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{
"cells": [
{
"cell_type": "markdown",
"id": "3008bf1f-df10-484b-9662-96cda681b93b",
"metadata": {},
"source": [
"```bash\n",
"git clone https://github.com/kIshizaki-sci/AutoAWQ.git\n",
"pip install -U transformers\n",
"pip install -e ./AutoAWQ\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a388d190-a611-46b0-aa8c-dcf5c97b0c1b",
"metadata": {},
"outputs": [],
"source": [
"from awq import AutoAWQForCausalLM\n",
"import torch\n",
"import transformers\n",
"from transformers import AutoProcessor"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "de7f888d-fe64-47f1-a106-75a7b911354f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch version : 2.4.1+cu124\n",
"transformers version : 4.51.3\n"
]
}
],
"source": [
"print('torch version : ', torch.__version__)\n",
"print('transformers version : ', transformers.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "61623ebd-d8a4-45b6-aff3-3c16a78ecfa5",
"metadata": {},
"outputs": [],
"source": [
"quant_path = 'kishizaki-sci/Llama-4-Scout-17B-16E-Instruct-AWQ'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c13b72e4-f6cd-4642-a110-040844127541",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/workspace/AutoAWQ/awq/models/llama4.py:312: UserWarning: Multimodal input has not been implemented in Llama4AWQForConditionalGeneration yet.\n",
" warnings.warn(\"Multimodal input has not been implemented in Llama4AWQForConditionalGeneration yet.\", UserWarning)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ac05674ff544f0382ab12b8ffcb2b0f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Fetching 25 files: 0%| | 0/25 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fab65ad4f784481499296cfd8ea03d3a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Replacing MoE Block...: 0%| | 0/48 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Replacing layers...: 100%|██████████| 48/48 [00:08<00:00, 5.66it/s]\n",
"/workspace/AutoAWQ/awq/models/base.py:539: UserWarning: Skipping fusing modules because AWQ extension is not installed.No module named 'awq_ext'\n",
" warnings.warn(\"Skipping fusing modules because AWQ extension is not installed.\" + msg)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1h 38min 56s, sys: 24min 23s, total: 2h 3min 19s\n",
"Wall time: 22min 8s\n"
]
}
],
"source": [
"%%time\n",
"model = AutoAWQForCausalLM.from_quantized(quant_path, torch_dtype=torch.float16, use_cache=True, device_map='auto')\n",
"processor = AutoProcessor.from_pretrained(quant_path)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "665ae24c-cb73-489d-bb03-35d760460070",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sat May 3 00:13:08 2025 \n",
"+-----------------------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 570.86.15 Driver Version: 570.86.15 CUDA Version: 12.8 |\n",
"|-----------------------------------------+------------------------+----------------------+\n",
"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|=========================================+========================+======================|\n",
"| 0 NVIDIA H100 NVL On | 00000000:C7:00.0 Off | 0 |\n",
"| N/A 36C P0 92W / 310W | 60518MiB / 95830MiB | 0% Default |\n",
"| | | Disabled |\n",
"+-----------------------------------------+------------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=========================================================================================|\n",
"+-----------------------------------------------------------------------------------------+\n"
]
}
],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "39c8154a-08c4-485e-b136-955d1b4fbec9",
"metadata": {},
"outputs": [],
"source": [
"messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"text\", \"text\": \"What does means the torsion free in the general relativit?\"},\n",
" ]\n",
" },\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "44c4ae97-96cf-47f7-a8e4-16d2c34bdc1b",
"metadata": {},
"outputs": [],
"source": [
"inputs = processor.apply_chat_template(\n",
" messages,\n",
" add_generation_prompt=True,\n",
" tokenize=True,\n",
" return_dict=True,\n",
" return_tensors=\"pt\",\n",
").to(model.model.device)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c6dddd7b-5382-45e8-8dd1-6ca719200f64",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1min 43s, sys: 867 ms, total: 1min 44s\n",
"Wall time: 1min 44s\n"
]
}
],
"source": [
"%%time\n",
"outputs = model.generate(\n",
" **inputs,\n",
" max_new_tokens=2048,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ab1d4fe0-e315-40ee-b3e0-10db1e6b2023",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A question from the realm of differential geometry and general relativity!\n",
"\n",
"In general relativity, \"torsion-free\" refers to a property of a connection on a manifold, specifically in the context of Riemannian geometry.\n",
"\n",
"**Torsion** is a measure of how much a connection \"twists\" or \"turns\" a vector as it is parallel-transported around a closed loop. In other words, it's a measure of how much the connection deviates from being \"flat\" or \"Euclidean\".\n",
"\n",
"A **torsion-free connection**, also known as a **symmetric connection**, is a connection that has zero torsion. This means that when you parallel-transport a vector around a closed loop, it returns to its original orientation, without any twisting or turning.\n",
"\n",
"In mathematical terms, a torsion-free connection satisfies the following condition:\n",
"\n",
"$$\\Gamma^i_{jk} = \\Gamma^i_{kj}$$\n",
"\n",
"where $\\Gamma^i_{jk}$ are the Christoffel symbols of the second kind, which define the connection.\n",
"\n",
"In general relativity, the Levi-Civita connection is a fundamental concept, and it is assumed to be torsion-free. This connection is used to define the covariant derivative of tensors, which is essential for describing the curvature of spacetime.\n",
"\n",
"The assumption of a torsion-free connection has important implications:\n",
"\n",
"1. **Geodesic equation**: The geodesic equation, which describes the shortest path in curved spacetime, is derived from the Levi-Civita connection. A torsion-free connection ensures that geodesics are symmetric, meaning that they have no \"twist\" or \"turn\".\n",
"2. **Riemannian geometry**: The Levi-Civita connection is a fundamental ingredient in Riemannian geometry, which is the mathematical framework for describing curved spacetime in general relativity.\n",
"3. **Einstein's field equations**: The Einstein field equations, which relate the curvature of spacetime to the distribution of mass and energy, rely on the Levi-Civita connection.\n",
"\n",
"In summary, a torsion-free connection in general relativity means that the connection used to describe the curvature of spacetime has zero torsion, which is a fundamental assumption in Riemannian geometry and leads to the Levi-Civita connection. This assumption is crucial for the mathematical formulation of general relativity, including the geodesic equation and Einstein's field equations.<|eot|>\n"
]
}
],
"source": [
"response = processor.batch_decode(outputs[:, inputs[\"input_ids\"].shape[-1]:])[0]\n",
"print(response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f3f1be7-6960-474e-b19d-afd2efa56174",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|