Upload 4 files
Browse files- README.md +2 -2
- RunTinyStories.cs +31 -42
- info.json +1 -1
- tinystories.sentis +2 -2
README.md
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@@ -4,8 +4,8 @@ library_name: unity-sentis
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pipeline_tag: text-generation
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---
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# Tiny Stories Model in Unity Sentis (
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*Version 1.3.0 Sentis files are not compatible with
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This is the [Tiny Stories model](https://huggingface.co/roneneldan/TinyStories-33M) checked to run on Unity 2023. Tiny Stories is a Large Language Model that was trained on children's stories and can create stories based on the first couple of sentences.
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pipeline_tag: text-generation
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---
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# Tiny Stories Model in Unity Sentis Format (Sentis 1.4.0-pre.2*)
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*Version 1.3.0 Sentis files are not compatible with Sentis 1.4.0 and would need to be recreated/downloaded
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This is the [Tiny Stories model](https://huggingface.co/roneneldan/TinyStories-33M) checked to run on Unity 2023. Tiny Stories is a Large Language Model that was trained on children's stories and can create stories based on the first couple of sentences.
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RunTinyStories.cs
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@@ -3,8 +3,8 @@ using System.Collections.Generic;
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using UnityEngine;
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using Unity.Sentis;
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using System.IO;
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using Newtonsoft.Json;
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using System.Text;
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/*
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* Tiny Stories Inference Code
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*
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* In Assets/StreamingAssets put:
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*
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* tinystories.sentis
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* vocab.json
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* merges.txt
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*
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public class RunTinyStories : MonoBehaviour
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{
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const BackendType backend = BackendType.GPUCompute;
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//string outputString = "Once upon a time, there were three bears";
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//Special tokens
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const int END_OF_TEXT = 50256;
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Ops ops;
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ITensorAllocator allocator;
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//Store the vocabulary
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string[] tokens;
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void Start()
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{
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allocator = new TensorCachingAllocator();
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ops = WorkerFactory.CreateOps(backend, allocator);
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SetupWhiteSpaceShifts();
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LoadVocabulary();
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-
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engine = WorkerFactory.CreateWorker(backend,
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DecodePrompt(outputString);
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void RunInference()
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{
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using var tokensSoFar = new TensorInt(new TensorShape(1, maxTokens), outputTokens);
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var
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using var rowB = ops.Mul(predictability, row);
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using var probs = ops.Softmax(rowB, 2);
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probs.MakeReadable();
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int ID =
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if (currentToken >= maxTokens - 1)
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{
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for (int i = 0; i < maxTokens - 1; i++) outputTokens[i] = outputTokens[i + 1];
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}
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void DecodePrompt(string text)
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{
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var inputTokens = GetTokens(text);
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currentToken = inputTokens.Count - 1;
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}
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void LoadVocabulary()
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{
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var jsonText = File.ReadAllText(Application.streamingAssetsPath
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vocab = Newtonsoft.Json.JsonConvert.DeserializeObject<Dictionary<string, int>>(jsonText);
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tokens = new string[vocab.Count];
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foreach (var item in vocab)
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tokens[item.Value] = item.Key;
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}
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merges = File.ReadAllLines(Application.streamingAssetsPath
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}
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int SelectRandomToken(float[] probs)
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{
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float p = UnityEngine.Random.Range(0, 1f);
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float t = 0;
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for (int i = 0; i < probs.Length; i++)
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{
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t += probs[i];
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if (p < t)
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{
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return i;
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}
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}
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return probs.Length - 1;
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}
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// Translates encoded special characters to Unicode
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for (int i = 0, n = 0; i < 256; i++)
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{
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encodedCharacters[i] = i;
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if (IsWhiteSpace(
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{
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encodedCharacters[i] = n + 256;
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whiteSpaceCharacters[n++] = i;
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}
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}
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bool IsWhiteSpace(
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{
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}
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List<int> GetTokens(string text)
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private void OnDestroy()
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{
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engine?.Dispose();
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ops?.Dispose();
