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 /ops /__init__.py

mainline777

Duplicate from silx-ai/Quasar-Preview

41865df 28 days ago

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	from .abc import chunk_abc
	from .attn import parallel_attn
	from .based import fused_chunk_based, parallel_based
	from .comba import chunk_comba, fused_recurrent_comba
	from .delta_rule import chunk_delta_rule, fused_chunk_delta_rule, fused_recurrent_delta_rule
	from .forgetting_attn import parallel_forgetting_attn
	from .gated_delta_rule import chunk_gated_delta_rule, chunk_gdn, fused_recurrent_gated_delta_rule, fused_recurrent_gdn
	from .generalized_delta_rule import (
	chunk_dplr_delta_rule,
	chunk_iplr_delta_rule,
	fused_recurrent_dplr_delta_rule,
	fused_recurrent_iplr_delta_rule,
	)
	from .gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla
	from .gsa import chunk_gsa, fused_recurrent_gsa
	from .hgrn import fused_recurrent_hgrn
	from .kda import chunk_kda, fused_recurrent_kda
	from .lightning_attn import chunk_lightning_attn, fused_recurrent_lightning_attn
	from .linear_attn import chunk_linear_attn, fused_chunk_linear_attn, fused_recurrent_linear_attn
	from .log_linear_attn import chunk_log_linear_attn
	from .mesa_net import chunk_mesa_net
	from .nsa import parallel_nsa
	from .path_attn import parallel_path_attn
	from .retention import chunk_retention, fused_chunk_retention, fused_recurrent_retention, parallel_retention
	from .rwkv6 import chunk_rwkv6, fused_recurrent_rwkv6
	from .rwkv7 import chunk_rwkv7, fused_recurrent_rwkv7
	from .simple_gla import chunk_simple_gla, fused_chunk_simple_gla, fused_recurrent_simple_gla, parallel_simple_gla

	__all__ = [
	'chunk_abc',
	'chunk_comba',
	'chunk_delta_rule',
	'chunk_dplr_delta_rule',
	'chunk_gated_delta_rule',
	'chunk_gdn',
	'chunk_gla',
	'chunk_gsa',
	'chunk_iplr_delta_rule',
	'chunk_kda',
	'chunk_lightning_attn',
	'chunk_linear_attn',
	'chunk_log_linear_attn',
	'chunk_mesa_net',
	'chunk_retention',
	'chunk_rwkv6',
	'chunk_rwkv7',
	'chunk_simple_gla',
	'fused_chunk_based',
	'fused_chunk_delta_rule',
	'fused_chunk_gla',
	'fused_chunk_linear_attn',
	'fused_chunk_retention',
	'fused_chunk_simple_gla',
	'fused_recurrent_comba',
	'fused_recurrent_delta_rule',
	'fused_recurrent_dplr_delta_rule',
	'fused_recurrent_gated_delta_rule',
	'fused_recurrent_gdn',
	'fused_recurrent_gla',
	'fused_recurrent_gsa',
	'fused_recurrent_hgrn',
	'fused_recurrent_iplr_delta_rule',
	'fused_recurrent_kda',
	'fused_recurrent_lightning_attn',
	'fused_recurrent_linear_attn',
	'fused_recurrent_retention',
	'fused_recurrent_rwkv6',
	'fused_recurrent_rwkv7',
	'fused_recurrent_simple_gla',
	'parallel_attn',
	'parallel_based',
	'parallel_forgetting_attn',
	'parallel_nsa',
	'parallel_path_attn',
	'parallel_retention',
	'parallel_simple_gla',
	]