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
File size: 2,305 Bytes
9f0e0ef 0cf64c3 164af9e 0cf64c3 164af9e 9f0e0ef 0cf64c3 164af9e 0cf64c3 164af9e 0cf64c3 164af9e 9f0e0ef 0cf64c3 9f0e0ef 0cf64c3 66082e4 0cf64c3 66082e4 942705a d45a371 0cf64c3 9f0e0ef 0cf64c3 164af9e db6cf6d 164af9e 0cf64c3 164af9e 9f0e0ef d45a371 0cf64c3 9f0e0ef 0cf64c3 164af9e 942705a 164af9e 0cf64c3 164af9e 9f0e0ef b85a545 0cf64c3 9f0e0ef 0cf64c3 164af9e d45a371 164af9e d45a371 0cf64c3 164af9e 9f0e0ef d45a371 0cf64c3 9f0e0ef 0cf64c3 9f0e0ef 0cf64c3 9f0e0ef | 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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | {
"step": 585000,
"stats": {
"climbmix/blocks": 21060000,
"climbmix/targets": 35204728526,
"climbmix/input_tokens": 35206959792,
"ultra_qa/blocks": 7897500,
"ultra_qa/targets": 12884016978,
"ultra_qa/input_tokens": 12884041832,
"cortex/blocks": 7897500,
"cortex/targets": 300307032,
"cortex/input_tokens": 12553655276,
"ultra_style/blocks": 7897500,
"ultra_style/targets": 12491928833,
"ultra_style/input_tokens": 12492584132,
"openmath/blocks": 5265000,
"openmath/targets": 8135369806,
"openmath/input_tokens": 8138079413,
"rewrite6/blocks": 2632500,
"rewrite6/targets": 4315618350,
"rewrite6/input_tokens": 4320349938
},
"sources": {
"climbmix": {
"page": 2884,
"row": 2144,
"start": {
"file": 139,
"reader": {
"format": "parquet-row-v1",
"row_group": 47,
"row": 1024
}
},
"epoch": 0,
"offset": 2048,
"documents": 11815008
},
"rewrite6": {
"page": 41,
"row": 2,
"start": {
"file": 0,
"reader": {
"format": "jsonl-row-v1",
"row": 167936
}
},
"epoch": 32,
"offset": 0,
"documents": 6740866
},
"openmath": {
"page": 1802,
"row": 1223,
"start": {
"file": 16,
"reader": {
"format": "parquet-row-v1",
"row_group": 394,
"row": 592
}
},
"epoch": 0,
"offset": 2048,
"documents": 7382215
},
"ultra_qa": {
"page": 779,
"row": 1531,
"start": {
"file": 6,
"reader": {
"format": "parquet-row-v1",
"row_group": 1,
"row": 8520
}
},
"epoch": 0,
"offset": 2048,
"documents": 3192315
},
"ultra_style": {
"page": 1189,
"row": 2100,
"start": {
"file": 7,
"reader": {
"format": "parquet-row-v1",
"row_group": 0,
"row": 75550
}
},
"epoch": 0,
"offset": 2048,
"documents": 4872244
},
"cortex": {
"page": 153166,
"row": 102,
"start": null,
"epoch": 0,
"offset": 0,
"documents": 19605350
}
}
} |