Instructions to use kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN")
model = AutoModelForCausalLM.from_pretrained("kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN

SGLang

How to use kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN with Docker Model Runner:
```
docker model run hf.co/kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN
```

kishizaki-sci commited on Dec 8, 2024

Commit

e74261e

verified ·

1 Parent(s): 958af28

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+{
+ "cells": [
+  {
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+   "execution_count": 1,
+   "id": "e0538a90-61d8-4bd0-b2f7-e08e69b32295",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Downloading websockets-14.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (168 kB)\n",
+      "Downloading cloudpickle-3.1.0-py3-none-any.whl (22 kB)\n",
+      "Downloading diskcache-5.6.3-py3-none-any.whl (45 kB)\n",
+      "Downloading lark-1.2.2-py3-none-any.whl (111 kB)\n",
+      "Downloading numba-0.60.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (3.7 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.7/3.7 MB\u001b[0m \u001b[31m132.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pyairports-2.1.1-py3-none-any.whl (371 kB)\n",
+      "Downloading pycountry-24.6.1-py3-none-any.whl (6.3 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m130.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading uvicorn-0.32.1-py3-none-any.whl (63 kB)\n",
+      "Downloading llvmlite-0.43.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (43.9 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.9/43.9 MB\u001b[0m \u001b[31m168.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: sentencepiece, pyairports, py-cpuinfo, nvidia-ml-py, websockets, uvloop, typing-extensions, triton, sympy, python-dotenv, pycountry, protobuf, pillow, partial-json-parser, opencv-python-headless, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, msgspec, msgpack, llvmlite, lark, jiter, interegular, httptools, gguf, einops, diskcache, cloudpickle, click, annotated-types, watchfiles, uvicorn, tiktoken, starlette, pydantic-core, nvidia-cusparse-cu12, numba, pydantic, prometheus-fastapi-instrumentator, nvidia-cusolver-cu12, torch, ray, openai, mistral-common, lm-format-enforcer, fastapi, xformers, torchvision, outlines, compressed-tensors, vllm\n",
+      "  Attempting uninstall: typing-extensions\n",
+      "    Found existing installation: typing_extensions 4.9.0\n",
+      "    Uninstalling typing_extensions-4.9.0:\n",
+      "      Successfully uninstalled typing_extensions-4.9.0\n",
+      "  Attempting uninstall: triton\n",
+      "    Found existing installation: triton 3.0.0\n",
+      "    Uninstalling triton-3.0.0:\n",
+      "      Successfully uninstalled triton-3.0.0\n",
+      "  Attempting uninstall: sympy\n",
+      "    Found existing installation: sympy 1.12\n",
+      "    Uninstalling sympy-1.12:\n",
+      "      Successfully uninstalled sympy-1.12\n",
+      "  Attempting uninstall: pillow\n",
+      "    Found existing installation: pillow 10.2.0\n",
+      "    Uninstalling pillow-10.2.0:\n",
+      "      Successfully uninstalled pillow-10.2.0\n",
+      "  Attempting uninstall: nvidia-nvtx-cu12\n",
+      "    Found existing installation: nvidia-nvtx-cu12 12.4.99\n",
+      "    Uninstalling nvidia-nvtx-cu12-12.4.99:\n",
+      "      Successfully uninstalled nvidia-nvtx-cu12-12.4.99\n",
+      "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
+      "    Found existing installation: nvidia-nvjitlink-cu12 12.4.99\n",
+      "    Uninstalling nvidia-nvjitlink-cu12-12.4.99:\n",
+      "      Successfully uninstalled nvidia-nvjitlink-cu12-12.4.99\n",
+      "  Attempting uninstall: nvidia-nccl-cu12\n",
+      "    Found existing installation: nvidia-nccl-cu12 2.20.5\n",
+      "    Uninstalling nvidia-nccl-cu12-2.20.5:\n",
+      "      Successfully uninstalled nvidia-nccl-cu12-2.20.5\n",
+      "  Attempting uninstall: nvidia-curand-cu12\n",
+      "    Found existing installation: nvidia-curand-cu12 10.3.5.119\n",
+      "    Uninstalling nvidia-curand-cu12-10.3.5.119:\n",
+      "      Successfully uninstalled nvidia-curand-cu12-10.3.5.119\n",
+      "  Attempting uninstall: nvidia-cufft-cu12\n",
+      "    Found existing installation: nvidia-cufft-cu12 11.2.0.44\n",
+      "    Uninstalling nvidia-cufft-cu12-11.2.0.44:\n",
+      "      Successfully uninstalled nvidia-cufft-cu12-11.2.0.44\n",
