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Inference Endpoints
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+ }
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+ "model_name": "DescriptionStyleModel",
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+ "model_module_version": "1.5.0",
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+ "state": {
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+ "_model_module": "@jupyter-widgets/controls",
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+ "_model_module_version": "1.5.0",
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+ "_model_name": "DescriptionStyleModel",
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+ "_view_count": null,
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+ "_view_module": "@jupyter-widgets/base",
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+ "_view_module_version": "1.2.0",
1726
+ "_view_name": "StyleView",
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+ "description_width": ""
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+ }
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+ }
1730
+ }
1731
+ }
1732
+ },
1733
+ "cells": [
1734
+ {
1735
+ "cell_type": "code",
1736
+ "execution_count": 1,
1737
+ "metadata": {
1738
+ "colab": {
1739
+ "base_uri": "https://localhost:8080/"
1740
+ },
1741
+ "id": "JyGfY-l3N9_W",
1742
+ "outputId": "628e000f-8db0-42bd-8f97-3ad4d5faff3f"
1743
+ },
1744
+ "outputs": [
1745
+ {
1746
+ "output_type": "stream",
1747
+ "name": "stdout",
1748
+ "text": [
1749
+ "Mounted at /gdrive\n",
1750
+ "ln: failed to create symbolic link '/content/workspace/workspace': File exists\n",
1751
+ "ln: failed to create symbolic link '/content/home/home': File exists\n",
1752
+ "ln: failed to create symbolic link '/content/data/data': File exists\n",
1753
+ "env: HOME=/content/drive/MyDrive/home\n",
1754
+ "/content/drive/MyDrive/home\n"
1755
+ ]
1756
+ }
1757
+ ],
1758
+ "source": [
1759
+ "# from google.colab import drive\n",
1760
+ "# drive.mount('/gdrive',force_remount=True)\n",
1761
+ "# !ln -s \"/gdrive/My Drive/workspace\" \"/content/workspace\"\n",
1762
+ "# !ln -s \"/gdrive/My Drive/home\" \"/content/home\"\n",
1763
+ "# !ln -s \"/gdrive/My Drive/data\" \"/content/data\"\n",
1764
+ "# %env HOME=/content/drive/MyDrive/home\n",
1765
+ "# !eval echo ~$USER"
1766
+ ]
1767
+ },
1768
+ {
1769
+ "cell_type": "code",
1770
+ "source": [
1771
+ "!export LC_ALL=\"en_US.UTF-8\"\n",
1772
+ "!export LD_LIBRARY_PATH=\"/usr/lib64-nvidia\"\n",
1773
+ "!export LIBRARY_PATH=\"/usr/local/cuda/lib64/stubs\"\n",
1774
+ "!ldconfig /usr/lib64-nvidia"
1775
+ ],
1776
+ "metadata": {
1777
+ "colab": {
1778
+ "base_uri": "https://localhost:8080/"
1779
+ },
1780
+ "id": "gxA7zmFIvuAG",
1781
+ "outputId": "bff0d73e-ca3c-4379-aa40-5363d0e3f1fe"
1782
+ },
1783
+ "execution_count": 1,
1784
+ "outputs": [
1785
+ {
1786
+ "output_type": "stream",
1787
+ "name": "stdout",
1788
+ "text": [
1789
+ "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
1790
+ "\n",
1791
+ "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
1792
+ "\n",
1793
+ "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
1794
+ "\n",
1795
+ "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
1796
+ "\n",
1797
+ "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
1798
+ "\n",
1799
+ "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
1800
+ "\n"
1801
+ ]
1802
+ }
1803
+ ]
1804
+ },
1805
+ {
1806
+ "cell_type": "code",
1807
+ "source": [
1808
+ "!pip install -q causal-conv1d==1.0.0\n",
1809
