Brother, I want you to get the QAN 3.0.6p locally, and I want you to test the maximum accuracy we can get on this GPU. I want you to go ahead and go all in and check.And you fucking don't care about any other files in this repo in this GPU. You fucking only create one folder, quench 0.6b, and only focus on that. You fucking don't know anything other than that.


The user wants me to get "QAN 3.0.6p" locally and test maximum accuracy on the GPU. Let me figure out what "QAN 3.0.6p" refers to - this likely refers to a model or framework. Let me search for it and understand what they're asking for.

Actually, "QAN 3.0.6p" - I'm not immediately sure what this refers to. Let me think... It could be "Qwen" model - specifically Qwen 3 0.6B parameter model. "QAN 3.0.6p" → Qwen3-0.6B perhaps? And "quench 0.6b" folder name they mentioned.

So they want me to set up a folder for the Qwen 3 0.6B model, check what GPU resources are available, download the model, and then run accuracy tests on it.


Alright, let me check the GPU first and set everything up. I'll focus exclusively on getting Qwen3-0.6B running and pushing max accuracy.


ToolSearch

Question: select:Bash


Tool loaded.

Question: select:Bash


Let me check the GPU and set up the environment.


Bash

Check GPU info


python3 --version && pip3 --version 2>/dev/null; which conda 2>/dev/null; which pip 2>/dev/null

Check Python and package manager versions


Sat Mar 21 10:34:08 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.05              Driver Version: 560.35.05      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A100-SXM4-80GB          On  |   00000000:01:00.0 Off |                    0 |
| N/A   25C    P0             49W /  400W |       1MiB /  81920MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+


Python 3.10.12
pip 26.0.1 from /home/ubuntu/.local/lib/python3.10/site-packages/pip (python 3.10)
/home/ubuntu/.local/bin/pip


A100 80GB - beast. Let me set up the folder and install everything needed.


Create the quench_0.6b folder


pip install torch transformers accelerate datasets evaluate lm-eval 2>&1 | tail -20

Install core ML dependencies


solospeech 0.1.0 requires diffusers==0.30, but you have diffusers 0.37.0 which is incompatible.
solospeech 0.1.0 requires librosa==0.9.2, but you have librosa 0.11.0 which is incompatible.
solospeech 0.1.0 requires speechbrain==1.0.2, but you have speechbrain 1.0.3 which is incompatible.
solospeech 0.1.0 requires torch==2.4.1, but you have torch 2.9.1 which is incompatible.
solospeech 0.1.0 requires torchaudio==2.4.1, but you have torchaudio 2.9.1+cu128 which is incompatible.
solospeech 0.1.0 requires torchvision==0.19.1, but you have torchvision 0.24.1+cu128 which is incompatible.
solospeech 0.1.0 requires wandb==0.19.1, but you have wandb 0.25.1 which is incompatible.
torch-tensorrt 2.10.0 requires torch<2.11.0,>=2.10.0, but you have torch 2.9.1 which is incompatible.
vibevoice 0.1.0 requires accelerate==1.6.0, but you have accelerate 1.12.0 which is incompatible.
vibevoice 0.1.0 requires datasets==3.5.0, but you have datasets 2.18.0 which is incompatible.
vibevoice 0.1.0 requires transformers==4.51.3, but you have transformers 4.57.6 which is incompatible.
vllm 0.15.1 requires numba==0.61.2, but you have numba 0.64.0 which is incompatible.
voxcpm 1.5.0 requires datasets<4,>=3, but you have datasets 2.18.0 which is incompatible.
voxcpm 1.5.0 requires modelscope>=1.22.0, but you have modelscope 1.17.1 which is incompatible.
xcodec2 0.1.5 requires einops==0.8.0, but you have einops 0.8.2 which is incompatible.
xcodec2 0.1.5 requires torch==2.5.0, but you have torch 2.9.1 which is incompatible.
xcodec2 0.1.5 requires torchaudio==2.5.0, but you have torchaudio 2.9.1+cu128 which is incompatible.
xformers 0.0.35 requires torch>=2.10, but you have torch 2.9.1 which is incompatible.
WARNING: Ignoring invalid distribution -riton (/home/ubuntu/.local/lib/python3.10/site-packages)
Successfully installed DataProperty-1.1.0 evaluate-0.4.6 fsspec-2024.2.0 lm-eval-0.4.11 mbstrdecoder-1.1.4 pathvalidate-3.3.1 pytablewriter-1.2.1 rouge-score-0.1.2 sacrebleu-2.6.0 sqlitedict-2.1.0 tabledata-1.3.4 tcolorpy-0.1.7 typepy-1.3.4 word2number-1.1


