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Co-authored-by: Chayenne <zhaochenyang@g.ucla.edu>
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Chayenne
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# Backend: SGLang Runtime (SRT)
The SGLang Runtime (SRT) is an efficient serving engine.
## Quick Start
Launch a server
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000
```
Send a request
```
curl http://localhost:30000/generate \
-H "Content-Type: application/json" \
-d '{
"text": "Once upon a time,",
"sampling_params": {
"max_new_tokens": 16,
"temperature": 0
}
}'
```
Learn more about the argument specification, streaming, and multi-modal support [here](https://sgl-project.github.io/sampling_params.html).
## OpenAI Compatible API
In addition, the server supports OpenAI-compatible APIs.
```python
import openai
client = openai.Client(
base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
# Text completion
response = client.completions.create(
model="default",
prompt="The capital of France is",
temperature=0,
max_tokens=32,
)
print(response)
# Chat completion
response = client.chat.completions.create(
model="default",
messages=[
{"role": "system", "content": "You are a helpful AI assistant"},
{"role": "user", "content": "List 3 countries and their capitals."},
],
temperature=0,
max_tokens=64,
)
print(response)
# Text embedding
response = client.embeddings.create(
model="default",
input="How are you today",
)
print(response)
```
It supports streaming, vision, and almost all features of the Chat/Completions/Models/Batch endpoints specified by the [OpenAI API Reference](https://platform.openai.com/docs/api-reference/).
## Additional Server Arguments
- To enable multi-GPU tensor parallelism, add `--tp 2`. If it reports the error "peer access is not supported between these two devices", add `--enable-p2p-check` to the server launch command.
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 2
```
- To enable multi-GPU data parallelism, add `--dp 2`. Data parallelism is better for throughput if there is enough memory. It can also be used together with tensor parallelism. The following command uses 4 GPUs in total.
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --dp 2 --tp 2
```
- If you see out-of-memory errors during serving, try to reduce the memory usage of the KV cache pool by setting a smaller value of `--mem-fraction-static`. The default value is `0.9`.
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --mem-fraction-static 0.7
```
- See [hyperparameter tuning](https://sgl-project.github.io/hyperparameter_tuning.html) on tuning hyperparameters for better performance.
- If you see out-of-memory errors during prefill for long prompts, try to set a smaller chunked prefill size.
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
```
- To enable the experimental overlapped scheduler, add `--enable-overlap-scheduler`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currenly.
- To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. This does not work for FP8 currenly.
- To enable torchao quantization, add `--torchao-config int4wo-128`. It supports various quantization strategies.
- To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.
- To enable fp8 kv cache quantization, add `--kv-cache-dtype fp8_e5m2`.
- If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](https://sgl-project.github.io/custom_chat_template.html).
- To run tensor parallelism on multiple nodes, add `--nnodes 2`. If you have two nodes with two GPUs on each node and want to run TP=4, let `sgl-dev-0` be the hostname of the first node and `50000` be an available port, you can use the following commands. If you meet deadlock, please try to add `--disable-cuda-graph`
```
# Node 0
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 4 --nccl-init sgl-dev-0:50000 --nnodes 2 --node-rank 0
# Node 1
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 4 --nccl-init sgl-dev-0:50000 --nnodes 2 --node-rank 1
```
## Engine Without HTTP Server
We also provide an inference engine **without a HTTP server**. For example,
```python
import sglang as sgl
def main():
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = {"temperature": 0.8, "top_p": 0.95}
llm = sgl.Engine(model_path="meta-llama/Meta-Llama-3.1-8B-Instruct")
outputs = llm.generate(prompts, sampling_params)
for prompt, output in zip(prompts, outputs):
print("===============================")
print(f"Prompt: {prompt}\nGenerated text: {output['text']}")
if __name__ == "__main__":
main()
```
This can be used for offline batch inference and building custom servers.
You can view the full example [here](https://github.com/sgl-project/sglang/tree/main/examples/runtime/engine).