+ "!pip install -q mamba-ssm==1.0.1"
1810
+ ],
1811
+ "metadata": {
1812
+ "id": "oWado1xXOZvs"
1813
+ },
1814
+ "execution_count": 3,
1815
+ "outputs": []
1816
+ },
1817
+ {
1818
+ "cell_type": "code",
1819
+ "source": [
1820
+ "# from google.colab import output\n",
1821
+ "# output.clear()\n",
1822
+ "# del tokenizer\n",
1823
+ "# del model"
1824
+ ],
1825
+ "metadata": {
1826
+ "id": "jMftjcXoHbQ8"
1827
+ },
1828
+ "execution_count": 18,
1829
+ "outputs": []
1830
+ },
1831
+ {
1832
+ "cell_type": "code",
1833
+ "source": [
1834
+ "import torch\n",
1835
+ "from transformers import AutoTokenizer\n",
1836
+ "from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\n",
1837
+ "\n",
1838
+ "# Check if GPU is available and set the device accordingly\n",
1839
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
1840
+ "\n",
1841
+ "# Print out the device information\n",
1842
+ "if device.type == 'cuda':\n",
1843
+ " print(f\"Using GPU: {torch.cuda.get_device_name(0)}\")\n",
1844
+ "else:\n",
1845
+ " print(\"Using CPU\")\n",
1846
+ "\n",
1847
+ "# Load the tokenizer\n",
1848
+ "tokenizer = AutoTokenizer.from_pretrained(\"venkycs/mamba-2.8b-cybersec\")\n",
1849
+ "tokenizer.eos_token = \"<|endoftext|>\"\n",
1850
+ "tokenizer.pad_token = tokenizer.eos_token\n",
1851
+ "\n",
1852
+ "# Load the model\n",
1853
+ "model = MambaLMHeadModel.from_pretrained(\"venkycs/mamba-2.8b-cybersec\")\n",
1854
+ "\n",
1855
+ "# Move the model to the selected device\n",
1856
+ "model = model.to(device)\n",
1857
+ "\n",
1858
+ "# Rest of your code for model usage follows...\n"
1859
+ ],
1860
+ "metadata": {
1861
+ "id": "3OotKz-7PZ_Z",
1862
+ "colab": {
1863
+ "base_uri": "https://localhost:8080/",
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+ "height": 230,
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+ "referenced_widgets": [
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+ "1d382eaa72e14f3aa2a48c5dba9f2e8d",
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+ "0feb2581de7340d593500bc920913180",
1905
+ "8bd252feb31749f9b84867aa864841fd",
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+ "dce17a5dfe93445e86eab212e2a1a1cd",
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+ "c5e9a9c9d8504b28901c6e8d191ad7ec",
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+ "073bc18d8cd74c1c8e227cd349a51d85",
1909
+ "2f147c232ee945f9bf2542c4548eec16",
1910
+ "a6dd77ab2a2548f8bad8928beac6c474",
1911
+ "47007135f8cb4dabbfce20ee564b32cb",
1912
+ "c6d7f45118b14ade9fd6f6891eefe686",
1913
+ "0258f73607dd47d28cfd3f69a1710ce0",
1914
+ "2c4c78a83f444ef7b464c7f5fbb48ade",
1915
+ "5c5d0c8aed734cd1a3eced971ca2ce6b",
1916
+ "0aceba2582eb4da090606ac62dfc4713",
1917
+ "f906ebf80e714e019c30512f7bb49b2c",
1918
+ "b3e491041ac84f85950ee72d1f174347",
1919
+ "a0cde38d14df40cabd59bd771659c286",
1920
+ "8d62f1f34ba34f18b5b7a99faff2dfd6"
1921
+ ]
1922
+ },
1923
+ "outputId": "7b533315-a82f-464b-a124-331dbc20f3af"
1924
+ },
1925
+ "execution_count": 2,
1926
+ "outputs": [
1927
+ {
1928
+ "output_type": "stream",
1929
+ "name": "stdout",
1930
+ "text": [
1931
+ "Using GPU: Tesla V100-SXM2-16GB\n"
1932
+ ]
1933
+ },
1934
+ {
1935
+ "output_type": "display_data",
1936
+ "data": {
1937
+ "text/plain": [
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+ "tokenizer_config.json: 0%| | 0.00/5.35k [00:00<?, ?B/s]"
1939
+ ],
1940
+ "application/vnd.jupyter.widget-view+json": {
1941
+ "version_major": 2,
1942
+ "version_minor": 0,
1943
+ "model_id": "25ac757f00114342abfe1d1b30265214"
1944
+ }
1945
+ },
1946
+ "metadata": {}
1947
+ },
1948
+ {
1949
