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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"from huggingface_hub import hf_hub_download\n",
"from transformers import AutoTokenizer\n",
"\n",
"from model.distilbert import DistilBertClassificationModel\n",
"from model.llama import LlamaClassificationModel"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"repo_id = \"ppak10/defect-classification-llama-baseline-25-epochs\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LlamaConfig {\n",
" \"_attn_implementation_autoset\": true,\n",
" \"_name_or_path\": \"meta-llama/Llama-3.2-1B\",\n",
" \"architectures\": [\n",
" \"LlamaForCausalLM\"\n",
" ],\n",
" \"attention_bias\": false,\n",
" \"attention_dropout\": 0.0,\n",
" \"bos_token_id\": 128000,\n",
" \"eos_token_id\": 128001,\n",
" \"head_dim\": 64,\n",
" \"hidden_act\": \"silu\",\n",
" \"hidden_size\": 2048,\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 8192,\n",
" \"max_position_embeddings\": 131072,\n",
" \"mlp_bias\": false,\n",
" \"model_type\": \"llama\",\n",
" \"num_attention_heads\": 32,\n",
" \"num_hidden_layers\": 16,\n",
" \"num_key_value_heads\": 8,\n",
" \"pretraining_tp\": 1,\n",
" \"rms_norm_eps\": 1e-05,\n",
" \"rope_scaling\": {\n",
" \"factor\": 32.0,\n",
" \"high_freq_factor\": 4.0,\n",
" \"low_freq_factor\": 1.0,\n",
" \"original_max_position_embeddings\": 8192,\n",
" \"rope_type\": \"llama3\"\n",
" },\n",
" \"rope_theta\": 500000.0,\n",
" \"tie_word_embeddings\": true,\n",
" \"torch_dtype\": \"bfloat16\",\n",
" \"transformers_version\": \"4.47.0\",\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 128256\n",
"}\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_3716586/1335258174.py:14: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" model.classifier.load_state_dict(torch.load(classification_head_path))\n"
]
},
{
"data": {
"text/plain": [
"LlamaClassificationModel(\n",
" (base_model): LlamaModel(\n",
" (embed_tokens): Embedding(128256, 2048)\n",
" (layers): ModuleList(\n",
" (0-15): 16 x LlamaDecoderLayer(\n",
" (self_attn): LlamaSdpaAttention(\n",
" (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (k_proj): Linear(in_features=2048, out_features=512, bias=False)\n",
" (v_proj): Linear(in_features=2048, out_features=512, bias=False)\n",
" (o_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (rotary_emb): LlamaRotaryEmbedding()\n",
" )\n",
" (mlp): LlamaMLP(\n",
" (gate_proj): Linear(in_features=2048, out_features=8192, bias=False)\n",
" (up_proj): Linear(in_features=2048, out_features=8192, bias=False)\n",
" (down_proj): Linear(in_features=8192, out_features=2048, bias=False)\n",
" (act_fn): SiLU()\n",
" )\n",
" (input_layernorm): LlamaRMSNorm((2048,), eps=1e-05)\n",
" (post_attention_layernorm): LlamaRMSNorm((2048,), eps=1e-05)\n",
" )\n",
" )\n",
" (norm): LlamaRMSNorm((2048,), eps=1e-05)\n",
" (rotary_emb): LlamaRotaryEmbedding()\n",
" )\n",
" (classifier): Linear(in_features=2048, out_features=4, bias=True)\n",
")"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Initialize the model\n",
"# model = DistilBertClassificationModel(repo_id)\n",
"model = LlamaClassificationModel()\n",
"\n",
"# Load the tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(repo_id)\n",
"\n",
"classification_head_path = hf_hub_download(\n",
" repo_id=repo_id,\n",
" repo_type=\"model\",\n",
" filename=\"classification_head.pt\"\n",
")\n",
"\n",
"model.classifier.load_state_dict(torch.load(classification_head_path))\n",