Good, dependencies are in. Now let me check what `lm-eval` benchmarks are available and run Qwen3-0.6B at full precision (no quantization) on the A100 to get maximum accuracy.


python3 -c "import torch; print(f'CUDA: {torch.cuda.is_available()}, Device: {torch.cuda.get_device_name(0)}, BF16: {torch.cuda.is_bf16_supported()}')"

Verify CUDA and bf16 support


CUDA: True, Device: NVIDIA A100-SXM4-80GB, BF16: True


BF16 supported. Now let me download Qwen3-0.6B and run comprehensive benchmarks. I'll write a script that tests max accuracy across key benchmarks.


ToolSearch

Question: select:Write


Tool loaded.

Question: select:Write


Let me first check if Qwen3-0.6B exists on HuggingFace and what the exact model name is, then write a comprehensive benchmark script.


Bash

python3 -c "from transformers import AutoModelForCausalLM, AutoTokenizer; print('Checking Qwen3-0.6B...'); t = AutoTokenizer.from_pretrained('Qwen/Qwen3-0.6B', trust_remote_code=True); print('Tokenizer loaded'); m = AutoModelForCausalLM.from_pretrained('Qwen/Qwen3-0.6B', torch_dtype='auto', device_map='auto', trust_remote_code=True); print(f'Model loaded. dtype: {m.dtype}, params: {sum(p.numel() for p in m.parameters())/1e6:.1f}M')"

Download and verify Qwen3-0.6B model


Output: `torch_dtype` is deprecated! Use `dtype` instead!

`torch_dtype` is deprecated! Use `dtype` instead!


Output: `torch_dtype` is deprecated! Use `dtype` instead!


Output: `torch_dtype` is deprecated! Use `dtype` instead!


Output: `torch_dtype` is deprecated! Use `dtype` instead!


Output:
`torch_dtype` is deprecated! Use `dtype` instead!
Checking Qwen3-0.6B...
Tokenizer loaded
Model loaded. dtype: torch.bfloat16, params: 596.0M

`torch_dtype` is deprecated! Use `dtype` instead!
Checking Qwen3-0.6B...
Tokenizer loaded
Model loaded. dtype: torch.bfloat16, params: 596.0M


`torch_dtype` is deprecated! Use `dtype` instead!
Checking Qwen3-0.6B...
Tokenizer loaded
Model loaded. dtype: torch.bfloat16, params: 596.0M


Model downloaded and loaded - 596M params in BF16. Now let me run the full lm-eval harness with the standard benchmarks to get max accuracy numbers.


Write

#!/bin/bash
# Qwen3-0.6B Maximum Accuracy Benchmark Suite
# GPU: A100-80GB | dtype: bfloat16 (native, no quantization)
# Using lm-eval harness for reproducible results

MODEL="Qwen/Qwen3-0.6B"
OUTPUT_DIR="/home/ubuntu/quench_0.6b/results"
mkdir -p "$OUTPUT_DIR"

echo "============================================"
echo "  Qwen3-0.6B Full Accuracy Benchmark"
echo "  GPU: $(nvidia-smi --query-gpu=name --format=csv,noheader)"
echo "  dtype: bfloat16 (full precision, no quant)"
echo "============================================"

# Run comprehensive benchmarks - all major ones
# Using batch_size=auto for maximum throughput
lm_eval --model hf \
  --model_args "pretrained=${MODEL},dtype=bfloat16,trust_remote_code=True" \
  --tasks hellaswag,arc_easy,arc_challenge,winogrande,piqa,mmlu,truthfulqa_mc2,gsm8k \
  --batch_size auto \
  --num_fewshot 0 \
  --output_path "$OUTPUT_DIR" \
  --log_samples 2>&1 | tee "$OUTPUT_DIR/benchmark_log.txt"

echo ""
echo "============================================"
echo "  Benchmark Complete - Results saved to:"
echo "  $OUTPUT_DIR"
echo "============================================"


