Instructions to use amewebstudio/ananke-sclm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amewebstudio/ananke-sclm with Transformers:

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

pipe = pipeline("text-generation", model="amewebstudio/ananke-sclm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amewebstudio/ananke-sclm", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use amewebstudio/ananke-sclm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amewebstudio/ananke-sclm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amewebstudio/ananke-sclm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amewebstudio/ananke-sclm

SGLang

How to use amewebstudio/ananke-sclm 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 "amewebstudio/ananke-sclm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amewebstudio/ananke-sclm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "amewebstudio/ananke-sclm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amewebstudio/ananke-sclm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amewebstudio/ananke-sclm with Docker Model Runner:
```
docker model run hf.co/amewebstudio/ananke-sclm
```

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🧠 SCLM: Stateful Coherent Language Model

SCLM adds persistent latent memory to transformer language models, enabling better coherence across long conversations and multi-turn generation.

🎯 Key Features

Persistent State: Memory that evolves across conversation turns
Entity Coherence: Maintains context about characters, places, and objects
Edit Mode: Make local changes without affecting global memory
Lightweight: Only 91.7M additional parameters (2.44% overhead)

📊 Architecture: EARCP

EARCP = Encapsulation + Alignment + Revision + Coherence + Propagation

Component	Function
Encapsulation	GRU-style state update from hidden states
Alignment	Cross-attention between state and hidden layers
Revision	Drift detection and correction
Coherence	Mixture-of-Experts for consistency
Propagation	State injection into transformer layers

🔧 Model Details

Parameter	Value
Base Model	mistralai/Mistral-7B-v0.1
EARCP Parameters	91.7M
Latent State Dim	256
Injection Layers	[8, 16]
Alpha (injection strength)	0.02
Experts	2

🚀 Quick Start

# Note: Full SCLM requires custom loading (see below)
# The inference widget uses the base model only

from transformers import AutoTokenizer
import torch

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("amewebstudio/ananke-sclm")

# For full SCLM functionality, load weights separately:
# 1. Load base Mistral-7B
# 2. Load EARCP weights from earcp_weights.pt
# 3. Apply SCLM wrapper

📈 Validation Results

Test	Result
Forward Pass	✅
State Evolution	✅ (norm: 0 → 4.6 → 7.5)
Coherent Generation	✅
Edit Mode	✅
Entity Memory	✅ (Elara, Nimbus retained)

💡 Use Cases

Interactive Fiction: Characters and plot points remain consistent
Long Conversations: Context persists without growing prompts
Creative Writing: Maintain story coherence across chapters
Role-Playing: NPCs remember past interactions

📝 Citation

@article{amega2025sclm,
  title={SCLM: Stateful Coherent Language Models with EARCP Architecture},
  author={Amega, Mike},
  year={2025},
  note={Ame Web Studio}
}

👤 Author

Mike Amega - Ame Web Studio

Business Source License 1.1 (BSL-1.1) - See LICENSE for details.

👤 Author Mike Amega - Ame Web Studio 📧 amewebstudio35@gmail.com

SCLM is an experimental architecture exploring persistent memory in language models.

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