+ "output_type": "display_data",
1950
+ "data": {
1951
+ "text/plain": [
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+ "tokenizer.json: 0%| | 0.00/2.11M [00:00<?, ?B/s]"
1953
+ ],
1954
+ "application/vnd.jupyter.widget-view+json": {
1955
+ "version_major": 2,
1956
+ "version_minor": 0,
1957
+ "model_id": "494fa84f1d144081a22f211784399137"
1958
+ }
1959
+ },
1960
+ "metadata": {}
1961
+ },
1962
+ {
1963
+ "output_type": "display_data",
1964
+ "data": {
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+ "text/plain": [
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+ "special_tokens_map.json: 0%| | 0.00/359 [00:00<?, ?B/s]"
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+ ],
1968
+ "application/vnd.jupyter.widget-view+json": {
1969
+ "version_major": 2,
1970
+ "version_minor": 0,
1971
+ "model_id": "c06c9f29f97f4953a8c0da04891fe67c"
1972
+ }
1973
+ },
1974
+ "metadata": {}
1975
+ },
1976
+ {
1977
+ "output_type": "stream",
1978
+ "name": "stderr",
1979
+ "text": [
1980
+ "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
1981
+ ]
1982
+ },
1983
+ {
1984
+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ "config.json: 0%| | 0.00/200 [00:00<?, ?B/s]"
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+ ],
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+ "application/vnd.jupyter.widget-view+json": {
1990
+ "version_major": 2,
1991
+ "version_minor": 0,
1992
+ "model_id": "206af374676748de9e11df96ad2df295"
1993
+ }
1994
+ },
1995
+ "metadata": {}
1996
+ },
1997
+ {
1998
+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ "pytorch_model.bin: 0%| | 0.00/5.54G [00:00<?, ?B/s]"
2002
+ ],
2003
+ "application/vnd.jupyter.widget-view+json": {
2004
+ "version_major": 2,
2005
+ "version_minor": 0,
2006
+ "model_id": "a6dd77ab2a2548f8bad8928beac6c474"
2007
+ }
2008
+ },
2009
+ "metadata": {}
2010
+ }
2011
+ ]
2012
+ },
2013
+ {
2014
+ "cell_type": "code",
2015
+ "source": [
2016
+ "model"
2017
+ ],
2018
+ "metadata": {
2019
+ "colab": {
2020
+ "base_uri": "https://localhost:8080/"
2021
+ },
2022
+ "id": "oZzGhRUkt8fP",
2023
+ "outputId": "2d431cce-a737-4678-b931-a68215f6b6d1"
2024
+ },
2025
+ "execution_count": 3,
2026
+ "outputs": [
2027
+ {
2028
+ "output_type": "execute_result",
2029
+ "data": {
2030
+ "text/plain": [
2031
+ "MambaLMHeadModel(\n",
2032
+ " (backbone): MixerModel(\n",
2033
+ " (embedding): Embedding(50280, 2560)\n",
2034
+ " (layers): ModuleList(\n",
2035
+ " (0-63): 64 x Block(\n",
2036
+ " (mixer): Mamba(\n",
2037
+ " (in_proj): Linear(in_features=2560, out_features=10240, bias=False)\n",
2038
+ " (conv1d): Conv1d(5120, 5120, kernel_size=(4,), stride=(1,), padding=(3,), groups=5120)\n",
2039
+ " (act): SiLU()\n",
2040
+ " (x_proj): Linear(in_features=5120, out_features=192, bias=False)\n",
2041
+ " (dt_proj): Linear(in_features=160, out_features=5120, bias=True)\n",
2042
+ " (out_proj): Linear(in_features=5120, out_features=2560, bias=False)\n",
2043
+ " )\n",
2044
+ " (norm): RMSNorm()\n",
2045
+ " )\n",
2046
+ " )\n",
2047
+ " (norm_f): RMSNorm()\n",
2048
+ " )\n",
2049
+ " (lm_head): Linear(in_features=2560, out_features=50280, bias=False)\n",
2050
+ ")"
2051
+ ]
2052
+ },
2053
+ "metadata": {},
2054
+ "execution_count": 3
2055
+ }
2056
+ ]
2057
+ },
2058
+ {
2059
+ "cell_type": "code",
2060
+ "source": [
2061
+ "# Print memory footprint of the model\n",
2062
+ "if device.type == 'cuda':\n",
2063
+ " print(f\"Model memory footprint: {torch.cuda.memory_allocated(device)/1024**2:.2f} MB\")\n",
2064
+ "else:\n",
2065
+ " print(\"Memory footprint measurement is only available for GPU.\")"
