Need a sample of nursing admission essay

• PROS: It has fantastic engraving, incredible playback (I would buy it if it was paid), and almost all the features you'll ever need. It's super intuitive and user-friendly, and I got the hang of it quickly even though it wasn't my first software. It's open source, which means you can suggest bug requests and get responses and workarounds really fast. There are no watermarks (there's no paid version to begin with).
• CONS: The places where Musescore lacks are how it crashes (in v4) and that it doesn't have many advanced features, which are usually required in contemporary composing. You're probably going to have a tough time with advanced techniques. Advanced techniques in general are tough, but that honestly goes for any of the softwares here. As for crashing, I haven't had any issues at all, but that may just be my hardware. It's also not good for collaboration, as it's a software you download, so sharing .xml or .mcsz files is the only way to do it.

• PROS: There are no watermarks on your scores, even in the free version of Noteflight. It has a fair selection of instruments, and you can change the octave of an instrument if they don't have one (ex. changing the octave of a trombone lower to make it a bass trombone). The engraving is decent. It is a lot more possible to collaborate than Musescore (it's not the best, but it's good). You are able to format your scores for free (change sizes, etc.).
• CONS: Noteflight is NOT a software you download, and it gets incredibly laggy and uses a lot of data if you're trying to write an orchestral piece. It is limited to a lot less than what Musescore can do, but it can do almost all of the things I need it to other than having multimeasure rests. One minor inconvenience is that you only get one font, but it's decent and you can at least change the size. You also have a score limit of 10, but you can easily get around this with multiple accounts.

• PROS: Out of these three options (even compared to Sibelius and Finale), Flat has the best software for collaboration. Like Noteflight, it is web-based. This does come with problems, but this allows it to be essentially Google Docs for composition. You have live edits, which aren't in any other software I've seen, and in my experience, it's not as laggy as Noteflight.
• CONS: To start off, Flat is the least feature-packed out of the three (with Musescore being the most). It has all the same free features as Noteflight, except for formatting, which I find a really major inconvenience. You can't change staff sizes, spacing, or fonts, which sucks for large arrangements. As for the engraving, as a musician, I find it unnatural-looking at best. The slurs are horrible, the tuplets seem off, and crescendos and decrescendos are way too wide. That's just me, feel free to disagree with me. The score also has a watermark in the free version, which none of the others have for free.

• The PDF scores use only the default font as that's what most (beginning) composers will care about and use.
  
• I got rid of Flat's watermark (with a PDF editor) because it's not really a blind test with it, is it?
  
• All of the Audio samples used only the free sounds that come with the software (no VSTs or plugins allowed).
  

• Advertising with art years old while your latest stuff is two times better than the sample which would prompt my immediate interest.
• Mentioning things that you draw only to see you doing art of it (this goes to all of you saying no furries but then drawing cat ears and tails, it's a spectrum, specify it).
• Sorting art randomly rather than chronologically; I want to see what's your latest like, not guess whether something you did in 2020 is what you did yesterday and just had a bad day.
• Hiding prices somewhere at the N-th tab as if it's something to be ashamed of; point that thing out, if someone thinks it's way too good to pass up even if you don't you'll get the impulsive-buying customers with extra money (that's me).
• Limiting your contacts; Twitter, email, discord, reddit and anything else you can think of. If it's some obscure social media or something that's super local to your country or region even can be off-putting. You'd need to be really, really good for me to bother creating an account there just for you. On the same topic having updated art socials is a bonus too, artstation and deviant art first come to mind.
• Having drafts or step by step examples; I love these, especially when it comes to knowing how easy it will be to go over the drafting process back and forth with you. This is especially relevant when it comes to filtering out AI generated content. If I'm getting a commission and not looking for AI art I prefer knowing that it's actually you making it, not being suspicious and trying to hunt down tells on dozens of your examples can be easily amended by a phone picture or a printscreen of you doing the art in process.
• Not advertising your art in spaces it'd be relevant at; Sure places like these are a given, but personally I commissioned most artist in places that just advertised their art on random forums. Think like canon characters in subreddits dedicated to them or their world. You drew a catgirl, find a few dozen kemonomimi channels. A character for a game? Run to the official spaces on reddit or discord and post it. People will take notice and you'll reach a wider audience.

Hp z840 2x Xeon 2680 v3 96 gb RAM (~400 - $600 refurb) 2070 Super (~200 used) 

llama_print_timings: load time = 8371.18 ms llama_print_timings: sample time = 642.54 ms / 344 runs ( 1.87 ms per token, 535.38 tokens per second) llama_print_timings: prompt eval time = 24282.47 ms / 1162 tokens ( 20.90 ms per token, 47.85 tokens per second) llama_print_timings: eval time = 240065.49 ms / 343 runs ( 699.90 ms per token, 1.43 tokens per second) llama_print_timings: total time = 268209.84 ms / 1505 tokens Output generated in 269.16 seconds (1.27 tokens/s, 343 tokens, context 1165, seed 641425816) Llama.generate: prefix-match hit llama_print_timings: load time = 8371.18 ms llama_print_timings: sample time = 510.09 ms / 273 runs ( 1.87 ms per token, 535.20 tokens per second) llama_print_timings: prompt eval time = 7477.01 ms / 47 tokens ( 159.09 ms per token, 6.29 tokens per second) llama_print_timings: eval time = 193086.69 ms / 272 runs ( 709.88 ms per token, 1.41 tokens per second) llama_print_timings: total time = 203635.21 ms / 319 tokens Output generated in 204.59 seconds (1.33 tokens/s, 272 tokens, context 1470, seed 1617512394) Llama.generate: prefix-match hit llama_print_timings: load time = 8371.18 ms llama_print_timings: sample time = 641.19 ms / 345 runs ( 1.86 ms per token, 538.06 tokens per second) llama_print_timings: prompt eval time = 7585.99 ms / 55 tokens ( 137.93 ms per token, 7.25 tokens per second) llama_print_timings: eval time = 243416.83 ms / 344 runs ( 707.61 ms per token, 1.41 tokens per second) llama_print_timings: total time = 254954.62 ms / 399 tokens Output generated in 255.91 seconds (1.34 tokens/s, 344 tokens, context 1797, seed 1111902968) 