File created successfully at: /home/ubuntu/quench_0.6b/run_benchmarks.sh

#!/bin/bash
# Qwen3-0.6B Maximum Accuracy Benchmark Suite
# GPU: A100-80GB | dtype: bfloat16 (native, no quantization)
# Using lm-eval harness for reproducible results

MODEL="Qwen/Qwen3-0.6B"
OUTPUT_DIR="/home/ubuntu/quench_0.6b/results"
mkdir -p "$OUTPUT_DIR"

echo "============================================"
echo "  Qwen3-0.6B Full Accuracy Benchmark"
echo "  GPU: $(nvidia-smi --query-gpu=name --format=csv,noheader)"
echo "  dtype: bfloat16 (full precision, no quant)"
echo "============================================"

# Run comprehensive benchmarks - all major ones
# Using batch_size=auto for maximum throughput
lm_eval --model hf \
  --model_args "pretrained=${MODEL},dtype=bfloat16,trust_remote_code=True" \
  --tasks hellaswag,arc_easy,arc_challenge,winogrande,piqa,mmlu,truthfulqa_mc2,gsm8k \
  --batch_size auto \
  --num_fewshot 0 \
  --output_path "$OUTPUT_DIR" \
  --log_samples 2>&1 | tee "$OUTPUT_DIR/benchmark_log.txt"

echo ""
echo "============================================"
echo "  Benchmark Complete - Results saved to:"
echo "  $OUTPUT_DIR"
echo "============================================"


Bash

chmod +x /home/ubuntu/quench_0.6b/run_benchmarks.sh && bash /home/ubuntu/quench_0.6b/run_benchmarks.sh

Run full benchmark suite for Qwen3-0.6B on A100


Command running in background with ID: bc6z1aaiw. Output is being written to: /tmp/claude-1001/-home-ubuntu/tasks/bc6z1aaiw.output


Benchmarks are running in the background. Let me check progress periodically.


sleep 30 && tail -30 /tmp/claude-1001/-home-ubuntu/tasks/bc6z1aaiw.output

Check benchmark progress after 30s


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Generating validation split: 100%|██████████| 299/299 [00:00<00:00, 171488.70 examples/s]

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Generating train split: 100%|██████████| 40398/40398 [00:00<00:00, 1264347.22 examples/s]

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Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 16113 examples [00:00, 871946.38 examples/s]

Generating validation split: 0 examples [00:00, ? examples/s]
Generating validation split: 1838 examples [00:00, 654203.22 examples/s]

Generating test split: 0 examples [00:00, ? examples/s]
Generating test split: 3084 examples [00:00, 676422.82 examples/s]

Downloading readme: 0.00B [00:00, ?B/s]
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Datasets downloading. Let me check again in a bit.


sleep 120 && tail -40 /tmp/claude-1001/-home-ubuntu/tasks/bc6z1aaiw.output

Check benchmark progress after 2 more minutes


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2026-03-21:10:37:31 INFO     [api.task:311] Building contexts for piqa on rank 0...

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2026-03-21:10:37:32 INFO     [api.task:311] Building contexts for winogrande on rank 0...

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2026-03-21:10:37:32 INFO     [api.task:311] Building contexts for arc_challenge on rank 0...

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2026-03-21:10:37:34 INFO     [api.task:311] Building contexts for arc_easy on rank 0...

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2026-03-21:10:37:38 INFO     [api.task:311] Building contexts for hellaswag on rank 0...

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2026-03-21:10:37:44 INFO     [evaluator:584] Running generate_until requests

Running generate_until requests:   0%|          | 0/1319 [00:00<?, ?it/s]


Running well - it's processing the benchmarks now. The generate tasks (GSM8K, TruthfulQA) are running. Let me wait for more progress.


sleep 180 && tail -50 /tmp/claude-1001/-home-ubuntu/tasks/bc6z1aaiw.output

Check benchmark progress after 3 more minutes


2026-03-21:10:37:23 INFO     [api.task:311] Building contexts for mmlu_human_sexuality on rank 0...

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2026-03-21:10:37:23 INFO     [api.task:311] Building contexts for mmlu_professional_psychology on rank 0...

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2026-03-21:10:37:24 INFO     [api.task:311] Building contexts for mmlu_public_relations on rank 0...

