Text Generation
Transformers
Safetensors
GGUF
English
norn_v20
continuous-latent-cot
neuro-symbolic
holographic-reduced-representations
titans-neural-memory
complex-fourier-manifold
openai-triton
sliding-window-attention
reasoning
math
code
agentic
qwen3.8
osakra-research
conversational
custom_code
Instructions to use Osakra/Project-Norn-V20-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Osakra/Project-Norn-V20-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Osakra/Project-Norn-V20-9B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Osakra/Project-Norn-V20-9B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Osakra/Project-Norn-V20-9B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Osakra/Project-Norn-V20-9B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Osakra/Project-Norn-V20-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Osakra/Project-Norn-V20-9B
- SGLang
How to use Osakra/Project-Norn-V20-9B 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 "Osakra/Project-Norn-V20-9B" \ --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": "Osakra/Project-Norn-V20-9B", "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 "Osakra/Project-Norn-V20-9B" \ --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": "Osakra/Project-Norn-V20-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Osakra/Project-Norn-V20-9B with Docker Model Runner:
docker model run hf.co/Osakra/Project-Norn-V20-9B
Release Project Norn V20: 9.55B Unified Holographic Titan (Titans-HRR + Sliding Window Attention)
102d648 verified Download Modelfile.vision from Osakra/Project-Norn-V20-9B: direct link, hf CLI and curl.
- Browser
- Download file 797 Bytes
-
https://huggingface.co/Osakra/Project-Norn-V20-9B/resolve/main/Modelfile.vision
- Command line
-
hf download hf://Osakra/Project-Norn-V20-9B/Modelfile.vision
-
curl -L -o Modelfile.vision https://huggingface.co/Osakra/Project-Norn-V20-9B/resolve/main/Modelfile.vision
797 Bytes
| # Project Norn V20: Multimodal Holographic Titan | |
| FROM ../hf_norn_v18/Norn-V18-9B-Q4_K_M.gguf | |
| FROM ./mmproj-norn-v20-f16.gguf | |
| PARAMETER num_ctx 65536 | |
| PARAMETER temperature 0.2 | |
| PARAMETER top_p 0.95 | |
| PARAMETER top_k 50 | |
| PARAMETER repeat_penalty 1.15 | |
| PARAMETER num_predict 65536 | |
| PARAMETER stop "<|im_end|>" | |
| PARAMETER stop "<|endoftext|>" | |
| PARAMETER stop "<|im_start|>" | |
| SYSTEM """You are Project Norn V20 ("The Holographic Titan"), an open-source neuro-symbolic reasoning architecture developed by Osakra Research. You possess native vision capabilities via an integrated Vision Titans-HRR Tower. You can directly analyze screenshots, images, diagrams, and user interfaces, parsing spatial topology, geometry, and visual elements natively alongside deep sub-symbolic long-term memory.""" | |