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DeepSeek-R1-Distill-Qwen-1.5B

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  1. deepseek_tflite.ipynb +1146 -29
deepseek_tflite.ipynb CHANGED
@@ -11,6 +11,1036 @@
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  },
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  "language_info": {
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  "name": "python"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }
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  "cells": [
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  "colab": {
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  "base_uri": "https://localhost:8080/"
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  "execution_count": 1,
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  "outputs": [
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  "output_type": "stream",
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  "name": "stdout",
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  "text": [
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- "Requirement already satisfied: ai-edge-litert in /usr/local/lib/python3.11/dist-packages (1.1.2)\n",
 
45
  "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.11/dist-packages (from ai-edge-litert) (25.2.10)\n",
46
- "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from ai-edge-litert) (1.26.4)\n"
 
 
 
 
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  ]
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  }
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  ]
@@ -80,10 +1115,43 @@
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  "model_path = hf_hub_download(repo_id=\"litert-community/DeepSeek-R1-Distill-Qwen-1.5B\", filename=\"deepseek_q8_seq128_ekv1280.tflite\")"
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  "execution_count": 3,
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  "cell_type": "markdown",
@@ -105,10 +1173,68 @@
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  "tokenizer = AutoTokenizer.from_pretrained(\"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\")"
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  ],
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  "metadata": {
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  {
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  "cell_type": "code",
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  "source": [
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- "\n",
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- "\n",
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  "class LiteRTLlmPipeline:\n",
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  "\n",
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  " def __init__(self, interpreter, tokenizer):\n",
@@ -143,7 +1267,6 @@
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  " - self._max_seq_len: The maximum sequence length supported by the model.\n",
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  " - self._max_kv_cache_seq_len: The maximum sequence length supported by the\n",
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  " KV cache.\n",
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- " - self._num_layers: The number of layers in the model.\n",
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  "\n",
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  " Args:\n",
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  " num_input_tokens: The number of input tokens.\n",
@@ -169,26 +1292,20 @@
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  " ]\n",
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  " self._max_kv_cache_seq_len = kv_cache_shape[1]\n",
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  "\n",
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- " # The two arguments excluded are `tokens` and `input_pos`. Dividing by 2\n",
173
- " # because each layer has key and value caches.\n",
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- " self._num_layers = (\n",
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- " len(self._prefill_runner.get_input_details().keys()) - 2\n",
176
- " ) // 2\n",
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- "\n",
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- "\n",
179
  " def _init_kv_cache(self) -> dict[str, np.ndarray]:\n",
180
  " if self._prefill_runner is None:\n",
181
  " raise ValueError(\"Prefill runner is not initialized.\")\n",
182
  " kv_cache = {}\n",
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- " for i in range(self._num_layers):\n",
184
- " kv_cache[f\"kv_cache_k_{i}\"] = np.zeros(\n",
185
- " self._prefill_runner.get_input_details()[f\"kv_cache_k_{i}\"][\"shape\"],\n",
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- " dtype=np.float32,\n",
187
- " )\n",
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- " kv_cache[f\"kv_cache_v_{i}\"] = np.zeros(\n",
189
- " self._prefill_runner.get_input_details()[f\"kv_cache_v_{i}\"][\"shape\"],\n",
190
- " dtype=np.float32,\n",
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- " )\n",
 
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  " return kv_cache\n",
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  "\n",
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  " def _get_prefill_runner(self, num_input_tokens: int) :\n",
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- "prompt = \"what is 8 mod 5\"\n",
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  "output = pipeline.generate(prompt, max_decode_steps = None)"
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  "cells": [
 
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1064
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1071
  "output_type": "stream",
1072
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1073
  "text": [
1074
+ "Collecting ai-edge-litert\n",
1075
+ " Downloading ai_edge_litert-1.1.2-cp311-cp311-manylinux_2_17_x86_64.whl.metadata (1.6 kB)\n",
1076
  "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.11/dist-packages (from ai-edge-litert) (25.2.10)\n",
1077
+ "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from ai-edge-litert) (1.26.4)\n",
1078
+ "Downloading ai_edge_litert-1.1.2-cp311-cp311-manylinux_2_17_x86_64.whl (3.5 MB)\n",
1079
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.5/3.5 MB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1080
+ "\u001b[?25hInstalling collected packages: ai-edge-litert\n",
1081
+ "Successfully installed ai-edge-litert-1.1.2\n"
1082
  ]
1083
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1084
  ]
 
1115
  "model_path = hf_hub_download(repo_id=\"litert-community/DeepSeek-R1-Distill-Qwen-1.5B\", filename=\"deepseek_q8_seq128_ekv1280.tflite\")"
1116
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1117
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1157
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1173
  "tokenizer = AutoTokenizer.from_pretrained(\"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\")"
1174
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1175
  "metadata": {
1176
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  {
1240
  "cell_type": "markdown",
 
1248
  {
1249
  "cell_type": "code",
1250
  "source": [
 
 
1251
  "class LiteRTLlmPipeline:\n",
1252
  "\n",
1253
  " def __init__(self, interpreter, tokenizer):\n",
 
1267
  " - self._max_seq_len: The maximum sequence length supported by the model.\n",
1268
  " - self._max_kv_cache_seq_len: The maximum sequence length supported by the\n",
1269
  " KV cache.\n",
 
1270
  "\n",
1271
  " Args:\n",
1272
  " num_input_tokens: The number of input tokens.\n",
 
1292
  " ]\n",
1293
  " self._max_kv_cache_seq_len = kv_cache_shape[1]\n",
1294
  "\n",
 
 
 
 
 
 
 
1295
  " def _init_kv_cache(self) -> dict[str, np.ndarray]:\n",
1296
  " if self._prefill_runner is None:\n",
1297
  " raise ValueError(\"Prefill runner is not initialized.\")\n",
1298
  " kv_cache = {}\n",
1299
+ " for input_key in self._prefill_runner.get_input_details().keys():\n",
1300
+ " if \"kv_cache\" in input_key:\n",
1301
+ " kv_cache[input_key] = np.zeros(\n",
1302
+ " self._prefill_runner.get_input_details()[input_key][\"shape\"],\n",
1303
+ " dtype=np.float32,\n",
1304
+ " )\n",
1305
+ " kv_cache[input_key] = np.zeros(\n",
1306
+ " self._prefill_runner.get_input_details()[input_key][\"shape\"],\n",
1307
+ " dtype=np.float32,\n",
1308
+ " )\n",
1309
  " return kv_cache\n",
1310
  "\n",
1311
  " def _get_prefill_runner(self, num_input_tokens: int) :\n",
 
1459
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1460
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1461
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1462
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1463
  "outputs": []
1464
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1465
  {
 
1480
  "metadata": {
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1482
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1483
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1484
  "outputs": []
1485
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1486
  {
1487
  "cell_type": "code",
1488
  "source": [
1489
+ "prompt = \"what is the capital of France\"\n",
1490
  "output = pipeline.generate(prompt, max_decode_steps = None)"
1491
  ],
1492
  "metadata": {