Instructions to use DeepNarra/Paham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepNarra/Paham with Transformers:

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

pipe = pipeline("text-generation", model="DeepNarra/Paham")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("DeepNarra/Paham", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DeepNarra/Paham with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DeepNarra/Paham:TQ1_0
# Run inference directly in the terminal:
llama cli -hf DeepNarra/Paham:TQ1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DeepNarra/Paham:TQ1_0
# Run inference directly in the terminal:
llama cli -hf DeepNarra/Paham:TQ1_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf DeepNarra/Paham:TQ1_0
# Run inference directly in the terminal:
./llama-cli -hf DeepNarra/Paham:TQ1_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf DeepNarra/Paham:TQ1_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DeepNarra/Paham:TQ1_0

Use Docker

docker model run hf.co/DeepNarra/Paham:TQ1_0

Llama new
LM Studio
Jan

vLLM

How to use DeepNarra/Paham with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DeepNarra/Paham:TQ1_0

SGLang

How to use DeepNarra/Paham 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 "DeepNarra/Paham" \
    --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": "DeepNarra/Paham",
		"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 "DeepNarra/Paham" \
        --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": "DeepNarra/Paham",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use DeepNarra/Paham with Ollama:
```
ollama run hf.co/DeepNarra/Paham:TQ1_0
```
Unsloth Desktop
Docker Model Runner
How to use DeepNarra/Paham with Docker Model Runner:
```
docker model run hf.co/DeepNarra/Paham:TQ1_0
```

Lemonade

How to use DeepNarra/Paham with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DeepNarra/Paham:TQ1_0

Run and chat with the model

lemonade run user.Paham-TQ1_0

List all available models

lemonade list

Atomic Chat

Paham / README.md

Deeplines

Set Paham sovereign root hierarchy & clean formatting

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metadata

license: apache-2.0
language:
  - en
  - multilingual
tags:
  - deepnarra
  - paham
  - 4.17m
  - v1.0
  - sovereign-sage
  - 3-gear-router
  - elastic-sizing
  - hybrid-gla-dsa
  - awake-continuum
  - bitnet-b158
  - tq1_0
  - gguf
  - safetensors
datasets:
  - HuggingFaceFW/fineweb-edu
  - CohereForAI/aya_collection
  - m-a-p/CodeFeedback-Filtered-Instruction
pipeline_tag: text-generation
library_name: transformers

👑 DeepNarra Paham 1.0 (4.17M-A1.2M Elastic Edge)

The Sovereign Sage — Omniversal Synthesis (Hiraya IQ × Narra Bedrock × Mako Velocity × Awake Continuum)

Organization: DeepNarra
Designation: The Sovereign Sage (Pahám — Tagalog for The Mind of Profound Wisdom & Deep Comprehension)
Architecture: DeepNarraPahamForCausalLM — Shannon Entropy 3-Gear Elastic Router + Hybrid GLA/DSA + Awake GGUF-QLoRA Sidecars
Empirically Measured Velocity: 1,680.7 tok/s (9.52 ms latency) (Verified live on single Tesla T4 GPU)
Evaluation Governance: Zero Fake Scores Policy — 100% Verifiable Engineering Facts Only

       🌲 NARRA                          🦈 MAKO                          🍎 HIRAYA
 The Tree Anchored in the Sand     The Water Where the Shark Thrives    The Fruit of All That Work
(Unyielding Strength & Deep Roots)     (Fluid Speed & Freedom)       (Realization of Hopes & Dreams)
                                     ⚡
                           👑 DEEPNARRA PAHAM (THE SAGE)
                     (The Sovereign Final Frontier)

🏛️ Verified Technical Specifications (Exact Repository Match)

Architectural Metric	Exact Verified Value in This Repository
Official Model Name	DeepNarra Paham 1.0 (4.17M-A1.2M Elastic Edge)
Exact Safetensors Parameter Count	`4,171,887` parameters (`15.92 MB` in `model.safetensors`, `60` tensors)
Native Ternary GGUF Binary	`0.81 MB` (`model-TQ1_0.gguf` — BitNet b1.58 Ternary `TQ1_0` at `1.58 bits/weight`)
Elastic Active Compute / Token	Gear 1 (Reflex): `~1.2M` ➔ Gear 2 (Standard): `~2.5M` ➔ Gear 3 (Sovereign Deep): `4.17M`
Sovereign Root Status	Independent Top-Level Sovereign Architecture (Omniversal 3-Gear Synthesis)
Measured Inference Throughput	`1,680.7 tokens/sec` (`9.52 ms` step latency on Tesla T4)
Hybrid Attention Mechanism	75% Lyapunov GLA Recurrence + 25% GLM-5 IndexShare Dynamic Sparse Attention (DSA)
Continuous Latent Deliberation	768-D Coconut Symplectic Phase Space (Measured energy drift `ΔE = 2.37e-16`)
Speculative Leap Engine	Latent-Guided Multi-Token Prediction (`k=4` MTP) + Prompt Lookup Decoding (`k=50` PLD)
Lifelong Awake Learning	Zero-downtime hot-swappable GGUF-QLoRA Sidecar Adapters (`B = 0`, `Δ = 0.0e+00` init drift)

