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
Qwen90M step 125000; complete resumable checkpoint
Browse files- complete.json +6 -6
- coverage.json +49 -55
- latest.json +1 -1
- model.safetensors +1 -1
- shard_scheduler.json +270 -24
- trainer.pt +2 -2
- training_config.json +2 -2
complete.json
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coverage.json
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latest.json
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model.safetensors
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shard_scheduler.json
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"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
|
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@@ -155,27 +155,27 @@
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| 155 |
"shard_benchmark_scope": "Local packing/read measurements; upload capacity measured only on real data commits. Targets larger than available pages are sample-limited.",
|
| 156 |
"estimated_fresh_generation_batch_seconds": 0.1434574083060457,
|
| 157 |
"selected": {
|
| 158 |
-
"prefetch_batches":
|
| 159 |
"prefetch_capacity_limit": 512,
|
| 160 |
"batch_memory_estimate_bytes": 688946,
|
| 161 |
-
"producer_batch_seconds": 0.
|
| 162 |
-
"producer_p95_seconds": 0.
|
| 163 |
-
"gpu_update_seconds": 0.
|
| 164 |
-
"producer_batches_per_second":
|
| 165 |
-
"gpu_batches_per_second": 1.
|
| 166 |
-
"producer_headroom":
|
| 167 |
"sustainable": true,
|
| 168 |
-
"buffer_seconds": 60.
|
| 169 |
"buffer_drain_seconds": null,
|
| 170 |
"shard_target_mib": 16,
|
| 171 |
"upload_pages_per_commit": 83,
|
| 172 |
"max_pending_pages": 4096,
|
| 173 |
-
"generation_bytes_per_second":
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-
"upload_bytes_per_second":
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| 175 |
"upload_sustainable": true,
|
| 176 |
"logical_page_rows": 4096,
|
| 177 |
"logical_cortex_page_rows": 128,
|
| 178 |
-
"estimated_full_shards_per_hour":
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| 179 |
"selection_policy": "Smallest shard within 90% of measured local peak, enlarged to amortize 30 seconds of measured page production; bounded to 256 MiB."
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},
|
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"upload_measurements": [
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@@ -184,6 +184,252 @@
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"bytes": 4555121,
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"pages": 1,
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"shards": 1
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| 187 |
}
|
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],
|
| 189 |
"cache_key": {
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|
| 22 |
"tokens_per_second": 1062967.6168812835,
|
| 23 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 24 |
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|
| 25 |
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"required_token_positions_per_second": 76000.48524185155,
|
| 26 |
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"required_pages_per_second_upper_estimate": 0.006587529439294878
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| 27 |
},
|
| 28 |
{
|
| 29 |
"source": "rewrite6",
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|
| 36 |
"tokens_per_second": 1206672.263377472,
|
| 37 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 38 |
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|
| 39 |
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|
| 40 |
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"required_pages_per_second_upper_estimate": 0.003789716233936271
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| 41 |
},
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| 42 |
{
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| 43 |
"source": "openmath",
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|
| 50 |
"tokens_per_second": 410042.7581134458,
|
| 51 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 56 |
{
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| 57 |
"source": "ultra_qa",
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|
| 64 |
"tokens_per_second": 2163486.5624068812,
|
| 65 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 66 |
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| 67 |
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| 68 |
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| 69 |
},
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| 70 |
{
|
| 71 |
"source": "ultra_style",
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|
| 78 |
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|
| 79 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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| 84 |
{
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| 85 |
"source": "cortex",
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|
| 91 |
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| 92 |
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| 94 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
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|
|
|
| 155 |
"shard_benchmark_scope": "Local packing/read measurements; upload capacity measured only on real data commits. Targets larger than available pages are sample-limited.",
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| 156 |
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| 157 |
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"prefetch_batches": 62,
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| 180 |
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| 184 |
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trainer.pt
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@@ -1,3 +1,3 @@
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|
| 1 |
version https://git-lfs.github.com/spec/v1
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oid sha256:
|
| 3 |
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size
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|
| 1 |
version https://git-lfs.github.com/spec/v1
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oid sha256:d623373e105ee371e8cd3e233d58364ce3b44c87e5d4ec425bc868f1ad80c99f
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| 3 |
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size 721783615
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training_config.json
CHANGED
|
@@ -46,8 +46,8 @@
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|
| 46 |
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|
| 46 |
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