base_IIXIV

Instructions to use mainline777/base_IIXIV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

base_IIXIV / fla /distributed_compat.py

mainline777

Duplicate from silx-ai/Quasar-Preview

41865df 24 days ago

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1.39 kB

	# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
	"""
	Centralized compatibility module for torch.distributed imports.
	All distributed-related imports should go through here to handle environments
	where distributed tensor APIs are not available.
	"""

	import torch

	# DeviceMesh
	try:
	from torch.distributed import DeviceMesh
	except ImportError:
	try:
	from torch.distributed.device_mesh import DeviceMesh
	except ImportError:
	DeviceMesh = None

	# DTensor
	try:
	from torch.distributed.tensor import DTensor
	except (ImportError, AttributeError):
	DTensor = None

	# Replicate, Shard, distribute_module, Placement
	try:
	from torch.distributed.tensor import Placement, Replicate, Shard, distribute_module
	except (ImportError, AttributeError):
	Placement = Replicate = Shard = distribute_module = None

	# ParallelStyle
	try:
	from torch.distributed.tensor.parallel import ParallelStyle
	except (ImportError, AttributeError):
	ParallelStyle = None

	# Convenience flag
	HAS_DISTRIBUTED = all([
	DeviceMesh is not None,
	DTensor is not None,
	Placement is not None,
	Replicate is not None,
	Shard is not None,
	distribute_module is not None,
	ParallelStyle is not None,
	])

	__all__ = [
	'DeviceMesh',
	'DTensor',
	'Placement',
	'Replicate',
	'Shard',
	'distribute_module',
	'ParallelStyle',
	'HAS_DISTRIBUTED',
	]