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allocator?.Dispose();
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}
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}
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using UnityEngine;
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using Unity.Sentis;
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using System.IO;
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using System.Text;
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using FF = Unity.Sentis.Functional;
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/*
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* Tiny Stories Inference Code
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*
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* In Assets/StreamingAssets put:
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*
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* tinystories.sentis (or put in asset folder and drag onto field)
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* vocab.json
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* merges.txt
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*
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public class RunTinyStories : MonoBehaviour
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{
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//Drop the tinystories.sentis or onnx file on here if using an asset:
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//public ModelAsset asset;
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const BackendType backend = BackendType.GPUCompute;
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//string outputString = "Once upon a time, there were three bears";
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//Special tokens
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const int END_OF_TEXT = 50256;
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//Store the vocabulary
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string[] tokens;
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void Start()
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{
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SetupWhiteSpaceShifts();
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LoadVocabulary();
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var model1 = ModelLoader.Load(Path.Join(Application.streamingAssetsPath , "tinystories.sentis"));
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//var model1 = ModelLoader.Load(asset);
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//Create a new model to select the random token:
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var model2 = FF.Compile(
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(input, currentToken) =>
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{
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var row = FF.Select(model1.Forward(input)[8], 1, currentToken);
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return FF.Multinomial(predictability * row, 1);
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},
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(InputDef.FromModel(model1)[0], new InputDef(DataType.Int, new TensorShape()))
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);
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engine = WorkerFactory.CreateWorker(backend, model2);
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DecodePrompt(outputString);
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void RunInference()
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{
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using var tokensSoFar = new TensorInt(new TensorShape(1, maxTokens), outputTokens);
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using var index = new TensorInt(currentToken);
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engine.Execute(new Dictionary<string, Tensor> { {"input_0", tokensSoFar }, { "input_1", index }});
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var probs = engine.PeekOutput() as TensorInt;
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Debug.Log(probs.shape);
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probs.CompleteOperationsAndDownload();
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int ID = probs[0];
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//shift window down if got to the end
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if (currentToken >= maxTokens - 1)
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{
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for (int i = 0; i < maxTokens - 1; i++) outputTokens[i] = outputTokens[i + 1];
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}
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void DecodePrompt(string text)
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{
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var inputTokens = GetTokens(text);
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currentToken = inputTokens.Count - 1;
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}
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void LoadVocabulary()
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{
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var jsonText = File.ReadAllText(Path.Join(Application.streamingAssetsPath , "vocab.json"));
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vocab = Newtonsoft.Json.JsonConvert.DeserializeObject<Dictionary<string, int>>(jsonText);
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tokens = new string[vocab.Count];
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foreach (var item in vocab)
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tokens[item.Value] = item.Key;
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}
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merges = File.ReadAllLines(Path.Join(Application.streamingAssetsPath , "merges.txt"));
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}
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// Translates encoded special characters to Unicode
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for (int i = 0, n = 0; i < 256; i++)
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{
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encodedCharacters[i] = i;
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if (IsWhiteSpace(i))
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{
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encodedCharacters[i] = n + 256;
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whiteSpaceCharacters[n++] = i;
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}
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}
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bool IsWhiteSpace(int i)
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{
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//returns true if it is a whitespace character
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return i <= 32 || (i >= 127 && i <= 160) || i == 173;
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}
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List<int> GetTokens(string text)
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private void OnDestroy()
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{
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engine?.Dispose();
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}
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}
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info.json
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"merges.txt"
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],
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"version": [
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-
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]
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}
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"merges.txt"
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],
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"version": [
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"1.4.0"
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]
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}
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tinystories.sentis
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:c7962eb7db56b241cc19cd3f0cffcf5d76d3c35639917f07effa6b3c242c91e9
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size 478818076
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