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
Update TensorBoard training metrics
Browse files
benchmark_cache/performance.json
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"selected": {
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"mode": "compiled",
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"estimated_update_seconds": 0.
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"input_tokens_per_second":
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"measurements": [
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{
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"selected": {
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"microbatch": 86,
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"mode": "compiled",
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"microbatch_seconds": 0.8980338759999995,
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"peak_gb": 85.39913082122803,
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"fits_budget": true,
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"estimated_update_seconds": 0.9548798630000306,
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"input_tokens_per_second": 196126.2316567667
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},
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"measurements": [
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{
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benchmark_cache/shard_scheduler.json
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"tokens_per_second": 1062967.6168812835,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"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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"source": "rewrite6",
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"tokens_per_second": 1206672.263377472,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"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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"source": "openmath",
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"tokens_per_second": 410042.7581134458,
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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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{
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"source": "ultra_qa",
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"tokens_per_second": 2163486.5624068812,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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{
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"source": "ultra_style",
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"tokens_per_second": 1546694.2737633225,
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"note": "Bounded fresh sample; full-page latency is an extrapolation.",
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"source": "cortex",
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"seconds": 0.8230368889999227,
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| 462 |
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| 463 |
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| 464 |
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{
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| 465 |
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"seconds": 1.1317692520024139,
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| 466 |
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"bytes": 10909640,
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| 467 |
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"pages": 41,
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| 468 |
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"shards": 3
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| 469 |
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},
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| 470 |
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{
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| 471 |
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"seconds": 0.8850410689992714,
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"bytes": 875876,
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"pages": 26,
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| 474 |
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"shards": 1
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| 475 |
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},
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| 476 |
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{
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| 477 |
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"seconds": 1.366803572003846,
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| 478 |
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"bytes": 13265901,
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| 480 |
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"shards": 4
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| 481 |
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"seconds": 1.1613982180060702,
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"seconds": 1.1397506580033223,
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"seconds": 1.4992664259989397,
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"seconds": 0.7898501529998612,
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"seconds": 1.4932303839959786,
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"seconds": 1.2937701949995244,
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"pages": 31,
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| 558 |
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"seconds": 1.1349576159991557,
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"bytes": 5303247,
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| 565 |
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}
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| 566 |
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| 567 |
"cache_key": {
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| 568 |
"version": 1,
|
| 569 |
"kind": "data",
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|
@@ -606,7 +221,7 @@
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|
| 606 |
},
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| 607 |
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| 608 |
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| 609 |
-
2.
|
| 610 |
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0.
|
| 611 |
]
|
| 612 |
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| 22 |
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|
| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 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 |
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| 39 |
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| 40 |
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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.",
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| 66 |
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| 67 |
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| 68 |
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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.",
|
| 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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|
| 93 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 94 |
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"estimated_pages_for_buffer": 22,
|
| 95 |
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|
| 96 |
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"required_pages_per_second_upper_estimate": 0.3595170883778412
|
| 97 |
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|
| 98 |
],
|
| 99 |
"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
|
|
|
|
| 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 |
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|
| 157 |
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|
| 158 |
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"prefetch_batches": 63,
|
| 159 |
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|
| 160 |
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|
| 161 |
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"producer_batch_seconds": 1.3818725664999931,
|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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"gpu_batches_per_second": 1.0472521609767866,
|
| 166 |
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|
| 167 |
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"sustainable": false,
|
| 168 |
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|
| 169 |
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| 170 |
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|
| 171 |
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"upload_pages_per_commit": 6,
|
| 172 |
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"max_pending_pages": 1513,
|
| 173 |
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"generation_bytes_per_second": 0,
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| 174 |
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"upload_bytes_per_second": null,
|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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"estimated_full_shards_per_hour": 0.0,
|
| 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 |
},
|
| 181 |
+
"upload_measurements": [],
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|
| 182 |
"cache_key": {
|
| 183 |
"version": 1,
|
| 184 |
"kind": "data",
|
|
|
|
| 221 |
},
|
| 222 |
"cache_reused": true,
|
| 223 |
"resume_probe_seconds": [
|
| 224 |
+
2.7487001069999906,
|
| 225 |
+
0.015045025999995687
|
| 226 |
]
|
| 227 |
}
|
runs/void-byte-v1/events.out.tfevents.1790563279.bdfa290c-aca9-4e96-a5d2-8425921a3278-d85hq.1304.0.1790563279934888633-82973089
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bd84b829c1c84630d8f7a77b4bf11b57455fc6af49ba305c9c917038a5e91ad2
|
| 3 |
+
size 165
|