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2026-03-21:10:37:24 INFO     [api.task:311] Building contexts for mmlu_security_studies on rank 0...

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2026-03-21:10:37:24 INFO     [api.task:311] Building contexts for mmlu_sociology on rank 0...

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2026-03-21:10:37:25 INFO     [api.task:311] Building contexts for mmlu_us_foreign_policy on rank 0...

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2026-03-21:10:37:25 INFO     [api.task:311] Building contexts for mmlu_formal_logic on rank 0...

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2026-03-21:10:37:25 INFO     [api.task:311] Building contexts for mmlu_high_school_european_history on rank 0...

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2026-03-21:10:37:25 INFO     [api.task:311] Building contexts for mmlu_high_school_us_history on rank 0...

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2026-03-21:10:37:25 INFO     [api.task:311] Building contexts for mmlu_high_school_world_history on rank 0...

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2026-03-21:10:37:26 INFO     [api.task:311] Building contexts for mmlu_international_law on rank 0...

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2026-03-21:10:37:26 INFO     [api.task:311] Building contexts for mmlu_jurisprudence on rank 0...

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2026-03-21:10:37:26 INFO     [api.task:311] Building contexts for mmlu_logical_fallacies on rank 0...

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2026-03-21:10:37:26 INFO     [api.task:311] Building contexts for mmlu_moral_disputes on rank 0...

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2026-03-21:10:37:27 INFO     [api.task:311] Building contexts for mmlu_moral_scenarios on rank 0...

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2026-03-21:10:37:28 INFO     [api.task:311] Building contexts for mmlu_philosophy on rank 0...

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2026-03-21:10:37:28 INFO     [api.task:311] Building contexts for mmlu_prehistory on rank 0...

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2026-03-21:10:37:28 INFO     [api.task:311] Building contexts for mmlu_professional_law on rank 0...

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2026-03-21:10:37:30 INFO     [api.task:311] Building contexts for mmlu_world_religions on rank 0...

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2026-03-21:10:37:31 INFO     [api.task:311] Building contexts for piqa on rank 0...

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2026-03-21:10:37:32 INFO     [api.task:311] Building contexts for winogrande on rank 0...

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2026-03-21:10:37:32 INFO     [api.task:311] Building contexts for arc_challenge on rank 0...

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2026-03-21:10:37:34 INFO     [api.task:311] Building contexts for arc_easy on rank 0...

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2026-03-21:10:37:38 INFO     [api.task:311] Building contexts for hellaswag on rank 0...

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2026-03-21:10:37:44 INFO     [evaluator:584] Running generate_until requests

Running generate_until requests:   0%|          | 0/1319 [00:00<?, ?it/s]


It's on the generate_until step (GSM8K/TruthfulQA generation). This can take a while with the full dataset. Let me wait longer.


sleep 300 && tail -30 /tmp/claude-1001/-home-ubuntu/tasks/bc6z1aaiw.output

Check benchmark progress after 5 more minutes


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2026-03-21:10:37:26 INFO     [api.task:311] Building contexts for mmlu_jurisprudence on rank 0...

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2026-03-21:10:37:26 INFO     [api.task:311] Building contexts for mmlu_logical_fallacies on rank 0...

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2026-03-21:10:37:26 INFO     [api.task:311] Building contexts for mmlu_moral_disputes on rank 0...

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2026-03-21:10:37:27 INFO     [api.task:311] Building contexts for mmlu_moral_scenarios on rank 0...

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2026-03-21:10:37:28 INFO     [api.task:311] Building contexts for mmlu_philosophy on rank 0...

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2026-03-21:10:37:28 INFO     [api.task:311] Building contexts for mmlu_prehistory on rank 0...

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2026-03-21:10:37:28 INFO     [api.task:311] Building contexts for mmlu_professional_law on rank 0...

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2026-03-21:10:37:30 INFO     [api.task:311] Building contexts for mmlu_world_religions on rank 0...

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2026-03-21:10:37:31 INFO     [api.task:311] Building contexts for piqa on rank 0...

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2026-03-21:10:37:32 INFO     [api.task:311] Building contexts for winogrande on rank 0...

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2026-03-21:10:37:32 INFO     [api.task:311] Building contexts for arc_challenge on rank 0...

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2026-03-21:10:37:34 INFO     [api.task:311] Building contexts for arc_easy on rank 0...