2066
+ ],
2067
+ "metadata": {
2068
+ "colab": {
2069
+ "base_uri": "https://localhost:8080/"
2070
+ },
2071
+ "id": "luB8-Eq4uCph",
2072
+ "outputId": "9510c659-bae6-414d-96f4-4171f2f840c7"
2073
+ },
2074
+ "execution_count": 4,
2075
+ "outputs": [
2076
+ {
2077
+ "output_type": "stream",
2078
+ "name": "stdout",
2079
+ "text": [
2080
+ "Model memory footprint: 10561.38 MB\n"
2081
+ ]
2082
+ }
2083
+ ]
2084
+ },
2085
+ {
2086
+ "cell_type": "code",
2087
+ "source": [
2088
+ "model = model.half()"
2089
+ ],
2090
+ "metadata": {
2091
+ "id": "QYfDpTX4uM1d"
2092
+ },
2093
+ "execution_count": 5,
2094
+ "outputs": []
2095
+ },
2096
+ {
2097
+ "cell_type": "code",
2098
+ "source": [
2099
+ "# Print memory footprint of the model\n",
2100
+ "if device.type == 'cuda':\n",
2101
+ " print(f\"Model memory footprint: {torch.cuda.memory_allocated(device)/1024**2:.2f} MB\")\n",
2102
+ "else:\n",
2103
+ " print(\"Memory footprint measurement is only available for GPU.\")"
2104
+ ],
2105
+ "metadata": {
2106
+ "colab": {
2107
+ "base_uri": "https://localhost:8080/"
2108
+ },
2109
+ "id": "VTY0e20puQzs",
2110
+ "outputId": "92288a12-05bd-44eb-b72f-698c0b7aeef0"
2111
+ },
2112
+ "execution_count": 6,
2113
+ "outputs": [
2114
+ {
2115
+ "output_type": "stream",
2116
+ "name": "stdout",
2117
+ "text": [
2118
+ "Model memory footprint: 5345.00 MB\n"
2119
+ ]
2120
+ }
2121
+ ]
2122
+ },
2123
+ {
2124
+ "cell_type": "code",
2125
+ "source": [
2126
+ "messages = []\n",
2127
+ "\n",
2128
+ "# Informing the user about how to exit the chat\n",
2129
+ "print(\"You can exit the chat anytime by typing 'bye' or 'quit'.\")\n",
2130
+ "\n",
2131
+ "while True:\n",
2132
+ " user_message = input(\"\\nUser: \")\n",
2133
+ "\n",
2134
+ " # Check if the user's message is 'bye' or 'quit'\n",
2135
+ " if user_message.lower() in ['bye', 'quit']:\n",
2136
+ " print(\"Exiting the chat.\")\n",
2137
+ " break\n",
2138
+ "\n",
2139
+ " messages.append(dict(\n",
2140
+ " role=\"user\",\n",
2141
+ " content=user_message\n",
2142
+ " ))\n",
2143
+ "\n",
2144
+ " input_ids = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", add_generation_prompt=True).to(device)\n",
2145
+ "\n",
2146
+ " out = model.generate(input_ids=input_ids, max_length=2000, temperature=0.9, top_p=0.7, eos_token_id=tokenizer.eos_token_id)\n",
2147
+ "\n",
2148
+ " decoded = tokenizer.batch_decode(out)\n",
2149
+ " messages.append(dict(\n",
2150
+ " role=\"assistant\",\n",
2151
+ " content=decoded[0].split(\"\\n\")[-2])\n",
2152
+ " )\n",
2153
+ "\n",
2154
+ " print(\"AI Assistant:\", decoded[0].split(\"\\n\")[-1])\n"
2155
+ ],
2156
+ "metadata": {
2157
+ "colab": {
2158
+ "base_uri": "https://localhost:8080/"
2159
+ },
2160
+ "id": "CzhhSwMcuS4d",
2161
+ "outputId": "45834eec-7443-434d-d304-4f495eeaeec7"
2162
+ },
2163
+ "execution_count": null,
2164
+ "outputs": [
2165
+ {
2166
+ "output_type": "stream",
2167
+ "name": "stdout",
2168
+ "text": [
2169
+ "You can exit the chat anytime by typing 'bye' or 'quit'.\n",
2170
+ "\n",
2171
+ "User: hi\n",
2172
+ "AI Assistant: Hello! I'm a helpful AI Assistant and expert in Security Incident Handling. I help organizations develop Security Incident Handling related training with appropriate solutions. I provide specific scenarios and clear steps for better understanding.<|endoftext|>\n",
2173
+ "\n",
2174
+ "User: How can AI be used to enhance network security?\n",
2175
