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

File size: 891 Bytes

dda583f

{
  "ENABLE_COHERENCE": false,
  "architectures": [
    "HFPForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_hfp.HFPConfig",
    "AutoModelForCausalLM": "modeling_hfp.HFPForCausalLM"
  },
  "aux_gate_entropy_weight": 0.0,
  "aux_ortho_weight": 0.0,
  "bos_token_id": 1,
  "bptt_across_chunks": false,
  "bulk_dim": 128,
  "conv_kernel": 3,
  "decay_mode": "exp",
  "dpfp_nu": 2,
  "dropout_p": 0.1,
  "dtype": "float32",
  "eos_token_id": 2,
  "hidden_size": 768,
  "intermediate_size": 3072,
  "key_feature_map": "elu",
  "local_window": 64,
  "max_position_embeddings": 4096,
  "max_short_len": null,
  "model_type": "hfp",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pe_scale": 0.3,
  "rec_block": 64,
  "short_len": 8,
  "tie_word_embeddings": true,
  "transformers_version": "5.13.0",
  "tunnel_decay": 0.8,
  "tunnel_depth": 3,
  "vocab_size": 50257
}