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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
File size: 1,243 Bytes
41865df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # 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,
)
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