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 605000; complete resumable checkpoint
Browse files- complete.json +8 -8
- coverage.json +45 -45
- latest.json +1 -1
- model.safetensors +1 -1
- performance.json +49 -50
- shard_scheduler.json +40 -160
- trainer.pt +2 -2
- training_config.json +11 -4
complete.json
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latest.json
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model.safetensors
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performance.json
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"estimated_update_seconds": 1.
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| 102 |
-
"input_tokens_per_second":
|
| 103 |
},
|
| 104 |
{
|
| 105 |
"microbatch": 16,
|
| 106 |
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| 107 |
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"microbatch_seconds": 0.
|
| 108 |
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| 109 |
"fits_budget": true,
|
| 110 |
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|
| 111 |
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"input_tokens_per_second":
|
| 112 |
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|
| 113 |
{
|
| 114 |
"microbatch": 32,
|
| 115 |
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| 116 |
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"microbatch_seconds": 0.
|
| 117 |
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|
| 118 |
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|
| 119 |
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| 120 |
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"input_tokens_per_second":
|
| 121 |
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|
| 122 |
{
|
| 123 |
"microbatch": 48,
|
| 124 |
"mode": "compiled",
|
| 125 |
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"microbatch_seconds": 0.
|
| 126 |
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|
| 127 |
"fits_budget": true,
|
| 128 |
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|
| 129 |
-
"input_tokens_per_second":
|
| 130 |
},
|
| 131 |
{
|
| 132 |
"microbatch": 64,
|
| 133 |
"mode": "compiled",
|
| 134 |
-
"microbatch_seconds": 0.
|
| 135 |
"peak_gb": 63.757601737976074,
|
| 136 |
"fits_budget": true,
|
| 137 |
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"estimated_update_seconds": 0.
|
| 138 |
-
"input_tokens_per_second":
|
| 139 |
},
|
| 140 |
{
|
| 141 |
"microbatch": 72,
|
| 142 |
"mode": "compiled",
|
| 143 |
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"microbatch_seconds": 0.
|
| 144 |
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| 145 |
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|
| 146 |
-
"estimated_update_seconds": 0.
|
| 147 |
-
"input_tokens_per_second":
|
| 148 |
},
|
| 149 |
{
|
| 150 |
"microbatch": 76,
|
| 151 |
"mode": "compiled",
|
| 152 |
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"microbatch_seconds": 0.
|
| 153 |
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|
| 154 |
"fits_budget": true,
|
| 155 |
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"estimated_update_seconds": 0.
|
| 156 |
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"input_tokens_per_second":
|
| 157 |
},
|
| 158 |
{
|
| 159 |
"microbatch": 80,
|
| 160 |
"mode": "compiled",
|
| 161 |
-
"microbatch_seconds": 0.
|
| 162 |
"peak_gb": 79.40062236785889,
|
| 163 |
"fits_budget": true,
|
| 164 |
-
"estimated_update_seconds": 0.
|
| 165 |
-
"input_tokens_per_second":
|
| 166 |
},
|
| 167 |
{
|
| 168 |
"microbatch": 86,
|
| 169 |
"mode": "compiled",
|
| 170 |
-
"microbatch_seconds": 0.
|
| 171 |
"peak_gb": 85.4687147140503,
|
| 172 |
"fits_budget": true,
|
| 173 |
-
"estimated_update_seconds": 0.
|
| 174 |
-
"input_tokens_per_second":
|
| 175 |
},
|
| 176 |
{
|
| 177 |
"microbatch": 90,
|
| 178 |
"mode": "compiled",
|
| 179 |
-
"microbatch_seconds": 0.
|
| 180 |
"peak_gb": 89.3772840499878,
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| 181 |
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| 182 |
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|
| 183 |
-
"input_tokens_per_second":
|
| 184 |
}
|
| 185 |
],
|
| 186 |
"failures": [
|
|
@@ -218,6 +218,5 @@
|
|
| 218 |
"selection_policy": "max_batch",
|
| 219 |
"budget_gb": 92.52170804977416,
|
| 220 |
"reserve_gb": 2.1791439056396484,
|
| 221 |
-
"note": "Forward/backward throughput; excludes optimizer, data, logging and checkpoint time."
