Text Generation
Transformers
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English
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quasar_long
silx-ai
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quasar
foundation-model
Mixture of Experts
18b
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sn24
decentralized-training
distillation
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safe-nope
drope
conversational
custom_code
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 triton | |
| import triton.language as tl | |
| from fla.ops.utils.op import exp, log | |
| from fla.utils import autotune_cache_kwargs | |
| def logcumsumexp_fwd_kernel( | |
| s, | |
| z, | |
| T, | |
| S: tl.constexpr, | |
| BT: tl.constexpr, | |
| ): | |
| i_bh = tl.program_id(0) | |
| o_i = tl.arange(0, BT) | |
| m_s = tl.where(o_i[:, None] >= o_i[None, :], 1., 0.) | |
| b_mp = tl.full([S], float('-inf'), dtype=tl.float32) | |
| b_zp = tl.zeros([S], dtype=tl.float32) | |
| for i_t in range(tl.cdiv(T, BT)): | |
| p_s = tl.make_block_ptr(s + i_bh * T*S, (T, S), (S, 1), (i_t * BT, 0), (BT, S), (1, 0)) | |
| p_z = tl.make_block_ptr(z + i_bh * T*S, (T, S), (S, 1), (i_t * BT, 0), (BT, S), (1, 0)) | |
| # [BT, S] | |
| b_s = tl.load(p_s, boundary_check=(0, 1)).to(tl.float32) | |
| # [S,] | |
| b_mc = tl.max(b_s, 0) | |
| b_mc = tl.maximum(b_mp, b_mc) | |
| b_zp = b_zp * exp(b_mp - b_mc) | |
| # [BT, S] | |
| b_s = exp(b_s - b_mc) | |
| b_z = tl.dot(m_s, b_s, allow_tf32=False) + b_zp | |
| # [S,] | |
| b_zc = tl.max(b_z, 0) | |
| b_mp = b_mc | |
| b_zp = b_zc | |
| # [BT, BS] | |
| # small eps to prevent underflows | |
| b_z = log(tl.where(b_z != 0, b_z, 1e-20)) + b_mc | |
| tl.store(p_z, b_z.to(p_z.dtype.element_ty), boundary_check=(0, 1)) | |