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ModelHub XC a8a4f62dba 初始化项目,由ModelHub XC社区提供模型
Model: rishabsponge/qwen-countdown-h100-hillclimb
Source: Original Platform
2026-08-16 20:35:19 +08:00

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[2026-07-15 00:25:12,786] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
INFO 07-15 00:25:15 __init__.py:183] Automatically detected platform cuda.
2026-07-15 00:25:15,797 | INFO | CUDA preflight passed with training GPU=NVIDIA H100 80GB HBM3 and vLLM GPU=NVIDIA H100 80GB HBM3
2026-07-15 00:25:15,973 | INFO | No checkpoint selected; starting a new training run
2026-07-15 00:25:15,974 | INFO | Starting h100-full profile with model=Qwen/Qwen2.5-3B-Instruct, dataset_size=50000, max_steps=450, max_completion_length=1024
2026-07-15 00:25:15,974 | INFO | Loading dataset Jiayi-Pan/Countdown-Tasks-3to4
2026-07-15 00:25:17,130 | INFO | Selected 50,000 examples
2026-07-15 00:25:17,131 | INFO | Loading tokenizer Qwen/Qwen2.5-3B-Instruct
2026-07-15 00:25:17,622 | INFO | Formatting prompts
2026-07-15 00:25:17,724 | INFO | Prepared 45,000 training examples and 5,000 test examples
2026-07-15 00:25:17,727 | INFO | Writing generated completions in outputs/qwen-countdown-h100-hillclimb/completions.jsonl
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
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INFO 07-15 00:25:32 config.py:520] This model supports multiple tasks: {'generate', 'reward', 'score', 'embed', 'classify'}. Defaulting to 'generate'.
INFO 07-15 00:25:32 llm_engine.py:232] Initializing an LLM engine (v0.7.0) with config: model='Qwen/Qwen2.5-3B-Instruct', speculative_config=None, tokenizer='Qwen/Qwen2.5-3B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda:1, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=Qwen/Qwen2.5-3B-Instruct, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=True, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":256}, use_cached_outputs=False,
INFO 07-15 00:25:34 cuda.py:225] Using Flash Attention backend.
INFO 07-15 00:25:34 model_runner.py:1110] Starting to load model Qwen/Qwen2.5-3B-Instruct...
INFO 07-15 00:25:35 weight_utils.py:251] Using model weights format ['*.safetensors']
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INFO 07-15 00:25:38 model_runner.py:1115] Loading model weights took 0.0000 GB
INFO 07-15 00:25:39 worker.py:266] Memory profiling takes 0.74 seconds
INFO 07-15 00:25:39 worker.py:266] the current vLLM instance can use total_gpu_memory (79.18GiB) x gpu_memory_utilization (0.50) = 39.59GiB
INFO 07-15 00:25:39 worker.py:266] model weights take 0.00GiB; non_torch_memory takes 0.00GiB; PyTorch activation peak memory takes 0.00GiB; the rest of the memory reserved for KV Cache is 39.59GiB.
INFO 07-15 00:25:39 executor_base.py:108] # CUDA blocks: 72070, # CPU blocks: 7281
INFO 07-15 00:25:39 executor_base.py:113] Maximum concurrency for 32768 tokens per request: 35.19x
INFO 07-15 00:25:41 model_runner.py:1430] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
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INFO 07-15 00:25:53 model_runner.py:1558] Graph capturing finished in 12 secs, took 0.00 GiB
INFO 07-15 00:25:53 llm_engine.py:429] init engine (profile, create kv cache, warmup model) took 15.29 seconds
wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from WANDB_API_KEY.
wandb: Currently logged in as: spongerish (spongerish-sponge) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.28.0
wandb: Run data is saved locally in /workspace/countdown/wandb/run-20260715_002558-1hjcn97o
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run h100-full-hillclimb-100
wandb: ⭐️ View project at https://wandb.ai/spongerish-sponge/countdown-grpo
wandb: 🚀 View run at https://wandb.ai/spongerish-sponge/countdown-grpo/runs/1hjcn97o
wandb: Detected [huggingface_hub.inference, openai] in use.
wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
wandb: For more information, check out the docs at: https://weave-docs.wandb.ai
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6%|▌ | 25/450 [45:58<13:10:24, 111.59s/it][2026-07-15 02:40:36,889] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
INFO 07-15 02:40:39 __init__.py:183] Automatically detected platform cuda.