|
| 222 |
-
"legacy_seed_verified": false
|
| 223 |
}
|
|
|
|
| 41 |
"attention_backend": "flash",
|
| 42 |
"vram_fraction": 0.98
|
| 43 |
},
|
| 44 |
+
"source": "d52752a64023cf29dc386d43835cf3173c5318ee093c3b375a812733c74023a1",
|
| 45 |
"requested_microbatch_ceiling": 90
|
| 46 |
},
|
| 47 |
+
"cache_reused": false,
|
| 48 |
"attention": "PyTorch FlashAttention (forced; forward/backward verified)",
|
| 49 |
"selected": {
|
| 50 |
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|
| 51 |
"mode": "compiled",
|
| 52 |
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"microbatch_seconds": 0.9299377820000245,
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| 53 |
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| 54 |
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|
| 55 |
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| 56 |
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"input_tokens_per_second": 198206.80863571487
|
| 57 |
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|
| 58 |
"measurements": [
|
| 59 |
{
|
| 60 |
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| 61 |
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|
| 62 |
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"microbatch_seconds": 0.15785124100000303,
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| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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| 68 |
{
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| 69 |
"microbatch": 16,
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| 70 |
"mode": "eager",
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| 71 |
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"microbatch_seconds": 0.3454984619999948,
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
{
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| 78 |
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| 79 |
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| 80 |
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| 85 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 101 |
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| 103 |
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| 104 |
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| 106 |
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| 113 |
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| 130 |
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| 132 |
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| 139 |
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| 140 |
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| 148 |
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| 149 |
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| 151 |
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| 157 |
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| 158 |
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| 159 |
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| 160 |
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| 161 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 175 |
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| 176 |
{
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| 177 |
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|
| 178 |
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|
| 179 |
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|
| 180 |
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| 181 |
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| 184 |
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| 185 |
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|
| 186 |
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|
|
|
| 218 |
"selection_policy": "max_batch",
|
| 219 |
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|
| 220 |
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|
| 221 |
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"note": "Forward/backward throughput; excludes optimizer, data, logging and checkpoint time."
|
|
|
|
| 222 |
}
|
shard_scheduler.json
CHANGED
|
@@ -22,8 +22,8 @@
|
|
| 22 |
"tokens_per_second": 598315.063954628,
|
| 23 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 24 |
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|
| 25 |
-
"required_token_positions_per_second":
|
| 26 |
-
"required_pages_per_second_upper_estimate": 0.
|
| 27 |
},
|
| 28 |
{
|
| 29 |
"source": "rewrite6",
|
|
@@ -36,8 +36,8 @@
|
|
| 36 |
"tokens_per_second": 8533.957213384612,
|
| 37 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 38 |
"estimated_pages_for_buffer": 1,
|
| 39 |
-
"required_token_positions_per_second":
|
| 40 |
-
"required_pages_per_second_upper_estimate": 0.
|
| 41 |
},
|
| 42 |
{
|
| 43 |
"source": "openmath",
|
|
@@ -50,8 +50,8 @@
|
|
| 50 |
"tokens_per_second": 380144.35761751566,
|
| 51 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 52 |
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|
| 53 |
-
"required_token_positions_per_second":
|
| 54 |
-
"required_pages_per_second_upper_estimate": 0.
|
| 55 |
},
|
| 56 |
{
|
| 57 |
"source": "ultra_qa",
|
|
@@ -64,8 +64,8 @@
|
|
| 64 |
"tokens_per_second": 43742.16556775435,
|
| 65 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 66 |
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|
| 67 |
-
"required_token_positions_per_second":
|
| 68 |
-
"required_pages_per_second_upper_estimate": 0.
|
| 69 |
},
|
| 70 |
{
|
| 71 |
"source": "ultra_style",
|
|
@@ -78,8 +78,8 @@
|
|
| 78 |
"tokens_per_second": 22018.86276523721,
|
| 79 |
"note": "Bounded fresh sample; full-page latency is an extrapolation.",
|
| 80 |
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|
| 81 |
-
"required_token_positions_per_second":
|
| 82 |
-
"required_pages_per_second_upper_estimate": 0.
|
| 83 |
},
|
| 84 |
{
|
| 85 |
"source": "cortex",
|
|
@@ -92,8 +92,8 @@
|
|
| 92 |
"tokens_per_second": 910315.184299418,
|
| 93 |
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|
| 94 |
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|
| 95 |
-
"required_token_positions_per_second":
|
| 96 |
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|
| 97 |
}
|
| 98 |
],
|
| 99 |
"gpu_measurement": "Selected forward/backward estimate; replaced by live successful update timings.",
|
|
@@ -158,182 +158,62 @@
|
|
| 158 |
"prefetch_batches": 63,
|
| 159 |
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|
| 160 |
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|
| 161 |
-
"producer_batch_seconds": 0.