## Supported Models
**Generative Models**
- Llama / Llama 2 / Llama 3 / Llama 3.1
- Mistral / Mixtral / Mistral NeMo
- Gemma / Gemma 2
- Qwen / Qwen 2 / Qwen 2 MoE / Qwen 2 VL
- DeepSeek / DeepSeek 2
- OLMoE
- [LLaVA-OneVision](https://llava-vl.github.io/blog/2024-08-05-llava-onevision/)
- `python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-7b-ov --port=30000 --chat-template=chatml-llava`
- `python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-72b-ov --port=30000 --tp-size=8 --chat-template=chatml-llava`
- Query the server with the [OpenAI Vision API](https://platform.openai.com/docs/guides/vision). See examples at [test/srt/test_vision_openai_server.py](https://github.com/sgl-project/sglang/blob/main/test/srt/test_vision_openai_server.py)
- LLaVA 1.5 / 1.6 / NeXT
- `python -m sglang.launch_server --model-path lmms-lab/llama3-llava-next-8b --port=30000 --tp-size=1 --chat-template=llava_llama_3`
- `python -m sglang.launch_server --model-path lmms-lab/llava-next-72b --port=30000 --tp-size=8 --chat-template=chatml-llava`
- Query the server with the [OpenAI Vision API](https://platform.openai.com/docs/guides/vision). See examples at [test/srt/test_vision_openai_server.py](https://github.com/sgl-project/sglang/blob/main/test/srt/test_vision_openai_server.py)
- Yi-VL
- StableLM
- Command-R
- DBRX
- Grok
- ChatGLM
- InternLM 2
- Exaone 3
- BaiChuan2
- MiniCPM / MiniCPM 3
- XVERSE / XVERSE MoE
- SmolLM
- GLM-4
**Embedding Models**
- e5-mistral
- gte-Qwen2
- `python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct --is-embedding`
Instructions for supporting a new model are [here](https://sgl-project.github.io/model_support.html).
### Use Models From ModelScope
<details>
<summary>More</summary>
To use a model from [ModelScope](https://www.modelscope.cn), set the environment variable SGLANG_USE_MODELSCOPE.
```
export SGLANG_USE_MODELSCOPE=true
```
Launch [Qwen2-7B-Instruct](https://www.modelscope.cn/models/qwen/qwen2-7b-instruct) Server
```
SGLANG_USE_MODELSCOPE=true python -m sglang.launch_server --model-path qwen/Qwen2-7B-Instruct --port 30000
```
Or start it by docker.
```bash
docker run --gpus all \
-p 30000:30000 \
-v ~/.cache/modelscope:/root/.cache/modelscope \
--env "SGLANG_USE_MODELSCOPE=true" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server --model-path Qwen/Qwen2.5-7B-Instruct --host 0.0.0.0 --port 30000
```
</details>
### Run Llama 3.1 405B
<details>
<summary>More</summary>
```bash
# Run 405B (fp8) on a single node
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 --tp 8
# Run 405B (fp16) on two nodes
## on the first node, replace the `172.16.4.52:20000` with your own first node ip address and port
GLOO_SOCKET_IFNAME=eth0 python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --nccl-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 0 --disable-cuda-graph
## on the first node, replace the `172.16.4.52:20000` with your own first node ip address and port
GLOO_SOCKET_IFNAME=eth0 python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --nccl-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 1 --disable-cuda-graph
```
</details>
## Benchmark Performance
- Benchmark a single static batch by running the following command without launching a server. The arguments are the same as for `launch_server.py`.
Note that this is not a dynamic batching server, so it may run out of memory for a batch size that a real server can handle.
A real server truncates the prefill into several batches, while this unit test does not. For accurate large batch testing, please use `sglang.bench_serving` instead.
```
python -m sglang.bench_latency --model-path meta-llama/Meta-Llama-3-8B-Instruct --batch 32 --input-len 256 --output-len 32
```
- Benchmark online serving. Launch a server first and run the following command.
