Text Generation
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Instructions to use mainline777/base_IIXIV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mainline777/base_IIXIV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mainline777/base_IIXIV", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mainline777/base_IIXIV", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mainline777/base_IIXIV with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mainline777/base_IIXIV" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mainline777/base_IIXIV", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mainline777/base_IIXIV
- SGLang
How to use mainline777/base_IIXIV 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 "mainline777/base_IIXIV" \ --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": "mainline777/base_IIXIV", "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 "mainline777/base_IIXIV" \ --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": "mainline777/base_IIXIV", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mainline777/base_IIXIV with Docker Model Runner:
docker model run hf.co/mainline777/base_IIXIV
| # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang | |
| import torch.nn as nn | |
| from torch.distributed import DeviceMesh | |
| from torch.distributed.tensor import distribute_module | |
| from torch.distributed.tensor.parallel import ParallelStyle | |
| from torch.distributed.tensor.placement_types import Placement | |
| try: | |
| from torch.distributed.tensor import DTensor | |
| except (ImportError, AttributeError): | |
| DTensor = None | |
| class PrepareModuleWeight(ParallelStyle): | |
| def __init__(self, *, layouts: Placement | None = None): | |
| super().__init__() | |
| self.layouts = layouts | |
| def _replicate_module_fn( | |
| self, | |
| name: str, | |
| module: nn.Module, | |
| device_mesh: DeviceMesh, | |
| ): | |
| for p_name, param in module.named_parameters(): | |
| replicated_param = nn.Parameter( | |
| DTensor.from_local(param, device_mesh, [self.layouts], run_check=False), | |
| ) | |
| module.register_parameter(p_name, replicated_param) | |
| def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module: | |
| return distribute_module( | |
| module, | |
| device_mesh, | |
| partition_fn=self._replicate_module_fn, | |
| input_fn=None, | |
| output_fn=None, | |
| ) | |