Text Generation
Transformers
Safetensors
GGUF
English
multilingual
deepnarra_paham
deepnarra
paham
4.17m
v1.0
sovereign-sage
3-gear-router
elastic-sizing
hybrid-gla-dsa
awake-continuum
bitnet-b158
tq1_0
Instructions to use DeepNarra/Paham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download NOTICE from DeepNarra/Paham: direct link, hf CLI and curl.
- Browser
- Download file 2.33 kB
-
https://huggingface.co/DeepNarra/Paham/resolve/main/NOTICE
- Command line
-
hf download hf://DeepNarra/Paham/NOTICE
-
curl -L -o NOTICE https://huggingface.co/DeepNarra/Paham/resolve/main/NOTICE
2.33 kB
| DeepNarra Sovereign AI Ecosystem | |
| Copyright 2026 DeepNarra AI Organization (https://huggingface.co/DeepNarra) | |
| This product includes software, model weights, and architectural designs developed by | |
| the DeepNarra AI Organization under the Apache License, Version 2.0. | |
| ================================================================================ | |
| UPSTREAM DATASET ATTRIBUTIONS & LICENSING DISCLOSURES | |
| ================================================================================ | |
| In accordance with open-data governance and Hugging Face Hub compliance, the | |
| following streaming datasets and open-source corpora are attributed: | |
| 1. FineWeb-Edu (HuggingFaceFW/fineweb-edu) | |
| - License: Open Data Commons Attribution License (ODC-By) v1.0 | |
| - Attribution: Lozhkov et al., Hugging Face FineWeb-Edu Dataset. Used for | |
| foundational educational prose pre-training. | |
| 2. Aya Multilingual Collection (CohereForAI/aya_collection) | |
| - License: Apache License, Version 2.0 | |
| - Attribution: Singh et al., Cohere For AI. Used for multilingual tokenizer | |
| and cross-lingual representation alignment. | |
| 3. CodeFeedback Filtered Instruction (m-a-p/CodeFeedback-Filtered-Instruction) | |
| - License: Apache License, Version 2.0 | |
| - Attribution: Multimodal Art Projection (M-A-P). Used for algorithmic code | |
| synthesis and verification training. | |
| 4. No-Robots (HuggingFaceH4/no_robots) | |
| - License: CC-BY-NC-4.0 (Attributed for research & instruction alignment evaluation) | |
| - Attribution: Rajani et al., Hugging Face H4. | |
| 5. Procedural Symbolic Mathematics (SymPy CAS Infinite Generator) | |
| - Generated synthetically on-the-fly using SymPy Computer Algebra System | |
| (BSD-3-Clause License) with deterministic formal proof verification. | |
| ================================================================================ | |
| ARCHITECTURAL & ALGORITHMIC CITATIONS | |
| ================================================================================ | |
| - Mamba-2 State Space Duality (SSD): Dao & Gu (2024), Apache-2.0. | |
| - Gated Linear Attention (GLA): Yang et al. (2024), MIT License. | |
| - BitNet b1.58 Ternary Quantization: Ma et al. (2024), MIT License. | |
| - Coconut Continuous Latent Space: Hao et al. (2024), MIT License. | |
| - DeepSeek-R1 Group Relative Policy Optimization (GRPO): DeepSeek-AI (2025), MIT License. | |