"model.eval() # Set the model to evaluation mode"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[1, 0, 0, 0]], dtype=torch.int32)\n"
]
}
],
"source": [
"# text = \"What defects would occur with a beam size of 100 microns, a power of 500 W, a velocity of 100 mm/s and layer height of 10 microns and a hatch spacing of 10 microns for Ti-6Al-4V\"\n",
"# text = \"SS316L[SEP]500 W[SEP]10.0 mm/s[SEP]500.0 microns[SEP]500.0 microns[SEP]100.0 microns\"\n",
"text = \"SS316L[SEP]250.0 W[SEP]280.0 mm/s[SEP][SEP]950.0 microns[SEP]600.0 microns\"\n",
"\n",
"# Ensure the model is on the GPU\n",
"# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"device = \"cpu\"\n",
"model = model.to(device)\n",
"\n",
"# Tokenize input for the entire batch and move to GPU\n",
"inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=\"max_length\", max_length=256)\n",
"inputs = {key: value.to(device) for key, value in inputs.items()}\n",
"\n",
"# Perform inference\n",
"outputs = model(**inputs)\n",
"\n",
"# Extract logits and apply sigmoid activation for multi-label classification\n",
"logits = outputs[\"logits\"]\n",
"probs = torch.sigmoid(logits)\n",
"\n",
"# Convert probabilities to one-hot encoded labels\n",
"preds = (probs > 0.5).int()\n",
"\n",
"# None, keyhole, lack of fusion, balling\n",
"print(preds)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/am/GitHub/LLM-Enabled-Process-Map/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import torch.nn as nn\n",
"from transformers import PreTrainedModel\n",
"\n",
"class PretrainedLlamaClassificationModel(PreTrainedModel):\n",
" def __init__(self, config):\n",
" super().__init__(config)\n",
" self.base_model = AutoModel.from_pretrained(config.model_path, config=config)\n",
" self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n",
" self.config = config\n",
"\n",
" def forward(self, input_ids, attention_mask, labels=None):\n",
" outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)\n",
" summed_representation = outputs.last_hidden_state.sum(dim=1)\n",
" logits = self.classifier(summed_representation)\n",
" loss = None\n",
" if labels is not None:\n",
" loss_fn = nn.BCEWithLogitsLoss()\n",
" loss = loss_fn(logits, labels.float())\n",
" return {\"loss\": loss, \"logits\": logits}\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/am/GitHub/LLM-Enabled-Process-Map/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"Some weights of LlamaModel were not initialized from the model checkpoint at ppak10/defect-classification-llama-baseline-25-epochs and are newly initialized: ['embed_tokens.weight', 'layers.0.input_layernorm.weight', 'layers.0.mlp.down_proj.weight', 'layers.0.mlp.gate_proj.weight', 'layers.0.mlp.up_proj.weight', 'layers.0.post_attention_layernorm.weight', 'layers.0.self_attn.k_proj.weight', 'layers.0.self_attn.o_proj.weight', 'layers.0.self_attn.q_proj.weight', 'layers.0.self_attn.v_proj.weight', 'layers.1.input_layernorm.weight', 'layers.1.mlp.down_proj.weight', 'layers.1.mlp.gate_proj.weight', 'layers.1.mlp.up_proj.weight', 'layers.1.post_attention_layernorm.weight', 'layers.1.self_attn.k_proj.weight', 'layers.1.self_attn.o_proj.weight', 'layers.1.self_attn.q_proj.weight', 'layers.1.self_attn.v_proj.weight', 'layers.10.input_layernorm.weight', 'layers.10.mlp.down_proj.weight', 'layers.10.mlp.gate_proj.weight', 'layers.10.mlp.up_proj.weight', 'layers.10.post_attention_layernorm.weight', 'layers.10.self_attn.k_proj.weight', 'layers.10.self_attn.o_proj.weight', 'layers.10.self_attn.q_proj.weight', 'layers.10.self_attn.v_proj.weight', 'layers.11.input_layernorm.weight', 