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 280000; complete resumable checkpoint
Browse files- complete.json +6 -6
- coverage.json +47 -47
- latest.json +1 -1
- model.safetensors +1 -1
- performance.json +3 -3
- shard_scheduler.json +32 -404
- trainer.pt +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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performance.json
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"selected": {
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shard_scheduler.json
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-
"required_pages_per_second_upper_estimate": 0.
|
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},
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{
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"source": "ultra_style",
|
|
@@ -78,8 +78,8 @@
|
|
| 78 |
"tokens_per_second": 1546694.2737633225,
|
| 79 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 80 |
"estimated_pages_for_buffer": 1,
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"required_token_positions_per_second":
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"required_pages_per_second_upper_estimate": 0.
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},
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{
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"source": "cortex",
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@@ -92,8 +92,8 @@
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"tokens_per_second": 1015177.8395898147,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"required_token_positions_per_second":
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"required_pages_per_second_upper_estimate": 0.
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}
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],
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"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
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@@ -158,410 +158,38 @@
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"prefetch_batches": 62,
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"prefetch_capacity_limit": 512,
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"gpu_batches_per_second": 1.
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"producer_headroom":
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"sustainable": true,
|
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"buffer_seconds": 60.
|
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"shard_target_mib":
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"upload_pages_per_commit":
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"generation_bytes_per_second":
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"upload_bytes_per_second":
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"upload_sustainable":
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"estimated_full_shards_per_hour":
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"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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| 23 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 24 |
"estimated_pages_for_buffer": 1,
|
| 25 |
+
"required_token_positions_per_second": 75842.57462698415,
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| 26 |
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"required_pages_per_second_upper_estimate": 0.006573842147418966
|
| 27 |
},
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| 28 |
{
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| 29 |
"source": "rewrite6",
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|
| 36 |
"tokens_per_second": 1206672.263377472,
|
| 37 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 38 |
"estimated_pages_for_buffer": 1,
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| 39 |
+
"required_token_positions_per_second": 9480.321828373018,
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| 40 |
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"required_pages_per_second_upper_estimate": 0.0037818421207806837
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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,
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| 51 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 52 |
"estimated_pages_for_buffer": 1,
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| 53 |
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"required_pages_per_second_upper_estimate": 0.0043862112140569684
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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.",
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| 66 |
"estimated_pages_for_buffer": 1,
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| 67 |
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"required_token_positions_per_second": 28440.965485119053,
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| 68 |
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"required_pages_per_second_upper_estimate": 0.0017319055798300411
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| 69 |
},
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| 70 |
{
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| 71 |
"source": "ultra_style",
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| 78 |
"tokens_per_second": 1546694.2737633225,
|
| 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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"required_token_positions_per_second": 28440.965485119053,
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| 82 |
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"required_pages_per_second_upper_estimate": 0.002189464252675848
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| 83 |
},
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| 84 |
{
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| 85 |
"source": "cortex",
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|
| 92 |
"tokens_per_second": 1015177.8395898147,
|
| 93 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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| 94 |
"estimated_pages_for_buffer": 22,
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| 95 |
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"required_token_positions_per_second": 28440.965485119053,
|
| 96 |
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"required_pages_per_second_upper_estimate": 0.3531416055368222
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| 97 |
}
|
| 98 |
],
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| 99 |
"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
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|
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| 158 |
"prefetch_batches": 62,
|
| 159 |
"prefetch_capacity_limit": 512,
|
| 160 |
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|
| 161 |
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"producer_batch_seconds": 0.080577971249987,
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"producer_p95_seconds": 0.18257642799926543,
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"gpu_update_seconds": 0.9721188971051651,
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"producer_batches_per_second": 12.410339755236285,
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"gpu_batches_per_second": 1.028680753946725,
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| 166 |
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"producer_headroom": 12.06432579556068,
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| 167 |
"sustainable": true,
|
| 168 |
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"buffer_seconds": 60.271371620520235,
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| 169 |
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| 170 |
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"shard_target_mib": 32,
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| 171 |
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"upload_pages_per_commit": 160,
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| 172 |
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| 173 |
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"generation_bytes_per_second": 905156.4916824569,
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| 174 |
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"upload_bytes_per_second": 607793.4295288165,
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| 175 |
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"upload_sustainable": false,
|
| 176 |
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|
| 177 |
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|
| 178 |
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"estimated_full_shards_per_hour": 97.11275607516899,
|
| 179 |
"selection_policy": "Smallest shard within 90% of measured local peak, enlarged to amortize 30 seconds of measured page production; bounded to 256 MiB."
|
| 180 |
},
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| 181 |
"upload_measurements": [
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| 182 |
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|
| 183 |
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"seconds": 0.8291290090000984,
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| 184 |
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"bytes": 27860,
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"pages": 1,
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|
| 186 |
"shards": 1
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| 187 |
},
|
| 188 |
{
|
| 189 |
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"seconds": 0.7566531879992908,
|
| 190 |
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"bytes": 935968,
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| 191 |
"pages": 27,
|
| 192 |
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|
| 193 |
}
|
| 194 |
],
|
| 195 |
"cache_key": {
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|
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|
| 234 |
},
|
| 235 |
"cache_reused": true,
|
| 236 |
"resume_probe_seconds": [
|
| 237 |
+
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|
| 238 |
+
0.014588057999958437
|
| 239 |
]
|
| 240 |
}
|
trainer.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:086c04c0e606c24ef7dab8710d39cde6eff9e0a7ec67fc90f9930de401842100
|
| 3 |
+
size 721763967
|