|
| 162 |
-
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|
| 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 |
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|
| 170 |
"shard_target_mib": 16,
|
| 171 |
-
"upload_pages_per_commit":
|
| 172 |
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|
| 173 |
-
"generation_bytes_per_second":
|
| 174 |
-
"upload_bytes_per_second":
|
| 175 |
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|
| 176 |
"logical_page_rows": 4096,
|
| 177 |
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|
| 178 |
-
"estimated_full_shards_per_hour":
|
| 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": [
|
| 182 |
{
|
| 183 |
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|
| 184 |
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"bytes":
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| 185 |
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| 186 |
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|
| 187 |
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|
| 188 |
{
|
| 189 |
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"seconds": 1.
|
| 190 |
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| 191 |
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| 192 |
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| 193 |
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},
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| 194 |
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{
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| 195 |
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| 199 |
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},
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| 200 |
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| 201 |
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| 202 |
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| 204 |
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| 205 |
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},
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| 206 |
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{
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| 207 |
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| 208 |
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| 213 |
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| 214 |
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| 217 |
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| 219 |
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| 223 |
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| 225 |
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| 226 |
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| 229 |
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| 231 |
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| 232 |
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| 234 |
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| 235 |
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| 236 |
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| 237 |
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| 241 |
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| 247 |
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| 248 |
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| 249 |
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|
| 252 |
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| 253 |
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},
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| 254 |
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| 255 |
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| 256 |
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| 258 |
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| 259 |
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},
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| 260 |
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| 295 |
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"seconds": 1.
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| 298 |
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"bytes":
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| 299 |
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| 300 |
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| 301 |
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"seconds": 1.
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| 304 |
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| 309 |
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| 311 |
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"shards": 2
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| 337 |
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| 338 |
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| 339 |
"cache_key": {
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|
@@ -378,7 +258,7 @@
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|
| 378 |
},
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| 379 |
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| 380 |
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| 381 |
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| 382 |
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| 383 |
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| 384 |
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| 22 |
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"source": "openmath",
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| 78 |
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| 92 |
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| 158 |
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| 197 |
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| 199 |
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| 217 |
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| 219 |
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| 258 |
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| 259 |
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trainer.pt
CHANGED
|
@@ -1,3 +1,3 @@
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|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
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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 sha256:0d3af9a8da6b443eadd810e3c1064dd4eef75f364569d9e4096a54e70b5227d9
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| 3 |
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size 721766335
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training_config.json
CHANGED
|
@@ -32,11 +32,18 @@
|
|
| 32 |
"openmath": 10,
|
| 33 |
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|
| 34 |
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|
| 35 |
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"lr": 0.
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|
|
|
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|
| 36 |
"warmup_steps": 1000,
|
| 37 |
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"
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| 38 |
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|
| 39 |
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"
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|
| 40 |
"grad_clip": 1.0,
|
| 41 |
"seed": 20260925,
|
| 42 |
"save_every": 5000,
|
|
@@ -71,7 +78,7 @@
|
|
| 71 |
"hub_resume": true,
|
| 72 |
"work_dir": "/marimo/qwen90m_workspace/runs/void-byte-v1",
|
| 73 |
"data_recipe": "c0b0679d54277afcf4c85726fd0998265697a759188400782be4a859641ba658",
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| 74 |
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"trainer_recipe": "
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| 75 |
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| 76 |
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|
| 77 |
}
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|
| 32 |
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|
| 33 |
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|
| 34 |
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| 35 |
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"lr": 0.0001,
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| 36 |
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"weight_decay": 0.1,
|
| 37 |
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"lr_schedule": "auto",
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| 38 |
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|
| 39 |
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| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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"optimization_file": "optimization.json",
|
| 47 |
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|
| 48 |
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| 49 |
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|
| 78 |
"hub_resume": true,
|
| 79 |
"work_dir": "/marimo/qwen90m_workspace/runs/void-byte-v1",
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| 80 |
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"trainer_recipe": "cada09ddc22837a37a731a733b4db79f20cd0d25748c84f9e80a63b31e4a73d1",
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|
| 84 |
}
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