File size: 3,674 Bytes
744c55e | 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 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | """HF Inference Endpoint custom handler for NX-AI/xLSTM-7b.
Deploys matrix-memory recurrent architecture (Beck et al. 2024) via the HF
Endpoints custom-handler interface. xLSTM introduces mLSTM (matrix-memory
long short-term memory) and sLSTM (exponential-gating scalar LSTM) blocks,
representing a non-SSM non-attention recurrent family.
Input schema (Bench 1.6-A concatenated completion format):
{
"inputs": "<flat text prompt with system + user turns concatenated>",
"parameters": {
"max_new_tokens": 512,
"temperature": 0.7,
"top_p": 0.95,
"do_sample": true,
}
}
Output schema:
{
"generated_text": "<model completion>",
"input_tokens": <int>,
"output_tokens": <int>,
"model": "NX-AI/xLSTM-7b"
}
Preregistered per docs/BENCH-1.6A-PREREG-V1.1-AMENDMENT.md as Cell A3.
Base-model asymmetry (v1.0 §5.5) applies: xLSTM-7b is a base model with no
instruction tuning, receives completion-format prompts.
"""
from __future__ import annotations
from typing import Any
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_ID = "NX-AI/xLSTM-7b"
class EndpointHandler:
"""HF Endpoints custom handler entry point."""
def __init__(self, path: str = "") -> None:
self.model_id = MODEL_ID
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.tokenizer = AutoTokenizer.from_pretrained(
self.model_id,
trust_remote_code=True,
)
# xLSTM-7b at BF16 ≈ 14GB, fits A10G 24GB comfortably.
# device_map="auto" handles multi-GPU gracefully if A100 80GB is used instead.
self.model = AutoModelForCausalLM.from_pretrained(
self.model_id,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
device_map="auto",
)
self.model.eval()
def __call__(self, data: dict[str, Any]) -> dict[str, Any]:
prompt: str = data.get("inputs", "")
params: dict[str, Any] = data.get("parameters", {}) or {}
max_new_tokens: int = int(params.get("max_new_tokens", 512))
temperature: float = float(params.get("temperature", 0.7))
top_p: float = float(params.get("top_p", 0.95))
do_sample: bool = bool(params.get("do_sample", True))
if not prompt:
return {
"generated_text": "",
"input_tokens": 0,
"output_tokens": 0,
"model": self.model_id,
"error": "empty_input",
}
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
input_tokens = int(inputs["input_ids"].shape[-1])
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature if do_sample else 1.0,
top_p=top_p,
do_sample=do_sample,
pad_token_id=self.tokenizer.eos_token_id
if self.tokenizer.pad_token_id is None
else self.tokenizer.pad_token_id,
)
full_text = self.tokenizer.decode(
outputs[0],
skip_special_tokens=True,
)
generated_only = full_text[len(prompt):] if full_text.startswith(prompt) else full_text
output_tokens = int(outputs.shape[-1]) - input_tokens
return {
"generated_text": generated_only,
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"model": self.model_id,
}
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