class Transformer { let modelSize: Int let outputSize: Int let vocabularySize: Int // Embed and Position let embeddings: SharedEmbeddings let positions: PositionalEncoder // Encode var encodeNorm1: [LayerNormalization] = [] var encodeNorm2: [LayerNormalization] = [] var linearA: [Dense] = [] var linearB: [Dense] = [] var multiHead: [MultiHeadAttention] = [] var resA: [Group] = [] var resB: [Group] = [] //let encoder: Group // Decode var decodeNorm1: [LayerNormalization] = [] var decodeNorm2: [LayerNormalization] = [] var decodeNorm3: [LayerNormalization] = [] var linearC: [Dense] = [] var linearD: [Dense] = [] var multiHeadA: [MultiHeadAttention] = [] var multiHeadB: [MultiHeadAttention] = [] var resC: [Group] = [] var resD: [Group] = [] var midBlock: [MultiHeadResidual] = [] let relu: ReLULayer = ReLULayer() //let decoder: Group let outputLinear: Dense let outputSoftmax: SoftMaxLayer = SoftMaxLayer() let sport: Bool let start_token: String let end_token: String let startData: [Double] let sequenceLength: Int var training: Bool var layers: Int var temperature: Double let vocabulary: [String] var inputData: (encoder: [(ER1: [[Double]], ER2: [[Double]])], EFinal: [[Double]], decoder: [(DR1: [[Double]], DR2: (keys: [[Double]], queries: [[Double]], values: [[Double]]), DR2o: [[Double]], DR3: [[Double]])], outputMatrix: [[Double]]) = ([], [], [], []) var inputEmbeddingData: [[Double]] = [] var outputEmbeddingData: [[Double]] = [] init(sequenceLength: Int, modelSize: Int, outputSize: Int, vocabulary: [String], layers: Int, heads: Int, sport: Bool = false, start_token: String = "", end_token: String = "", training: Bool = true, temperature: Double = 1.0) { self.modelSize = modelSize self.training = training self.outputSize = outputSize self.layers = layers let vocabularySize = vocabulary.count self.vocabularySize = vocabularySize self.sequenceLength = sequenceLength self.sport = sport self.start_token = start_token self.end_token = end_token self.temperature = temperature self.vocabulary = vocabulary // Embed and Position self.embeddings = SharedEmbeddings(dimensionSize: modelSize, vocabularySize: vocabularySize, initialization: .xavier_glorot, sport: sport) self.positions = PositionalEncoder() self.startData = oneHotEncode([start_token], vocabulary: vocabulary)[0] for i in 0.. (output: [[Double]], memory: (keys: [[Double]], queries: [[Double]], values: [[Double]])) { let data = (keys, queries, values) let multiHeadOutput = multiHead.forward(queries: queries, keys: keys, values: values) dropInput = multiHeadOutput let normmultiHeadOutput = matrixAdd(queries, drop.forward(multiHeadOutput))! inputDataRes = normmultiHeadOutput return (norm.forward(normmultiHeadOutput), data) } func backward(keys: [[Double]], queries: [[Double]], values: [[Double]], passthrough: [[Double]], learningRate: Double) -> (inner: [[Double]], external: [[Double]]) { var firstPassthrough = norm.backward(input: inputDataRes, passthrough: passthrough, learningRate: learningRate) firstPassthrough = drop.backward(input: dropInput, passthrough: firstPassthrough, learningRate: learningRate) let (_, keyPass, queryPass, valuePass) = multiHead.backward(queries: queries, keys: keys, values: values, passthrough: firstPassthrough, learningRate: learningRate) return (matrixAdd(firstPassthrough, queryPass)!, matrixAdd(keyPass, valuePass)!) } } func forward(_ input: [[Double]], target: [[Double]] = []) -> [[Double]] { var outputs: [[Double]] = [startData] let endIndex = vocabulary.firstIndex(of: end_token) if !training { while outputs.count < outputSize { outputs.append(Array(repeating: 0.0, count: vocabularySize)) } } else { outputs = target } var inputEmbeddings = embeddings.embedForward(input) inputEmbeddings = positions.forward(inputEmbeddings) var encoderOutput: [[Double]] = inputEmbeddings var encoderInputData: [(ER1: [[Double]], ER2: [[Double]])] = [] var encoderFinal: [[Double]] = [] for i in 0.. The dataset I am using is: 
 
inputs = [ "I am feeling good", "I feel bad", "I feel not good", "I am not feeling bad", "I am not feeling good", "I feel good", "I feel not bad" ] targets = [ "you feel good  ", "you feel bad  ", "you feel bad  ", "you feel good  ", "you feel bad  ", "you feel good  ", "you feel good  ", ] 
 I use a learning rate of 0.1, model size of 12 with 2 layers, and each multi head attention mechanism has 2 heads. If anyone is able to help me figure out what is causing the issue, any help is greatly appreciated.