'layers.11.mlp.down_proj.weight', 'layers.11.mlp.gate_proj.weight', 'layers.11.mlp.up_proj.weight', 'layers.11.post_attention_layernorm.weight', 'layers.11.self_attn.k_proj.weight', 'layers.11.self_attn.o_proj.weight', 'layers.11.self_attn.q_proj.weight', 'layers.11.self_attn.v_proj.weight', 'layers.12.input_layernorm.weight', 'layers.12.mlp.down_proj.weight', 'layers.12.mlp.gate_proj.weight', 'layers.12.mlp.up_proj.weight', 'layers.12.post_attention_layernorm.weight', 'layers.12.self_attn.k_proj.weight', 'layers.12.self_attn.o_proj.weight', 'layers.12.self_attn.q_proj.weight', 'layers.12.self_attn.v_proj.weight', 'layers.13.input_layernorm.weight', 'layers.13.mlp.down_proj.weight', 'layers.13.mlp.gate_proj.weight', 'layers.13.mlp.up_proj.weight', 'layers.13.post_attention_layernorm.weight', 'layers.13.self_attn.k_proj.weight', 'layers.13.self_attn.o_proj.weight', 'layers.13.self_attn.q_proj.weight', 'layers.13.self_attn.v_proj.weight', 'layers.14.input_layernorm.weight', 'layers.14.mlp.down_proj.weight', 'layers.14.mlp.gate_proj.weight', 'layers.14.mlp.up_proj.weight', 'layers.14.post_attention_layernorm.weight', 'layers.14.self_attn.k_proj.weight', 'layers.14.self_attn.o_proj.weight', 'layers.14.self_attn.q_proj.weight', 'layers.14.self_attn.v_proj.weight', 'layers.15.input_layernorm.weight', 'layers.15.mlp.down_proj.weight', 'layers.15.mlp.gate_proj.weight', 'layers.15.mlp.up_proj.weight', 'layers.15.post_attention_layernorm.weight', 'layers.15.self_attn.k_proj.weight', 'layers.15.self_attn.o_proj.weight', 'layers.15.self_attn.q_proj.weight', 'layers.15.self_attn.v_proj.weight', 'layers.16.input_layernorm.weight', 'layers.16.mlp.down_proj.weight', 'layers.16.mlp.gate_proj.weight', 'layers.16.mlp.up_proj.weight', 'layers.16.post_attention_layernorm.weight', 'layers.16.self_attn.k_proj.weight', 'layers.16.self_attn.o_proj.weight', 'layers.16.self_attn.q_proj.weight', 'layers.16.self_attn.v_proj.weight', 'layers.17.input_layernorm.weight', 'layers.17.mlp.down_proj.weight', 'layers.17.mlp.gate_proj.weight', 'layers.17.mlp.up_proj.weight', 'layers.17.post_attention_layernorm.weight', 'layers.17.self_attn.k_proj.weight', 'layers.17.self_attn.o_proj.weight', 'layers.17.self_attn.q_proj.weight', 'layers.17.self_attn.v_proj.weight', 'layers.18.input_layernorm.weight', 'layers.18.mlp.down_proj.weight', 'layers.18.mlp.gate_proj.weight', 'layers.18.mlp.up_proj.weight', 'layers.18.post_attention_layernorm.weight', 'layers.18.self_attn.k_proj.weight', 'layers.18.self_attn.o_proj.weight', 'layers.18.self_attn.q_proj.weight', 'layers.18.self_attn.v_proj.weight', 'layers.19.input_layernorm.weight', 'layers.19.mlp.down_proj.weight', 'layers.19.mlp.gate_proj.weight', 'layers.19.mlp.up_proj.weight', 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'layers.3.post_attention_layernorm.weight', 'layers.3.self_attn.k_proj.weight', 'layers.3.self_attn.o_proj.weight', 'layers.3.self_attn.q_proj.weight', 'layers.3.self_attn.v_proj.weight', 'layers.30.input_layernorm.weight', 'layers.30.mlp.down_proj.weight', 'layers.30.mlp.gate_proj.weight', 'layers.30.mlp.up_proj.weight', 'layers.30.post_attention_layernorm.weight', 'layers.30.self_attn.k_proj.weight', 'layers.30.self_attn.o_proj.weight', 'layers.30.self_attn.q_proj.weight', 'layers.30.self_attn.v_proj.weight', 'layers.31.input_layernorm.weight', 'layers.31.mlp.down_proj.weight', 'layers.31.mlp.gate_proj.weight', 'layers.31.mlp.up_proj.weight', 'layers.31.post_attention_layernorm.weight', 'layers.31.self_attn.k_proj.weight', 