2026-07-15 02:40:39,520 | INFO | CUDA preflight passed with training GPU=NVIDIA H100 80GB HBM3 and vLLM GPU=NVIDIA H100 80GB HBM3
2026-07-15 02:40:39,659 | INFO | Resuming training from outputs/qwen-countdown-h100-hillclimb/checkpoint-25
2026-07-15 02:40:39,659 | INFO | Starting h100-full profile with model=Qwen/Qwen2.5-3B-Instruct, dataset_size=50000, max_steps=450, max_completion_length=1024
2026-07-15 02:40:39,659 | INFO | Loading dataset Jiayi-Pan/Countdown-Tasks-3to4
2026-07-15 02:40:40,976 | INFO | Selected 50,000 examples
2026-07-15 02:40:40,976 | INFO | Loading tokenizer Qwen/Qwen2.5-3B-Instruct
2026-07-15 02:40:41,422 | INFO | Formatting prompts
2026-07-15 02:40:41,637 | INFO | Prepared 45,000 training examples and 5,000 test examples
2026-07-15 02:40:41,638 | INFO | Appending generated completions in outputs/qwen-countdown-h100-hillclimb/completions.jsonl
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
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INFO 07-15 02:41:22 config.py:520] This model supports multiple tasks: {'classify', 'score', 'embed', 'generate', 'reward'}. Defaulting to 'generate'.
INFO 07-15 02:41:22 llm_engine.py:232] Initializing an LLM engine (v0.7.0) with config: model='Qwen/Qwen2.5-3B-Instruct', speculative_config=None, tokenizer='Qwen/Qwen2.5-3B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda:1, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=Qwen/Qwen2.5-3B-Instruct, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=True, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":256}, use_cached_outputs=False,
INFO 07-15 02:41:23 cuda.py:225] Using Flash Attention backend.
INFO 07-15 02:41:23 model_runner.py:1110] Starting to load model Qwen/Qwen2.5-3B-Instruct...
INFO 07-15 02:41:23 weight_utils.py:251] Using model weights format ['*.safetensors']
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INFO 07-15 02:41:25 model_runner.py:1115] Loading model weights took 0.0000 GB
INFO 07-15 02:41:26 worker.py:266] Memory profiling takes 0.75 seconds
INFO 07-15 02:41:26 worker.py:266] the current vLLM instance can use total_gpu_memory (79.18GiB) x gpu_memory_utilization (0.50) = 39.59GiB
INFO 07-15 02:41:26 worker.py:266] model weights take 0.00GiB; non_torch_memory takes 0.00GiB; PyTorch activation peak memory takes 0.00GiB; the rest of the memory reserved for KV Cache is 39.59GiB.
INFO 07-15 02:41:26 executor_base.py:108] # CUDA blocks: 72070, # CPU blocks: 7281
INFO 07-15 02:41:26 executor_base.py:113] Maximum concurrency for 32768 tokens per request: 35.19x
INFO 07-15 02:41:28 model_runner.py:1430] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
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INFO 07-15 02:41:38 model_runner.py:1558] Graph capturing finished in 11 secs, took 0.00 GiB
INFO 07-15 02:41:38 llm_engine.py:429] init engine (profile, create kv cache, warmup model) took 13.63 seconds
There were missing keys in the checkpoint model loaded: ['lm_head.weight'].
/root/countdown-venv/lib/python3.12/site-packages/transformers/trainer.py:3441: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location=map_location)
wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from WANDB_API_KEY.
wandb: Currently logged in as: spongerish (spongerish-sponge) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
wandb: setting up run 1hjcn97o
wandb: Tracking run with wandb version 0.28.0
wandb: Run data is saved locally in /workspace/countdown/wandb/run-20260715_024258-1hjcn97o
wandb: Run `wandb offline` to turn off syncing.
wandb: Resuming run h100-full-hillclimb-100
wandb: ⭐️ View project at https://wandb.ai/spongerish-sponge/countdown-grpo
wandb: 🚀 View run at https://wandb.ai/spongerish-sponge/countdown-grpo/runs/1hjcn97o
wandb: Detected [huggingface_hub.inference, openai] in use.
wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
wandb: For more information, check out the docs at: https://weave-docs.wandb.ai
0%| | 0/450 [00:00<?, ?it/s]/root/countdown-venv/lib/python3.12/site-packages/transformers/trainer.py:3105: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
checkpoint_rng_state = torch.load(rng_file)
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wandb: WARNING The get_url method is deprecated and will be removed in a future release. Please use `run.url` instead.
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