+      "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+      "    Found existing installation: nvidia-cuda-runtime-cu12 12.4.99\n",
+      "    Uninstalling nvidia-cuda-runtime-cu12-12.4.99:\n",
+      "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.4.99\n",
+      "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
+      "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.4.99\n",
+      "    Uninstalling nvidia-cuda-nvrtc-cu12-12.4.99:\n",
+      "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.4.99\n",
+      "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+      "    Found existing installation: nvidia-cuda-cupti-cu12 12.4.99\n",
+      "    Uninstalling nvidia-cuda-cupti-cu12-12.4.99:\n",
+      "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.4.99\n",
+      "  Attempting uninstall: nvidia-cublas-cu12\n",
+      "    Found existing installation: nvidia-cublas-cu12 12.4.2.65\n",
+      "    Uninstalling nvidia-cublas-cu12-12.4.2.65:\n",
+      "      Successfully uninstalled nvidia-cublas-cu12-12.4.2.65\n",
+      "  Attempting uninstall: nvidia-cusparse-cu12\n",
+      "    Found existing installation: nvidia-cusparse-cu12 12.3.0.142\n",
+      "    Uninstalling nvidia-cusparse-cu12-12.3.0.142:\n",
+      "      Successfully uninstalled nvidia-cusparse-cu12-12.3.0.142\n",
+      "  Attempting uninstall: nvidia-cusolver-cu12\n",
+      "    Found existing installation: nvidia-cusolver-cu12 11.6.0.99\n",
+      "    Uninstalling nvidia-cusolver-cu12-11.6.0.99:\n",
+      "      Successfully uninstalled nvidia-cusolver-cu12-11.6.0.99\n",
+      "  Attempting uninstall: torch\n",
+      "    Found existing installation: torch 2.4.1+cu124\n",
+      "    Uninstalling torch-2.4.1+cu124:\n",
+      "      Successfully uninstalled torch-2.4.1+cu124\n",
+      "  Attempting uninstall: torchvision\n",
+      "    Found existing installation: torchvision 0.19.1+cu124\n",
+      "    Uninstalling torchvision-0.19.1+cu124:\n",
+      "      Successfully uninstalled torchvision-0.19.1+cu124\n",
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "torchaudio 2.4.1+cu124 requires torch==2.4.1, but you have torch 2.5.1 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0mSuccessfully installed annotated-types-0.7.0 click-8.1.7 cloudpickle-3.1.0 compressed-tensors-0.8.0 diskcache-5.6.3 einops-0.8.0 fastapi-0.115.6 gguf-0.10.0 httptools-0.6.4 interegular-0.3.3 jiter-0.8.0 lark-1.2.2 llvmlite-0.43.0 lm-format-enforcer-0.10.9 mistral-common-1.5.1 msgpack-1.1.0 msgspec-0.18.6 numba-0.60.0 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-ml-py-12.560.30 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.4.127 openai-1.57.0 opencv-python-headless-4.10.0.84 outlines-0.0.46 partial-json-parser-0.2.1.1.post4 pillow-10.4.0 prometheus-fastapi-instrumentator-7.0.0 protobuf-5.29.1 py-cpuinfo-9.0.0 pyairports-2.1.1 pycountry-24.6.1 pydantic-2.10.3 pydantic-core-2.27.1 python-dotenv-1.0.1 ray-2.40.0 sentencepiece-0.2.0 starlette-0.41.3 sympy-1.13.1 tiktoken-0.7.0 torch-2.5.1 torchvision-0.20.1 triton-3.1.0 typing-extensions-4.12.2 uvicorn-0.32.1 uvloop-0.21.0 vllm-0.6.4.post1 watchfiles-1.0.0 websockets-14.1 xformers-0.0.28.post3\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install vllm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e772542e-467c-481a-9128-8364987a1bd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sun Dec  8 01:39:25 2024       \n",
+      "+-----------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 565.57.01              Driver Version: 565.57.01      CUDA Version: 12.7     |\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  |   00000000:3C:00.0 Off |                    0 |\n",
+      "| N/A   26C    P0             62W /  310W |       1MiB /  95830MiB |      0%      Default |\n",
+      "|                                         |                        |             Disabled |\n",
+      "+-----------------------------------------+------------------------+----------------------+\n",
+      "|   1  NVIDIA H100 NVL                On  |   00000000:AE:00.0 Off |                    0 |\n",
+      "| N/A   26C    P0             59W /  310W |       1MiB /  95830MiB |      0%      Default |\n",
+      "|                                         |                        |             Disabled |\n",
+      "+-----------------------------------------+------------------------+----------------------+\n",
+      "|   2  NVIDIA H100 NVL                On  |   00000000:BD:00.0 Off |                    0 |\n",
+      "| N/A   24C    P0             60W /  310W |       1MiB /  95830MiB |      0%      Default |\n",
