Instructions to use Lanni-ni/alibi_4_6_384_babylm_100m_seed43 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lanni-ni/alibi_4_6_384_babylm_100m_seed43 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Lanni-ni/alibi_4_6_384_babylm_100m_seed43", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Lanni-ni/alibi_4_6_384_babylm_100m_seed43", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Lanni-ni/alibi_4_6_384_babylm_100m_seed43 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Lanni-ni/alibi_4_6_384_babylm_100m_seed43" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Lanni-ni/alibi_4_6_384_babylm_100m_seed43", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Lanni-ni/alibi_4_6_384_babylm_100m_seed43
- SGLang
How to use Lanni-ni/alibi_4_6_384_babylm_100m_seed43 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 "Lanni-ni/alibi_4_6_384_babylm_100m_seed43" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Lanni-ni/alibi_4_6_384_babylm_100m_seed43", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Lanni-ni/alibi_4_6_384_babylm_100m_seed43" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Lanni-ni/alibi_4_6_384_babylm_100m_seed43", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Lanni-ni/alibi_4_6_384_babylm_100m_seed43 with Docker Model Runner:
docker model run hf.co/Lanni-ni/alibi_4_6_384_babylm_100m_seed43
Download ops/stickbreaking_attention_std.py from Lanni-ni/alibi_4_6_384_babylm_100m_seed43: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/Lanni-ni/alibi_4_6_384_babylm_100m_seed43/resolve/main/ops/stickbreaking_attention_std.py
- Command line
-
hf download hf://Lanni-ni/alibi_4_6_384_babylm_100m_seed43/ops/stickbreaking_attention_std.py
-
curl -L -o stickbreaking_attention_std.py https://huggingface.co/Lanni-ni/alibi_4_6_384_babylm_100m_seed43/resolve/main/ops/stickbreaking_attention_std.py
1.11 kB
| """ | |
| Stick-breaking Attention - 官方Triton实现 | |
| """ | |
| from stickbreaking_attention.sb_attn import sb_attn | |
| import math | |
| import torch | |
| from einops import rearrange | |
| from typing import Optional | |
| def stickbreaking_attention_std( | |
| q: torch.Tensor, | |
| k: torch.Tensor, | |
| v: torch.Tensor, | |
| *, | |
| head_first: bool = False, | |
| seq_start: Optional[torch.Tensor] = None, | |
| sm_scale: Optional[float] = None, | |
| normalize: bool = True, | |
| attend_current: bool = False, | |
| ) -> torch.Tensor: | |
| """Stick-breaking attention using official Triton implementation""" | |
| if not head_first: | |
| q = rearrange(q, "b t h d -> b h t d") | |
| k = rearrange(k, "b t h d -> b h t d") | |
| v = rearrange(v, "b t h d -> b h t d") | |
| B, H, T_q, D = q.shape | |
| if sm_scale is None: | |
| sm_scale = 1.0 / math.sqrt(D) | |
| # 官方Triton实现 | |
| # 返回 (output, remainder) | |
| out, rem = sb_attn( | |
| q, k, v, | |
| inv_temp=sm_scale, | |
| attend_current=attend_current | |
| ) | |
| if not head_first: | |
| out = rearrange(out, "b h t d -> b t h d") | |
| return out | |