Text Generation
Transformers
Safetensors
English
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quasar_long
silx-ai
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Mixture of Experts
18b
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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 os | |
| import triton | |
| import triton.language as tl | |
| import triton.language.extra.libdevice as tldevice | |
| from fla.utils import IS_GATHER_SUPPORTED | |
| if os.environ.get('FLA_USE_FAST_OPS', '0') == '1': | |
| def exp(x): return tldevice.fast_expf(x.to(tl.float32)) | |
| def exp2(x): return tldevice.exp2(x.to(tl.float32)) | |
| def log(x): return tldevice.fast_logf(x.to(tl.float32)) | |
| def log2(x): return tldevice.fast_log2f(x.to(tl.float32)) | |
| else: | |
| def exp(x): return tl.exp(x.to(tl.float32)) | |
| def exp2(x): return tl.math.exp2(x.to(tl.float32)) | |
| def log(x): return tl.log(x.to(tl.float32)) | |
| def log2(x): return tl.log2(x.to(tl.float32)) | |
| if not IS_GATHER_SUPPORTED: | |
| def gather(src, index, axis, _builder=None): | |
| """ | |
| Gather operation that works when tl.gather is not supported. | |
| This is a fallback implementation that returns None. | |
| Just to make triton compiler happy. | |
| """ | |
| return None | |
| else: | |
| gather = tl.gather | |
| if hasattr(triton.language, '_experimental_make_tensor_descriptor'): | |
| # For Triton 3.3.x | |
| make_tensor_descriptor = triton.language._experimental_make_tensor_descriptor | |
| elif hasattr(triton.language, 'make_tensor_descriptor'): | |
| # For Triton 3.4.x and later | |
| make_tensor_descriptor = triton.language.make_tensor_descriptor | |
| else: | |
| """ | |
| Fallback implementation when TMA is not supported. | |
| Returns None to indicate TMA descriptors are unavailable. | |
| Just make triton compiler happy. | |
| """ | |
| def make_tensor_descriptor( | |
| base, | |
| shape, | |
| strides, | |
| block_shape, | |
| _builder=None, | |
| ): | |
| return None | |