+      "|                                         |                        |             Disabled |\n",
+      "+-----------------------------------------+------------------------+----------------------+\n",
+      "|   3  NVIDIA H100 NVL                On  |   00000000:BE:00.0 Off |                    0 |\n",
+      "| N/A   26C    P0             60W /  310W |       1MiB /  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",
+      "|  No running processes found                                                             |\n",
+      "+-----------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2bf7e331-4686-4c0d-ae0f-72cbb79e2e8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from vllm import LLM, SamplingParams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a51d52bc-d60e-412e-a150-20bc0526d20e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c598964dfdb4818aad022b3d085af8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/1.25k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO 12-08 01:39:53 config.py:350] This model supports multiple tasks: {'generate', 'embedding'}. Defaulting to 'generate'.\n",
+      "INFO 12-08 01:39:53 awq_marlin.py:113] Detected that the model can run with awq_marlin, however you specified quantization=awq explicitly, so forcing awq. Use quantization=awq_marlin for faster inference\n",
+      "WARNING 12-08 01:39:53 config.py:428] awq quantization is not fully optimized yet. The speed can be slower than non-quantized models.\n",
+      "INFO 12-08 01:39:53 config.py:1020] Defaulting to use mp for distributed inference\n",
+      "WARNING 12-08 01:39:53 arg_utils.py:1013] Chunked prefill is enabled by default for models with max_model_len > 32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False.\n",
+      "INFO 12-08 01:39:53 config.py:1136] Chunked prefill is enabled with max_num_batched_tokens=512.\n",
+      "INFO 12-08 01:39:53 llm_engine.py:249] Initializing an LLM engine (v0.6.4.post1) with config: model='kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN', speculative_config=None, tokenizer='kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=4, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=awq, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=False, use_async_output_proc=True, use_cached_outputs=False, chat_template_text_format=string, mm_processor_kwargs=None, pooler_config=None)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f7cc9b8b1b54e97a7638c1fdf2ddcaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/55.4k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef6601b681a24fd9a2208a04352df88a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06e8fb444a9847878c56f24e9856c9e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/325 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bef29f785a341a5bada54897d06284e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/182 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING 12-08 01:39:57 multiproc_gpu_executor.py:56] Reducing Torch parallelism from 72 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.\n",
+      "INFO 12-08 01:39:57 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m INFO 12-08 01:39:57 multiproc_worker_utils.py:215] Worker ready; awaiting tasks\n",
+      "INFO 12-08 01:39:57 selector.py:135] Using Flash Attention backend.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m \u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m INFO 12-08 01:39:57 selector.py:135] Using Flash Attention backend.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m INFO 12-08 01:39:57 multiproc_worker_utils.py:215] Worker ready; awaiting tasks\n",
+      "INFO 12-08 01:39:57 selector.py:135] Using Flash Attention backend.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:39:57 selector.py:135] Using Flash Attention backend.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:39:57 multiproc_worker_utils.py:215] Worker ready; awaiting tasks\n",
+      "INFO 12-08 01:40:00 utils.py:961] Found nccl from library libnccl.so.2\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m INFO 12-08 01:40:00 pynccl.py:69] vLLM is using nccl==2.21.5\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m \u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:40:00 utils.py:961] Found nccl from library libnccl.so.2\n",
+      "INFO 12-08 01:40:00 utils.py:961] Found nccl from library libnccl.so.2\n",
