Text Generation
Transformers
TensorBoard
Safetensors
English
qwen3
byte-level
pretraining
symbolic
text-generation-inference
Instructions to use dotlabs/void.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dotlabs/void.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dotlabs/void.1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dotlabs/void.1") model = AutoModelForCausalLM.from_pretrained("dotlabs/void.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dotlabs/void.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dotlabs/void.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dotlabs/void.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dotlabs/void.1
- SGLang
How to use dotlabs/void.1 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 "dotlabs/void.1" \ --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": "dotlabs/void.1", "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 "dotlabs/void.1" \ --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": "dotlabs/void.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dotlabs/void.1 with Docker Model Runner:
docker model run hf.co/dotlabs/void.1
Download benchmark_cache/shard_scheduler.json from dotlabs/void.1: direct link, hf CLI and curl.
- Browser
- Download file 14.5 kB
-
https://huggingface.co/dotlabs/void.1/resolve/9e598e66d628058da170496d881ff8e781c5eee5/benchmark_cache/shard_scheduler.json
- Command line
-
hf download hf://dotlabs/void.1@9e598e66d628058da170496d881ff8e781c5eee5/benchmark_cache/shard_scheduler.json
-
curl -L -o shard_scheduler.json https://huggingface.co/dotlabs/void.1/resolve/9e598e66d628058da170496d881ff8e781c5eee5/benchmark_cache/shard_scheduler.json
14.5 kB
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| "ultra_style": 15, | |
| "cortex": 15, | |
| "openmath": 10, | |
| "rewrite6": 5 | |
| }, | |
| "cpu_threads": 4 | |
| }, | |
| "source": "f8722b66ff2f92bca174f36564336149e85c65c48b964d05ff3a4d5d85da308f", | |
| "cpu": "unknown", | |
| "cpu_count": 20, | |
| "microbatch": 90 | |
| }, | |
| "cache_reused": true, | |
| "resume_probe_seconds": [ | |
| 2.576500048000014, | |
| 0.012398547999964649 | |
| ] | |
| } |