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2026-03-21:10:37:38 INFO     [api.task:311] Building contexts for hellaswag on rank 0...

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2026-03-21:10:37:44 INFO     [evaluator:584] Running generate_until requests

Running generate_until requests:   0%|          | 0/1319 [00:00<?, ?it/s]The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.

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The generate_until step is very slow - about 7-8 seconds per iteration with 1319 total. That's ~2.5-3 hours for just this step. This is because GSM8K requires generating full solutions. Let me check if there's a way to speed this up or just wait for it.

Actually, this is going to take a very long time. The generate_until requests for GSM8K are slow because each one needs to generate a full chain-of-thought answer. At ~7.5s per sample and 1319 samples, that's about 2.75 hours just for generation.

Let me check if this is still running and wait for completion, or check the output file size to see progress.


The generation step is running at ~7-8s per sample (1319 total GSM8K samples). This will take a while. Let me check the progress at regular intervals.


sleep 600 && tail -5 /tmp/claude-1001/-home-ubuntu/tasks/bc6z1aaiw.output

Check benchmark progress after 10 more minutes


2026-03-21:10:37:38 INFO     [api.task:311] Building contexts for hellaswag on rank 0...

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2026-03-21:10:37:44 INFO     [evaluator:584] Running generate_until requests

Running generate_until requests:   0%|          | 0/1319 [00:00<?, ?it/s]The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.

