Instructions to use Lanni-ni/alibi_2_4_256_babylm_10m_seed43 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lanni-ni/alibi_2_4_256_babylm_10m_seed43 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Lanni-ni/alibi_2_4_256_babylm_10m_seed43", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Lanni-ni/alibi_2_4_256_babylm_10m_seed43", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Lanni-ni/alibi_2_4_256_babylm_10m_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_2_4_256_babylm_10m_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_2_4_256_babylm_10m_seed43", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Lanni-ni/alibi_2_4_256_babylm_10m_seed43
- SGLang
How to use Lanni-ni/alibi_2_4_256_babylm_10m_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_2_4_256_babylm_10m_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_2_4_256_babylm_10m_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_2_4_256_babylm_10m_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_2_4_256_babylm_10m_seed43", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Lanni-ni/alibi_2_4_256_babylm_10m_seed43 with Docker Model Runner:
docker model run hf.co/Lanni-ni/alibi_2_4_256_babylm_10m_seed43
Download configuration_alibi.py from Lanni-ni/alibi_2_4_256_babylm_10m_seed43: direct link, hf CLI and curl.
- Browser
- Download file 2.26 kB
-
https://huggingface.co/Lanni-ni/alibi_2_4_256_babylm_10m_seed43/resolve/main/configuration_alibi.py
- Command line
-
hf download hf://Lanni-ni/alibi_2_4_256_babylm_10m_seed43/configuration_alibi.py
-
curl -L -o configuration_alibi.py https://huggingface.co/Lanni-ni/alibi_2_4_256_babylm_10m_seed43/resolve/main/configuration_alibi.py
2.26 kB
| # -*- coding: utf-8 -*- | |
| from typing import Optional | |
| from transformers.configuration_utils import PretrainedConfig | |
| class AlibiConfig(PretrainedConfig): | |
| model_type = 'alibi' | |
| keys_to_ignore_at_inference = ['past_key_values'] | |
| def __init__( | |
| self, | |
| vocab_size: int = 32000, | |
| hidden_size: int = 2048, | |
| hidden_ratio: Optional[int] = 4, | |
| intermediate_size: Optional[int] = None, | |
| num_hidden_layers: int = 24, | |
| num_heads: int = 32, | |
| num_kv_heads: int = None, | |
| hidden_act: str = "swish", | |
| window_size: Optional[int] = None, | |
| max_position_embeddings: int = 2048, | |
| initializer_range: float = 0.02, | |
| elementwise_affine: Optional[bool] = True, | |
| norm_eps: float = 1e-6, | |
| use_cache: bool = True, | |
| pad_token_id: int = None, | |
| bos_token_id: int = 1, | |
| eos_token_id: int = 2, | |
| tie_word_embeddings: bool = False, | |
| attention_bias: bool = False, | |
| fuse_norm: bool = True, | |
| fuse_cross_entropy: bool = True, | |
| rope_base: float = 500000.0, | |
| use_rope: bool = False, | |
| use_alibi: bool = True, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.hidden_ratio = hidden_ratio | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_heads = num_heads | |
| self.num_kv_heads = num_kv_heads | |
| self.window_size = window_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.hidden_act = hidden_act | |
| self.initializer_range = initializer_range | |
| self.elementwise_affine = elementwise_affine | |
| self.norm_eps = norm_eps | |
| self.use_cache = use_cache | |
| self.attention_bias = attention_bias | |
| self.fuse_cross_entropy = fuse_cross_entropy | |
| self.fuse_norm = fuse_norm | |
| self.rope_base = rope_base | |
| self.use_rope = use_rope | |
| self.use_alibi = use_alibi | |
| super().__init__( | |
| pad_token_id=pad_token_id, | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) |