+ "AI Assistant: AI can be used to enhance network security in various ways, such as detecting and responding to threats in real-time, identifying anomalous behavior, and automating security incident response. AI can also help in predicting and preventing future security breaches by analyzing historical data and identifying patterns of attack.<|endoftext|>\n",
2176
+ "\n",
2177
+ "User: Can you explain how the MITRE ATT&CK framework can be integrated into security training programs?\n",
2178
+ "AI Assistant: Certainly! The MITRE ATT&CK framework can be integrated into security training programs by first familiarizing the participants with the ATT&CK matrix, which outlines various tactics and techniques used by threat actors. Then, educators can use this framework to create scenario-based training exercises that simulate real-world cyber attacks. Participants can practice identifying and responding to these tactics, which will enhance their ability to detect and mitigate cyber threats in real-world situations.<|endoftext|>\n",
2179
+ "\n",
2180
+ "User: Give me more information and I want to learn practical steps \n",
2181
+ "AI Assistant: Certainly! The MITRE ATT&CK framework is a comprehensive matrix of tactics and techniques used by threat actors. To integrate it into security training programs, you can start by creating a module that introduces the framework's structure and concepts. Then, develop scenarios that walk through real-world attack scenarios based on MITRE ATT&CK tactics and techniques. Include hands-on exercises where trainees can practice identifying and responding to these tactics using simulated attack scenarios. This practical approach will help trainees understand how to apply the framework in real-world situations.<|endoftext|>\n",
2182
+ "\n",
2183
+ "User: Give me markdown format, high level steps may be 10 key points\n",
2184
+ "AI Assistant: By following these steps, you can ensure that your organization's security training program is practical and effective in mitigating the risks associated with network security.<|endoftext|>\n",
2185
+ "\n",
2186
+ "User: give me 3 more \n",
2187
+ "AI Assistant: By integrating these steps, organizations can better prepare for and respond to cyber threats using the MITRE ATT&CK framework.<|endoftext|>\n"
2188
+ ]
2189
+ }
2190
+ ]
2191
+ },
2192
+ {
2193
+ "cell_type": "code",
2194
+ "source": [
2195
+ "# Better to save locally, these model is not yet convered to hf\n",
2196
+ "# tokenizer.save_pretrained('/content/home/mamba2.8b-cybersec')"
2197
+ ],
2198
+ "metadata": {
2199
+ "colab": {
2200
+ "base_uri": "https://localhost:8080/"
2201
+ },
2202
+ "id": "eeueGN3_zd7x",
2203
+ "outputId": "3e6f8af2-66ea-4081-c79b-1f231a315771"
2204
+ },
2205
+ "execution_count": 17,
2206
+ "outputs": [
2207
+ {
2208
+ "output_type": "execute_result",
2209
+ "data": {
2210
+ "text/plain": [
2211
+ "('/content/home/mamba2.8b-cybersec/tokenizer_config.json',\n",
2212
+ " '/content/home/mamba2.8b-cybersec/special_tokens_map.json',\n",
2213
+ " '/content/home/mamba2.8b-cybersec/tokenizer.json')"
2214
+ ]
2215
+ },
2216
+ "metadata": {},
2217
+ "execution_count": 17
2218
+ }
2219
+ ]
2220
+ },
2221
+ {
2222
+ "cell_type": "code",
2223
+ "source": [
2224
+ "# save_directory = \"/content/home/mamba2.8b-cybersec\"\n",
2225
+ "# # Save the model weights\n",
2226
+ "# # model.save_pretrained('/content/home/mamba2.8b-cybersec')\n",
2227
+ "# model_weights_path = os.path.join(save_directory, 'pytorch_model.bin')\n",
2228
+ "# torch.save(model.state_dict(), model_weights_path)"
2229
+ ],
2230
+ "metadata": {
2231
+ "id": "JSEWOQDoz_E0"
2232
+ },
2233
+ "execution_count": 16,
2234
+ "outputs": []
2235
+ }
2236
+ ]
2237
+ }