Instructions to use kayrahan35/HFP-O1-Memory-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kayrahan35/HFP-O1-Memory-Model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kayrahan35/HFP-O1-Memory-Model", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("kayrahan35/HFP-O1-Memory-Model", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kayrahan35/HFP-O1-Memory-Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kayrahan35/HFP-O1-Memory-Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kayrahan35/HFP-O1-Memory-Model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kayrahan35/HFP-O1-Memory-Model

SGLang

How to use kayrahan35/HFP-O1-Memory-Model 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 "kayrahan35/HFP-O1-Memory-Model" \
    --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": "kayrahan35/HFP-O1-Memory-Model",
		"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 "kayrahan35/HFP-O1-Memory-Model" \
        --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": "kayrahan35/HFP-O1-Memory-Model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kayrahan35/HFP-O1-Memory-Model with Docker Model Runner:
```
docker model run hf.co/kayrahan35/HFP-O1-Memory-Model
```

HFP-O1-Memory-Model / hfp_config.py

kayrahan35

Upload 11 files

f9b2068 verified about 1 month ago

Raw

History Blame Contribute Delete

2.43 kB

	# Hyper Flux Projection (HFP) — O(1)-memory causal language model
	# Copyright (C) 2026 Kayrahan Yılmaz
	#
	# This program is free software: you can redistribute it and/or modify
	# it under the terms of the GNU Affero General Public License as published
	# by the Free Software Foundation, either version 3 of the License, or
	# (at your option) any later version.
	#
	# This program is distributed in the hope that it will be useful,
	# but WITHOUT ANY WARRANTY; without even the implied warranty of
	# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	# GNU Affero General Public License for more details.
	#
	# You should have received a copy of the GNU Affero General Public License
	# along with this program. If not, see <https://www.gnu.org/licenses/>.

	import dataclasses

	@dataclasses.dataclass
	class HFPConfig:
	"""Feature flags and hyper-parameters for optional physics-inspired analogues.

	All flags are disabled by default to keep the baseline model clean, fast
	and honest: these physics-inspired aux terms are experimental hooks, NOT
	load-bearing parts of the trained model. Enable one only to test whether
	it adds value; when off, no wasted compute and no false "physics" claim.
	(Onceki surumde hepsi True idi ama modeling bunlari loss'a hic baglamiyordu
	-> olu hesap. Durust baseline icin kapatildi; ilham olarak deneye acik kalir.)
	"""
	# Feature toggles - deneysel, default kapali (opt-in)
	ENABLE_CURVATURE: bool = False
	ENABLE_ENTROPY_MAP: bool = False
	ENABLE_DEFECT_FLAG: bool = False
	ENABLE_COHERENCE: bool = False
	ENABLE_CONSERVATION: bool = False
	ENABLE_RYU_TAKAYANAGI: bool = False
	ENABLE_5D_CURVATURE: bool = False

	# Hyper-parameters (used when the feature is enabled)
	REG_WEIGHT: float = 0.01 # gate-entropy regularisation weight
	LANDMARK_MAX: int = 49 # max entries in landmark buffer
	ENTROPY_THRESH: float = 0.25 # threshold for dynamic short-memory expansion
	MAX_SHORT_LEN: int = 32 # maximum short-memory length (tokens)
	GRAD_CLIP_VAL: float = 0.5 # gradient-clipping value per memory block
	MIXED_PRECISION: bool = True # use torch.float16 for gate logits only
	WARP_K: float = 0.5 # Witten propagator warp factor

	# Global singleton configuration used throughout the package
	config = HFPConfig()