'layers.31.self_attn.o_proj.weight', 'layers.31.self_attn.q_proj.weight', 'layers.31.self_attn.v_proj.weight', 'layers.4.input_layernorm.weight', 'layers.4.mlp.down_proj.weight', 'layers.4.mlp.gate_proj.weight', 'layers.4.mlp.up_proj.weight', 'layers.4.post_attention_layernorm.weight', 'layers.4.self_attn.k_proj.weight', 'layers.4.self_attn.o_proj.weight', 'layers.4.self_attn.q_proj.weight', 'layers.4.self_attn.v_proj.weight', 'layers.5.input_layernorm.weight', 'layers.5.mlp.down_proj.weight', 'layers.5.mlp.gate_proj.weight', 'layers.5.mlp.up_proj.weight', 'layers.5.post_attention_layernorm.weight', 'layers.5.self_attn.k_proj.weight', 'layers.5.self_attn.o_proj.weight', 'layers.5.self_attn.q_proj.weight', 'layers.5.self_attn.v_proj.weight', 'layers.6.input_layernorm.weight', 'layers.6.mlp.down_proj.weight', 'layers.6.mlp.gate_proj.weight', 'layers.6.mlp.up_proj.weight', 'layers.6.post_attention_layernorm.weight', 'layers.6.self_attn.k_proj.weight', 'layers.6.self_attn.o_proj.weight', 'layers.6.self_attn.q_proj.weight', 'layers.6.self_attn.v_proj.weight', 'layers.7.input_layernorm.weight', 'layers.7.mlp.down_proj.weight', 'layers.7.mlp.gate_proj.weight', 'layers.7.mlp.up_proj.weight', 'layers.7.post_attention_layernorm.weight', 'layers.7.self_attn.k_proj.weight', 'layers.7.self_attn.o_proj.weight', 'layers.7.self_attn.q_proj.weight', 'layers.7.self_attn.v_proj.weight', 'layers.8.input_layernorm.weight', 'layers.8.mlp.down_proj.weight', 'layers.8.mlp.gate_proj.weight', 'layers.8.mlp.up_proj.weight', 'layers.8.post_attention_layernorm.weight', 'layers.8.self_attn.k_proj.weight', 'layers.8.self_attn.o_proj.weight', 'layers.8.self_attn.q_proj.weight', 'layers.8.self_attn.v_proj.weight', 'layers.9.input_layernorm.weight', 'layers.9.mlp.down_proj.weight', 'layers.9.mlp.gate_proj.weight', 'layers.9.mlp.up_proj.weight', 'layers.9.post_attention_layernorm.weight', 'layers.9.self_attn.k_proj.weight', 'layers.9.self_attn.o_proj.weight', 'layers.9.self_attn.q_proj.weight', 'layers.9.self_attn.v_proj.weight', 'norm.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"from transformers import AutoModel, pipeline\n",
"\n",
"repo_id = \"ppak10/defect-classification-llama-baseline-25-epochs\"\n",
"model = AutoModel.from_pretrained(repo_id)\n",
"# tokenizer = AutoTokenizer.from_pretrained(repo_id)\n",
"\n",
"# classification_pipeline = pipeline(\"text-classification\", model=model, tokenizer=tokenizer)\n",
"# result = classification_pipeline(\"Test input text\")\n",
"# print(result)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LlamaModel(\n",
" (embed_tokens): Embedding(32000, 2048)\n",
" (layers): ModuleList(\n",
" (0-31): 32 x LlamaDecoderLayer(\n",
" (self_attn): LlamaSdpaAttention(\n",
" (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (o_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (rotary_emb): LlamaRotaryEmbedding()\n",
" )\n",
" (mlp): LlamaMLP(\n",
" (gate_proj): Linear(in_features=2048, out_features=11008, bias=False)\n",
" (up_proj): Linear(in_features=2048, out_features=11008, bias=False)\n",
" (down_proj): Linear(in_features=11008, out_features=2048, bias=False)\n",
" (act_fn): SiLU()\n",
" )\n",
" (input_layernorm): LlamaRMSNorm((2048,), eps=1e-06)\n",
" (post_attention_layernorm): LlamaRMSNorm((2048,), eps=1e-06)\n",
" )\n",
" )\n",
" (norm): LlamaRMSNorm((2048,), eps=1e-06)\n",
" (rotary_emb): LlamaRotaryEmbedding()\n",
")\n"
]
}
],
"source": [
"print(model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
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|