Instructions to use BernaLabs/berna-r8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BernaLabs/berna-r8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BernaLabs/berna-r8")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BernaLabs/berna-r8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BernaLabs/berna-r8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BernaLabs/berna-r8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BernaLabs/berna-r8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BernaLabs/berna-r8
- SGLang
How to use BernaLabs/berna-r8 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 "BernaLabs/berna-r8" \ --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": "BernaLabs/berna-r8", "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 "BernaLabs/berna-r8" \ --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": "BernaLabs/berna-r8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BernaLabs/berna-r8 with Docker Model Runner:
docker model run hf.co/BernaLabs/berna-r8
Berna R8 β Cell-Based Language Model
Built on Berna R7 v3. Includes Bounded Forgetting experiments.
R8-v2 (Current, from scratch)
- Training: 10,200 steps, eng+math
- Architecture: 28L Γ 1536d, 1.2B params, 5,000 cell cap
- lr: 1.5e-4, accum: 4, measure_every: 500
- Best loss: ~4.0
- Cells: ~4,500 (capped at 5,000)
- Births: ~360
R8-v1 (First attempt, stopped)
- Best loss: 5.49 (plateaued)
- Cells: 5,000 (hit cap)
- Issue: lr=3e-4 unstable
H2 Experiment β Bounded Forgetting (CORRECTED)
- Base: R7 v3 (step_1400)
- Fine-tune: 2,000 steps on German
- Results:
Metric Value ENG before 4.078 ENG after 4.481 F_eng 0.403 DE before 4.741 DE after 2.678 - F_eng = 0.403 (10% forgetting, bounded)
Fixes over R7 v3
- K2/K5/K6 per-cell
- Plexus threshold 0.3
- Cell cap = 5,000 (anti-explosion)
- SmartFreezer active
License
CC BY-NC 4.0 β research use only.
Citation
@misc{berna_r8_2026, author = {Kamil, Muhamed}, title = {Berna R8}, year = {2026}}