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
File size: 2,257 Bytes
b9b11d5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | # -*- 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,
) |