```
python3 -m sglang.bench_serving --backend sglang --num-prompt 10
```

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Embedding Model\n",
"\n",
"SGLang supports embedding models in the same way as completion models. Here are some example models:\n",
"\n",
"- [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)\n",
"- [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Launch A Server\n",
"\n",
"The following code is equivalent to running this in the shell:\n",
"```bash\n",
"python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n",
" --port 30010 --host 0.0.0.0 --is-embedding\n",
"```\n",
"\n",
"Remember to add `--is-embedding` to the command."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-01T02:47:32.337369Z",
"iopub.status.busy": "2024-11-01T02:47:32.337032Z",
"iopub.status.idle": "2024-11-01T02:47:59.540926Z",
"shell.execute_reply": "2024-11-01T02:47:59.539861Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:37] server_args=ServerArgs(model_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='Alibaba-NLP/gte-Qwen2-7B-instruct', chat_template=None, is_embedding=True, host='0.0.0.0', port=30010, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=314021918, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
" warnings.warn(\n",
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:43 TP0] Init torch distributed begin.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:44 TP0] Load weight begin. avail mem=47.27 GB\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:44 TP0] lm_eval is not installed, GPTQ may not be usable\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO 10-31 19:47:45 weight_utils.py:243] Using model weights format ['*.safetensors']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Loading safetensors checkpoint shards: 0% Completed | 0/7 [00:00<?, ?it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Loading safetensors checkpoint shards: 14% Completed | 1/7 [00:00<00:03, 1.96it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Loading safetensors checkpoint shards: 29% Completed | 2/7 [00:01<00:03, 1.39it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Loading safetensors checkpoint shards: 43% Completed | 3/7 [00:02<00:03, 1.13it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Loading safetensors checkpoint shards: 57% Completed | 4/7 [00:03<00:02, 1.00it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Loading safetensors checkpoint shards: 71% Completed | 5/7 [00:04<00:02, 1.05s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Loading safetensors checkpoint shards: 86% Completed | 6/7 [00:05<00:01, 1.09s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Loading safetensors checkpoint shards: 100% Completed | 7/7 [00:07<00:00, 1.11s/it]\n",
"\r",
"Loading safetensors checkpoint shards: 100% Completed | 7/7 [00:07<00:00, 1.01s/it]\n",
"\n",
"[2024-10-31 19:47:53 TP0] Load weight end. type=Qwen2ForCausalLM, dtype=torch.float16, avail mem=32.91 GB\n",
"[2024-10-31 19:47:53 TP0] Memory pool end. avail mem=4.56 GB\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:53 TP0] max_total_num_tokens=509971, max_prefill_tokens=16384, max_running_requests=2049, context_len=131072\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:54] INFO: Started server process [1552642]\n",
"[2024-10-31 19:47:54] INFO: Waiting for application startup.\n",
"[2024-10-31 19:47:54] INFO: Application startup complete.\n",
"[2024-10-31 19:47:54] INFO: Uvicorn running on http://0.0.0.0:30010 (Press CTRL+C to quit)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:54] INFO: 127.0.0.1:47776 - \"GET /v1/models HTTP/1.1\" 200 OK\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:55] INFO: 127.0.0.1:50344 - \"GET /get_model_info HTTP/1.1\" 200 OK\n",
"[2024-10-31 19:47:55 TP0] Prefill batch. #new-seq: 1, #new-token: 6, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:55] INFO: 127.0.0.1:50352 - \"POST /encode HTTP/1.1\" 200 OK\n",
"[2024-10-31 19:47:55] The server is fired up and ready to roll!\n"
]
},
{
"data": {
"text/html": [
"<strong style='color: #00008B;'><br><br> NOTE: Typically, the server runs in a separate terminal.<br> In this notebook, we run the server and notebook code together, so their outputs are combined.<br> To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.<br> </strong>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sglang.utils import (\n",
" execute_shell_command,\n",
" wait_for_server,\n",
" terminate_process,\n",
" print_highlight,\n",
")\n",
"\n",
"embedding_process = execute_shell_command(\n",
" \"\"\"\n",
"python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n",
" --port 30010 --host 0.0.0.0 --is-embedding\n",
"\"\"\"\n",
")\n",
"\n",
"wait_for_server(\"http://localhost:30010\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use Curl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-01T02:47:59.543958Z",