+      "INFO 12-08 01:40:00 utils.py:961] Found nccl from library libnccl.so.2\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m \u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m INFO 12-08 01:40:00 pynccl.py:69] vLLM is using nccl==2.21.5\n",
+      "INFO 12-08 01:40:00 pynccl.py:69] vLLM is using nccl==2.21.5\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:40:00 pynccl.py:69] vLLM is using nccl==2.21.5\n",
+      "WARNING 12-08 01:40:01 custom_all_reduce.py:134] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m \u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m WARNING 12-08 01:40:01 custom_all_reduce.py:134] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.\n",
+      "WARNING 12-08 01:40:01 custom_all_reduce.py:134] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m WARNING 12-08 01:40:01 custom_all_reduce.py:134] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.\n",
+      "INFO 12-08 01:40:01 shm_broadcast.py:236] vLLM message queue communication handle: Handle(connect_ip='127.0.0.1', local_reader_ranks=[1, 2, 3], buffer=<vllm.distributed.device_communicators.shm_broadcast.ShmRingBuffer object at 0x7fe462e95610>, local_subscribe_port=36659, remote_subscribe_port=None)\n",
+      "INFO 12-08 01:40:01 model_runner.py:1072] Starting to load model kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN...\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m \u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m \u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:40:01 model_runner.py:1072] Starting to load model kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN...\n",
+      "INFO 12-08 01:40:01 model_runner.py:1072] Starting to load model kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN...\n",
+      "INFO 12-08 01:40:01 model_runner.py:1072] Starting to load model kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN...\n",
+      "INFO 12-08 01:40:02 weight_utils.py:243] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m INFO 12-08 01:40:02 weight_utils.py:243] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m INFO 12-08 01:40:02 weight_utils.py:243] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:40:02 weight_utils.py:243] Using model weights format ['*.safetensors']\n"
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+      "application/vnd.jupyter.widget-view+json": {
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+      "application/vnd.jupyter.widget-view+json": {
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+      "application/vnd.jupyter.widget-view+json": {
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+    },
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+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a664a2e9587343f99883157151ba080f",
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e2b847d11014313807e518426b573a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading safetensors checkpoint shards:   0% Completed | 0/44 [00:00<?, ?it/s]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO 12-08 01:52:50 model_runner.py:1077] Loading model weights took 50.6331 GB\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m INFO 12-08 01:52:52 model_runner.py:1077] Loading model weights took 50.6331 GB\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m INFO 12-08 01:52:52 model_runner.py:1077] Loading model weights took 50.6331 GB\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:52:52 model_runner.py:1077] Loading model weights took 50.6331 GB\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m \u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m \u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:52:54 worker.py:232] Memory profiling results: total_gpu_memory=93.11GiB initial_memory_usage=51.58GiB peak_torch_memory=51.55GiB memory_usage_post_profile=51.82GiB non_torch_memory=1.15GiB kv_cache_size=37.61GiB gpu_memory_utilization=0.97\n",
+      "INFO 12-08 01:52:54 worker.py:232] Memory profiling results: total_gpu_memory=93.11GiB initial_memory_usage=51.51GiB peak_torch_memory=51.55GiB memory_usage_post_profile=51.68GiB non_torch_memory=1.01GiB kv_cache_size=37.75GiB gpu_memory_utilization=0.97\n",
+      "INFO 12-08 01:52:54 worker.py:232] Memory profiling results: total_gpu_memory=93.11GiB initial_memory_usage=51.58GiB peak_torch_memory=51.55GiB memory_usage_post_profile=51.82GiB non_torch_memory=1.15GiB kv_cache_size=37.61GiB gpu_memory_utilization=0.97\n",
+      "INFO 12-08 01:52:54 worker.py:232] Memory profiling results: total_gpu_memory=93.11GiB initial_memory_usage=51.51GiB peak_torch_memory=51.84GiB memory_usage_post_profile=51.68GiB non_torch_memory=1.02GiB kv_cache_size=37.46GiB gpu_memory_utilization=0.97\n",
