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
File size: 1,723 Bytes
d997548 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | from pathlib import Path
p = Path("/data/berna-r7/core/code/cells/dna_kernel.py")
text = p.read_text()
old = '''def decide(dna: DNA, S: float) -> dict:
"""
(D_c, K_c, u_c) -> {route, grow, split, retain}
Returns a dict of signals + a discrete decision.
"""
route = signal_route(dna)
grow = signal_grow(dna)
split = signal_split(dna, S)
retain = signal_retain(dna)
signals = {"route": route, "grow": grow, "split": split, "retain": retain}
# Discrete decision: highest signal wins
decision = max(signals, key=signals.get)
signals["decision"] = decision
return signals'''
new = '''def decide(dna: DNA, S: float, S_star: float = 0.85,
t_consecutive: int = 3) -> dict:
"""
(D_c, K_c, u_c) -> {route, grow, split, retain}
Priority-based decision per R5 Section 3.5.
Priority order (per R5):
1. split - if S >= S* sustained (handled by caller's S_history)
2. retain - if persistence high (frozen state)
3. grow - if plasticity high and not saturated
4. route - default behavior
"""
route = signal_route(dna)
grow = signal_grow(dna)
split = signal_split(dna, S, S_star)
retain = signal_retain(dna)
signals = {"route": route, "grow": grow, "split": split, "retain": retain}
# Priority-based decision (R5 Section 3.5)
if S >= S_star:
decision = "split"
elif retain > 0.7:
decision = "retain"
elif grow > 0.6:
decision = "grow"
else:
decision = "route"
signals["decision"] = decision
return signals'''
assert old in text, "pattern not found"
text = text.replace(old, new, 1)
p.write_text(text)
print("✅ decide() patched")
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