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Running generate_until requests:   3%|▎         | 42/1319 [08:24<2:02:24,  5.75s/it]
Running generate_until requests:   3%|▎         | 43/1319 [08:32<2:15:30,  6.37s/it]
Running generate_until requests:   3%|▎         | 44/1319 [08:32<1:36:08,  4.52s/it]
Running generate_until requests:   3%|▎         | 45/1319 [08:40<1:56:50,  5.50s/it]
Running generate_until requests:   3%|▎         | 46/1319 [08:47<2:11:08,  6.18s/it]
Running generate_until requests:   4%|▎         | 47/1319 [08:55<2:21:13,  6.66s/it]
Running generate_until requests:   4%|▎         | 48/1319 [09:03<2:28:15,  7.00s/it]
Running generate_until requests:   4%|▎         | 49/1319 [09:03<1:45:04,  4.96s/it]
Running generate_until requests:   4%|▍         | 50/1319 [09:11<2:02:51,  5.81s/it]
Running generate_until requests:   4%|▍         | 51/1319 [09:19<2:15:17,  6.40s/it]
Running generate_until requests:   4%|▍         | 52/1319 [09:27<2:23:48,  6.81s/it]
Running generate_until requests:   4%|▍         | 53/1319 [09:34<2:29:48,  7.10s/it]
Running generate_until requests:   4%|▍         | 54/1319 [09:42<2:33:59,  7.30s/it]
Running generate_until requests:   4%|▍         | 55/1319 [09:50<2:37:03,  7.46s/it]
Running generate_until requests:   4%|▍         | 56/1319 [09:50<1:51:01,  5.27s/it]
Running generate_until requests:   4%|▍         | 57/1319 [09:58<2:06:59,  6.04s/it]
Running generate_until requests:   4%|▍         | 58/1319 [10:06<2:18:15,  6.58s/it]
Running generate_until requests:   4%|▍         | 59/1319 [10:14<2:25:57,  6.95s/it]
Running generate_until requests:   5%|▍         | 60/1319 [10:21<2:31:11,  7.21s/it]
Running generate_until requests:   5%|▍         | 61/1319 [10:29<2:34:45,  7.38s/it]
Running generate_until requests:   5%|▍         | 62/1319 [10:37<2:37:14,  7.51s/it]
Running generate_until requests:   5%|▍         | 63/1319 [10:45<2:38:55,  7.59s/it]
Running generate_until requests:   5%|▍         | 64/1319 [10:52<2:39:59,  7.65s/it]
Running generate_until requests:   5%|▍         | 65/1319 [10:53<1:52:52,  5.40s/it]
Running generate_until requests:   5%|▌         | 66/1319 [11:00<2:07:42,  6.11s/it]
Running generate_until requests:   5%|▌         | 67/1319 [11:01<1:30:39,  4.34s/it]
Running generate_until requests:   5%|▌         | 68/1319 [11:08<1:52:06,  5.38s/it]
Running generate_until requests:   5%|▌         | 69/1319 [11:16<2:07:01,  6.10s/it]
Running generate_until requests:   5%|▌         | 70/1319 [11:16<1:30:11,  4.33s/it]
Running generate_until requests:   5%|▌         | 71/1319 [11:24<1:51:43,  5.37s/it]
Running generate_until requests:   5%|▌         | 72/1319 [11:32<2:06:44,  6.10s/it]
Running generate_until requests:   6%|▌         | 73/1319 [11:40<2:17:20,  6.61s/it]
Running generate_until requests:   6%|▌         | 74/1319 [11:48<2:24:42,  6.97s/it]
Running generate_until requests:   6%|▌         | 75/1319 [11:55<2:29:47,  7.22s/it]
Running generate_until requests:   6%|▌         | 76/1319 [11:56<1:46:18,  5.13s/it]
Running generate_until requests:   6%|▌         | 77/1319 [12:04<2:02:48,  5.93s/it]
Running generate_until requests:   6%|▌         | 78/1319 [12:11<2:14:11,  6.49s/it]
Running generate_until requests:   6%|▌         | 79/1319 [12:19<2:22:14,  6.88s/it]
Running generate_until requests:   6%|▌         | 80/1319 [12:27<2:27:47,  7.16s/it]
Running generate_until requests:   6%|▌         | 81/1319 [12:35<2:31:43,  7.35s/it]
Running generate_until requests:   6%|▌         | 82/1319 [12:43<2:34:31,  7.50s/it]
Running generate_until requests:   6%|▋         | 83/1319 [12:43<1:49:25,  5.31s/it]
Running generate_until requests:   6%|▋         | 84/1319 [12:43<1:17:51,  3.78s/it]
Running generate_until requests:   6%|▋         | 85/1319 [12:51<1:42:38,  4.99s/it]
Running generate_until requests:   7%|▋         | 86/1319 [12:59<1:59:52,  5.83s/it]
Running generate_until requests:   7%|▋         | 87/1319 [13:06<2:11:50,  6.42s/it]
Running generate_until requests:   7%|▋         | 88/1319 [13:14<2:20:07,  6.83s/it]
Running generate_until requests:   7%|▋         | 89/1319 [13:22<2:25:51,  7.12s/it]
Running generate_until requests:   7%|▋         | 90/1319 [13:30<2:29:47,  7.31s/it]
Running generate_until requests:   7%|▋         | 91/1319 [13:37<2:32:34,  7.46s/it]
Running generate_until requests:   7%|▋         | 92/1319 [13:45<2:34:24,  7.55s/it]
Running generate_until requests:   7%|▋         | 93/1319 [13:53<2:35:34,  7.61s/it]
Running generate_until requests:   7%|▋         | 94/1319 [13:53<1:50:18,  5.40s/it]
Running generate_until requests:   7%|▋         | 95/1319 [14:01<2:04:51,  6.12s/it]
