
Button("Bench") {
bench()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Clear") {
clear()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Copy") {
UIPasteboard.general.string = llamaState.messageLog
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
}
VStack {
DownloadButton(
llamaState: llamaState,
modelName: "TinyLlama-1.1B (Q4_0)",
modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
)
.font(.system(size: 12))
.padding(.top, 4)
DownloadButton(
llamaState: llamaState,
modelName: "TinyLlama-1.1B (Q8_0)",
modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true",
filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf"
)
.font(.system(size: 12))
Button("Clear downloaded models") {
ContentView.cleanupModelCaches()
llamaState.cacheCleared = true
}
.padding(8)
.font(.system(size: 12))
} }
} }
.padding() .padding()
@ -34,9 +107,20 @@ struct ContentView: View {
multiLineText = "" multiLineText = ""
} }
} }
func bench() {
Task {
await llamaState.bench()
}
}
func clear() {
Task {
await llamaState.clear()
}
}
} }
/*
#Preview { //#Preview {
ContentView() // ContentView()
} //}
*/

122
examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift Normal file
View File

@ -0,0 +1,122 @@
import SwiftUI
struct DownloadButton: View {
@ObservedObject private var llamaState: LlamaState
private var modelName: String
private var modelUrl: String
private var filename: String
@State private var status: String
@State private var downloadTask: URLSessionDownloadTask?
@State private var progress = 0.0
@State private var observation: NSKeyValueObservation?
private static func getFileURL(filename: String) -> URL {
FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0].appendingPathComponent(filename)
}
private func checkFileExistenceAndUpdateStatus() {
}
init(llamaState: LlamaState, modelName: String, modelUrl: String, filename: String) {
self.llamaState = llamaState
self.modelName = modelName
self.modelUrl = modelUrl
self.filename = filename
let fileURL = DownloadButton.getFileURL(filename: filename)
status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download"
}
private func download() {
status = "downloading"
print("Downloading model \(modelName) from \(modelUrl)")
guard let url = URL(string: modelUrl) else { return }
let fileURL = DownloadButton.getFileURL(filename: filename)
downloadTask = URLSession.shared.downloadTask(with: url) { temporaryURL, response, error in
if let error = error {
print("Error: \(error.localizedDescription)")
return
}
guard let response = response as? HTTPURLResponse, (200...299).contains(response.statusCode) else {
print("Server error!")
return
}
do {
if let temporaryURL = temporaryURL {
try FileManager.default.copyItem(at: temporaryURL, to: fileURL)
print("Writing to \(filename) completed")
llamaState.cacheCleared = false
status = "downloaded"
}
} catch let err {
print("Error: \(err.localizedDescription)")
}
}
observation = downloadTask?.progress.observe(\.fractionCompleted) { progress, _ in
self.progress = progress.fractionCompleted
}
downloadTask?.resume()
}
var body: some View {
VStack {
if status == "download" {
Button(action: download) {
Text("Download " + modelName)
}
} else if status == "downloading" {
Button(action: {
downloadTask?.cancel()
status = "download"
}) {
Text("\(modelName) (Downloading \(Int(progress * 100))%)")
}
} else if status == "downloaded" {
Button(action: {
let fileURL = DownloadButton.getFileURL(filename: filename)
if !FileManager.default.fileExists(atPath: fileURL.path) {
download()
return
}
do {
try llamaState.loadModel(modelUrl: fileURL)
} catch let err {
print("Error: \(err.localizedDescription)")
}
}) {
Text("\(modelName) (Downloaded)")
}
} else {
Text("Unknown status")
}
}
.onDisappear() {
downloadTask?.cancel()
}
.onChange(of: llamaState.cacheCleared) { newValue in
if newValue {
downloadTask?.cancel()
let fileURL = DownloadButton.getFileURL(filename: filename)
status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download"
}
}
}
}
// #Preview {
// DownloadButton(
// llamaState: LlamaState(),
// modelName: "TheBloke / TinyLlama-1.1B-1T-OpenOrca-GGUF (Q4_0)",
// modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
// filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
// )
// }

33
llama.cpp
View File

@ -2397,25 +2397,25 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
switch (ftype) { switch (ftype) {
case LLAMA_FTYPE_ALL_F32: return "all F32"; case LLAMA_FTYPE_ALL_F32: return "all F32";
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16"; case LLAMA_FTYPE_MOSTLY_F16: return "F16";
case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0"; case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0";
case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1"; case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1";
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16: case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
return "mostly Q4_1, some F16"; return "Q4_1, some F16";
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0"; case LLAMA_FTYPE_MOSTLY_Q5_0: return "Q5_0";
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1"; case LLAMA_FTYPE_MOSTLY_Q5_1: return "Q5_1";
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0"; case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0";
// K-quants // K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K"; case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K";
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small"; case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small";
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large"; case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large";
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small"; case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "Q4_K - Small";
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "Q4_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small"; case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small";
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K"; case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K";
default: return "unknown, may not work"; default: return "unknown, may not work";
} }
@ -2533,6 +2533,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) { switch (hparams.n_layer) {
case 22: model.type = e_model::MODEL_1B; break;
case 26: model.type = e_model::MODEL_3B; break; case 26: model.type = e_model::MODEL_3B; break;
case 32: model.type = e_model::MODEL_7B; break; case 32: model.type = e_model::MODEL_7B; break;
case 40: model.type = e_model::MODEL_13B; break; case 40: model.type = e_model::MODEL_13B; break;