Instructions to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints
- SGLang
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints 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 "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with Docker Model Runner:
docker model run hf.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints
| # Copyright 2024 OpenNLPLab | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # coding=utf-8 | |
| import logging | |
| import os | |
| import sys | |
| import torch | |
| from torch import nn | |
| logging.basicConfig( | |
| format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", | |
| datefmt="%Y-%m-%d %H:%M:%S", | |
| level=os.environ.get("LOGLEVEL", "INFO").upper(), | |
| stream=sys.stdout, | |
| ) | |
| logger = logging.getLogger("srmsnorm") | |
| class SimpleRMSNorm(nn.Module): | |
| def __init__(self, dim: int, eps: float = 1e-6): | |
| super().__init__() | |
| self.eps = eps | |
| def _norm(self, x): | |
| return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) | |
| def forward(self, x): | |
| output = self._norm(x.float()).type_as(x) | |
| return output | |