Running generate_until requests:   7%|▋         | 96/1319 [14:09<2:15:02,  6.62s/it]
Running generate_until requests:   7%|▋         | 97/1319 [14:09<1:35:34,  4.69s/it]
Running generate_until requests:   7%|▋         | 98/1319 [14:09<1:07:58,  3.34s/it]
Running generate_until requests:   8%|▊         | 99/1319 [14:09<48:50,  2.40s/it]
Running generate_until requests:   8%|▊         | 100/1319 [14:17<1:21:39,  4.02s/it]
Running generate_until requests:   8%|▊         | 101/1319 [14:25<1:44:32,  5.15s/it]
Running generate_until requests:   8%|▊         | 102/1319 [14:33<2:00:15,  5.93s/it]
Running generate_until requests:   8%|▊         | 103/1319 [14:41<2:11:23,  6.48s/it]
Running generate_until requests:   8%|▊         | 104/1319 [14:48<2:19:03,  6.87s/it]
Running generate_until requests:   8%|▊         | 105/1319 [14:56<2:24:18,  7.13s/it]
Running generate_until requests:   8%|▊         | 106/1319 [14:56<1:42:02,  5.05s/it]
Running generate_until requests:   8%|▊         | 107/1319 [15:04<1:58:28,  5.86s/it]
Running generate_until requests:   8%|▊         | 108/1319 [15:04<1:24:31,  4.19s/it]
Running generate_until requests:   8%|▊         | 109/1319 [15:05<1:00:35,  3.00s/it]
Running generate_until requests:   8%|▊         | 110/1319 [15:12<1:29:20,  4.43s/it]
Running generate_until requests:   8%|▊         | 111/1319 [15:13<1:03:46,  3.17s/it]
Running generate_until requests:   8%|▊         | 112/1319 [15:20<1:31:27,  4.55s/it]
Running generate_until requests:   9%|▊         | 113/1319 [15:28<1:50:52,  5.52s/it]
Running generate_until requests:   9%|▊         | 114/1319 [15:36<2:04:29,  6.20s/it]
Running generate_until requests:   9%|▊         | 115/1319 [15:36<1:28:22,  4.40s/it]
Running generate_until requests:   9%|▉         | 116/1319 [15:44<1:48:32,  5.41s/it]
Running generate_until requests:   9%|▉         | 117/1319 [15:52<2:02:47,  6.13s/it]
Running generate_until requests:   9%|▉         | 118/1319 [15:59<2:12:30,  6.62s/it]
Running generate_until requests:   9%|▉         | 119/1319 [16:00<1:34:09,  4.71s/it]
Running generate_until requests:   9%|▉         | 120/1319 [16:07<1:52:27,  5.63s/it]
Running generate_until requests:   9%|▉         | 121/1319 [16:15<2:05:30,  6.29s/it]
Running generate_until requests:   9%|▉         | 122/1319 [16:16<1:29:25,  4.48s/it]
Running generate_until requests:   9%|▉         | 123/1319 [16:23<1:49:06,  5.47s/it]
Running generate_until requests:   9%|▉         | 124/1319 [16:31<2:02:48,  6.17s/it]
Running generate_until requests:   9%|▉         | 125/1319 [16:39<2:12:17,  6.65s/it]
Running generate_until requests:  10%|▉         | 126/1319 [16:42<1:50:37,  5.56s/it]
Running generate_until requests:  10%|▉         | 127/1319 [16:50<2:03:44,  6.23s/it]
Running generate_until requests:  10%|▉         | 128/1319 [16:50<1:27:39,  4.42s/it]
Running generate_until requests:  10%|▉         | 129/1319 [16:50<1:02:35,  3.16s/it]
Running generate_until requests:  10%|▉         | 130/1319 [16:58<1:29:53,  4.54s/it]
Running generate_until requests:  10%|▉         | 131/1319 [17:06<1:49:03,  5.51s/it]
Running generate_until requests:  10%|█         | 132/1319 [17:13<2:02:26,  6.19s/it]
Running generate_until requests:  10%|█         | 133/1319 [17:14<1:27:05,  4.41s/it]
Running generate_until requests:  10%|█         | 134/1319 [17:14<1:01:49,  3.13s/it]
Running generate_until requests:  10%|█         | 135/1319 [17:14<44:41,  2.26s/it]
Running generate_until requests:  10%|█         | 136/1319 [17:14<32:31,  1.65s/it]
Running generate_until requests:  10%|█         | 137/1319 [17:22<1:08:45,  3.49s/it]
Running generate_until requests:  10%|█         | 138/1319 [17:30<1:34:01,  4.78s/it]
Running generate_until requests:  11%|█         | 139/1319 [17:38<1:51:43,  5.68s/it]
Running generate_until requests:  11%|█         | 140/1319 [17:45<2:04:04,  6.31s/it]
Running generate_until requests:  11%|█         | 141/1319 [17:53<2:12:41,  6.76s/it]
Running generate_until requests:  11%|█         | 142/1319 [18:01<2:18:41,  7.07s/it]
Running generate_until requests:  11%|█         | 143/1319 [18:09<2:22:56,  7.29s/it]
Running generate_until requests:  11%|█         | 144/1319 [18:17<2:25:45,  7.44s/it]
Running generate_until requests:  11%|█         | 145/1319 [18:24<2:27:46,  7.55s/it]


At ~7.5s/sample, ~145/1319 done after ~18 min. The generate step will take about 2.5 hours total. Let me wait for the full completion.


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Question: select:TaskOutput


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Question: select:TaskOutput


Let me wait for the task to complete. It's going to take a while - about 2.5 hours for the generate step. Let me check periodically.