"iopub.status.busy": "2024-11-01T02:47:59.543670Z",
"iopub.status.idle": "2024-11-01T02:47:59.591699Z",
"shell.execute_reply": "2024-11-01T02:47:59.590809Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:59 TP0] Prefill batch. #new-seq: 1, #new-token: 4, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n",
"[2024-10-31 19:47:59] INFO: 127.0.0.1:50358 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n"
]
},
{
"data": {
"text/html": [
"<strong style='color: #00008B;'>Text embedding (first 10): [0.0083160400390625, 0.0006804466247558594, -0.00809478759765625, -0.0006995201110839844, 0.0143890380859375, -0.0090179443359375, 0.01238250732421875, 0.00209808349609375, 0.0062103271484375, -0.003047943115234375]</strong>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import subprocess, json\n",
"\n",
"text = \"Once upon a time\"\n",
"\n",
"curl_text = f\"\"\"curl -s http://localhost:30010/v1/embeddings \\\n",
" -H \"Content-Type: application/json\" \\\n",
" -H \"Authorization: Bearer None\" \\\n",
" -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"input\": \"{text}\"}}'\"\"\"\n",
"\n",
"text_embedding = json.loads(subprocess.check_output(curl_text, shell=True))[\"data\"][0][\n",
" \"embedding\"\n",
"]\n",
"\n",
"print_highlight(f\"Text embedding (first 10): {text_embedding[:10]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using OpenAI Compatible API"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-01T02:47:59.594229Z",
"iopub.status.busy": "2024-11-01T02:47:59.594049Z",
"iopub.status.idle": "2024-11-01T02:48:00.006233Z",
"shell.execute_reply": "2024-11-01T02:48:00.005255Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:47:59 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 3, cache hit rate: 21.43%, token usage: 0.00, #running-req: 0, #queue-req: 0\n",
"[2024-10-31 19:47:59] INFO: 127.0.0.1:50362 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n"
]
},
{
"data": {
"text/html": [
"<strong style='color: #00008B;'>Text embedding (first 10): [0.00829315185546875, 0.0007004737854003906, -0.00809478759765625, -0.0006799697875976562, 0.01438140869140625, -0.00897979736328125, 0.0123748779296875, 0.0020923614501953125, 0.006195068359375, -0.0030498504638671875]</strong>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import openai\n",
"\n",
"client = openai.Client(base_url=\"http://127.0.0.1:30010/v1\", api_key=\"None\")\n",
"\n",
"# Text embedding example\n",
"response = client.embeddings.create(\n",
" model=\"Alibaba-NLP/gte-Qwen2-7B-instruct\",\n",
" input=text,\n",
")\n",
"\n",
"embedding = response.data[0].embedding[:10]\n",
"print_highlight(f\"Text embedding (first 10): {embedding}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using Input IDs\n",
"\n",
"SGLang also supports `input_ids` as input to get the embedding."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-01T02:48:00.008858Z",
"iopub.status.busy": "2024-11-01T02:48:00.008689Z",
"iopub.status.idle": "2024-11-01T02:48:01.872542Z",
"shell.execute_reply": "2024-11-01T02:48:01.871573Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-10-31 19:48:01 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 3, cache hit rate: 33.33%, token usage: 0.00, #running-req: 0, #queue-req: 0\n",
"[2024-10-31 19:48:01] INFO: 127.0.0.1:50366 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n"
]
},
{
"data": {
"text/html": [
"<strong style='color: #00008B;'>Input IDs embedding (first 10): [0.00829315185546875, 0.0007004737854003906, -0.00809478759765625, -0.0006799697875976562, 0.01438140869140625, -0.00897979736328125, 0.0123748779296875, 0.0020923614501953125, 0.006195068359375, -0.0030498504638671875]</strong>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import json\n",
"import os\n",
"from transformers import AutoTokenizer\n",
"\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"Alibaba-NLP/gte-Qwen2-7B-instruct\")\n",
"input_ids = tokenizer.encode(text)\n",
"\n",
"curl_ids = f\"\"\"curl -s http://localhost:30010/v1/embeddings \\\n",
" -H \"Content-Type: application/json\" \\\n",
" -H \"Authorization: Bearer None\" \\\n",
" -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"input\": {json.dumps(input_ids)}}}'\"\"\"\n",
"\n",
"input_ids_embedding = json.loads(subprocess.check_output(curl_ids, shell=True))[\"data\"][\n",
" 0\n",
"][\"embedding\"]\n",
"\n",
"print_highlight(f\"Input IDs embedding (first 10): {input_ids_embedding[:10]}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-01T02:48:01.875204Z",
"iopub.status.busy": "2024-11-01T02:48:01.874915Z",
"iopub.status.idle": "2024-11-01T02:48:02.193734Z",
"shell.execute_reply": "2024-11-01T02:48:02.192158Z"
}
},
"outputs": [],
"source": [
"terminate_process(embedding_process)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "AlphaMeemory",
"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": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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