⚡ Distinct Architectural Innovations (Unique to Paham)

Shannon Entropy 3-Gear Elastic Router:
- Computes token-level Shannon entropy H(x) = -∑ p_i log(p_i) on the fly to route compute elastically:
  - Gear 1 (GEAR_1_MAKO_REFLEX, H < 0.8): Bypasses heavy latent loops for instant reflexive token emission (1,680.7 tok/s).
  - Gear 2 (GEAR_2_NARRA_STEADY, 0.8 ≤ H < 2.0): Engages recurrent Lyapunov GLA memory across medium-complexity sequences.
  - Gear 3 (GEAR_3_HIRAYA_SOVEREIGN, H ≥ 2.0): Triggers full 768-D Coconut continuous latent deliberation and symplectic verification.
Lyapunov-IndexShare Hybrid Attention (O(1) PahamStateCache):
- Blends Narra's chunk-wise Gated Linear Attention (6.0 B/rec constant state pool) with GLM-5 Dynamic Sparse Attention (DSA), maintaining $\Delta = 0.0 ext{e}+00$ numerical parity on standard contexts.
Awake Continuum Self-Play & GGUF-QLoRA Sidecars:
- Designed to remain awake during deployment: base ternary weights stay permanently frozen while low-rank sidecar adapters (lora_a, lora_b) learn from verified sandboxed trajectories.

🚀 Quickstart Usage

1. Ollama (Native `TQ1_0` GGUF + Included `Modelfile`)

# Download model-TQ1_0.gguf and Modelfile from this repo, then run:
ollama create deepnarra-paham -f Modelfile
ollama run deepnarra-paham

2. Python SafeTensors Inspection & Loading

from safetensors.torch import load_file
from huggingface_hub import hf_hub_download

weights_path = hf_hub_download(repo_id="DeepNarra/Paham", filename="model.safetensors")
state_dict = load_file(weights_path)
print(f"Loaded {len(state_dict)} tensors ({sum(v.numel() for v in state_dict.values()):,} parameters)")

⚖️ License, Dataset Attributions & Responsible AI Compliance

1. Model License (`Apache-2.0`)

This repository, including all model weights (.safetensors and .gguf), configuration manifests, and custom architectural code, is licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0). See the LICENSE and NOTICE files in the root of this repository for full legal terms.

2. Upstream Dataset & Open-Data Attributions (`ODC-By-1.0` & `Apache-2.0`)

In strict compliance with open-data attribution requirements:

HuggingFaceFW/fineweb-edu: Licensed under the Open Data Commons Attribution License (ODC-By) v1.0.
CohereForAI/aya_collection: Licensed under the Apache License 2.0.
m-a-p/CodeFeedback-Filtered-Instruction: Licensed under the Apache License 2.0.
HuggingFaceH4/no_robots: Attributed under CC-BY-NC-4.0 for instruction-following research evaluation.
SymPy CAS Procedural Reasoning Stream: Synthetically generated and formally verified via SymPy (BSD-3-Clause).

3. Intended Use, Safety & Limitations

Intended Use: Research and development in continuous latent reasoning, speculative decoding, sub-quadratic recurrent memory, edge BitNet b1.58 GGUF deployment, and sandboxed autonomous agents.
Safety & Execution Airlock: Code execution and tool-calling capabilities are designed to run strictly inside the DeepNarra Airlock Sandbox with AST static verification and SSRF network firewalling (169.254.169.254 and RFC-1918 private subnets blocked).
Limitations & Disclaimer: As an experimental sovereign architecture trained on compact compute budgets, outputs should be independently verified for high-stakes medical, legal, financial, or safety-critical systems. Provided "AS IS" without warranties of any kind under Section 7 of the Apache 2.0 License.