+      "INFO 12-08 01:52:54 distributed_gpu_executor.py:57] # GPU blocks: 19483, # CPU blocks: 2080\n",
+      "INFO 12-08 01:52:54 distributed_gpu_executor.py:61] Maximum concurrency for 131072 tokens per request: 2.38x\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m INFO 12-08 01:52:59 model_runner.py:1400] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m INFO 12-08 01:52:59 model_runner.py:1404] If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.\n",
+      "INFO 12-08 01:52:59 model_runner.py:1400] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.\n",
+      "INFO 12-08 01:52:59 model_runner.py:1404] If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:53:00 model_runner.py:1400] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:53:00 model_runner.py:1404] If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m INFO 12-08 01:53:00 model_runner.py:1400] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m INFO 12-08 01:53:00 model_runner.py:1404] If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=731)\u001b[0;0m INFO 12-08 01:53:44 model_runner.py:1518] Graph capturing finished in 45 secs, took 2.71 GiB\n",
+      "INFO 12-08 01:53:45 model_runner.py:1518] Graph capturing finished in 46 secs, took 2.71 GiB\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=729)\u001b[0;0m INFO 12-08 01:53:45 model_runner.py:1518] Graph capturing finished in 45 secs, took 2.71 GiB\n",
+      "\u001b[1;36m(VllmWorkerProcess pid=730)\u001b[0;0m INFO 12-08 01:53:45 model_runner.py:1518] Graph capturing finished in 46 secs, took 2.71 GiB\n"
+     ]
+    }
+   ],
+   "source": [
+    "llm = LLM(\n",
+    "    model=\"kishizaki-sci/Llama-3.1-405B-Instruct-AWQ-4bit-JP-EN\",\n",
+    "    tensor_parallel_size=4,\n",
+    "    gpu_memory_utilization=0.97,\n",
+    "    quantization=\"awq\"\n",
+    ")\n",
+    "tokenizer = llm.get_tokenizer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cc81f387-a06f-4564-a50e-37e367a79422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DEFAULT_SYSTEM_PROMPT = \"あなたは日本人のアシスタントです。\"\n",
+    "text = \"plotly.graph_objectsを使って散布図を作るサンプルコードを書いてください．\"\n",
+    "\n",
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": DEFAULT_SYSTEM_PROMPT},\n",
+    "    {\"role\": \"user\", \"content\": text},\n",
+    "]\n",
+    "\n",
+    "prompt = tokenizer.apply_chat_template(\n",
+    "    messages,\n",
+    "    tokenize=False,\n",
+    "    add_generation_prompt=True\n",
+    ")\n",
+    "\n",
+    "sampling_params = SamplingParams(\n",
+    "    temperature=0.6,\n",
+    "    top_p=0.9,\n",
+    "    max_tokens=1000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c74b2d83-12ff-4324-bc84-51e88b3e12b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processed prompts: 100%|██████████| 1/1 [00:20<00:00, 20.38s/it, est. speed input: 3.29 toks/s, output: 13.59 toks/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plotly.graph_objectsを使って散布図を作るサンプルコードを以下に示します。\n",
+      "\n",
+      "```python\n",
+      "import plotly.graph_objects as go\n",
+      "import numpy as np\n",
+      "\n",
+      "# サンプルデータを生成\n",
+      "np.random.seed(0)\n",
+      "x = np.random.randn(100)\n",
+      "y = np.random.randn(100)\n",
+      "\n",
+      "# 散布図を作成\n",
+      "fig = go.Figure(data=[go.Scatter(\n",
+      "    x=x,\n",
+      "    y=y,\n",
+      "    mode='markers',\n",
+      "    marker=dict(\n",
+      "        size=10,\n",
+      "        color='blue',\n",
+      "        opacity=0.7\n",
+      "    )\n",
+      ")])\n",
+      "\n",
+      "# グラフのタイトルと軸ラベルを設定\n",
+      "fig.update_layout(\n",
+      "    title='散布図のサンプル',\n",
+      "    xaxis_title='X軸',\n",
+      "    yaxis_title='Y軸'\n",
+      ")\n",
+      "\n",
+      "# グラフを表示\n",
+      "fig.show()\n",
+      "```\n",
+      "\n",
+      "このコードでは、numpyを使用してランダムなサンプルデータを生成し、plotly.graph_objectsのScatterオブジェクトを使用して散布図を作成しています。散布図のマーカーのサイズ、色、透明度を設定し、���ラフのタイトルと軸ラベルを設定しています。最後に、`fig.show()`を使用してグラフを表示しています。\n",
+      "CPU times: user 19.8 s, sys: 645 ms, total: 20.5 s\n",
+      "Wall time: 20.4 s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "outputs = llm.generate(prompt, sampling_params)\n",
+    "print(outputs[0].outputs[0].text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fb4a3d0-10ba-4eda-824d-e774322ddf07",
+   "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": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}