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
Download code/k_vector/k2_breadth.py from BernaLabs/berna-r8: direct link, hf CLI and curl.
- Browser
- Download file 870 Bytes
-
https://huggingface.co/BernaLabs/berna-r8/resolve/main/code/k_vector/k2_breadth.py
- Command line
-
hf download hf://BernaLabs/berna-r8/code/k_vector/k2_breadth.py
-
curl -L -o k2_breadth.py https://huggingface.co/BernaLabs/berna-r8/resolve/main/code/k_vector/k2_breadth.py
870 Bytes
| """ | |
| K2 — Domain breadth per-cell (R8 fix). | |
| K2[cell] = distinct domains seen / total domains seen. | |
| """ | |
| from collections import defaultdict | |
| def compute_k2_percell(cells, cell_domains): | |
| """ | |
| Args: | |
| cells: iterable of Cell | |
| cell_domains: {cell_id: set(domain_names)} | |
| Returns: | |
| {cell_id: K2 in [0, 1]} | |
| """ | |
| all_domains = set() | |
| for doms in cell_domains.values(): | |
| all_domains.update(doms) | |
| n_total = max(1, len(all_domains)) | |
| out = {} | |
| for c in cells: | |
| doms = cell_domains.get(c.cell_id, set()) | |
| out[c.cell_id] = len(doms) / n_total | |
| return out | |
| def update_cell_domains(cell_domains, cells, active_ids, domain_name): | |
| """Add domain_name to each active cell's domain set.""" | |
| for cid in active_ids: | |
| cell_domains.setdefault(cid, set()).add(domain_name) |