INFO 04-11 12:35:41 [__init__.py:216] Automatically detected platform cuda.
2026-04-11 12:35:42,502 | INFO | train_grpo_sanity | mode=sanity

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The model is already on multiple devices. Skipping the move to device specified in `args`.
INFO 04-11 12:36:18 [utils.py:233] non-default args: {'seed': 0, 'max_model_len': 1664, 'distributed_executor_backend': 'external_launcher', 'gpu_memory_utilization': 0.6, 'max_num_batched_tokens': 4096, 'max_num_seqs': 8, 'disable_log_stats': True, 'model_impl': 'vllm', 'model': 'Navid-AI/Yehia-7B-preview'}
INFO 04-11 12:36:19 [model.py:547] Resolved architecture: LlamaForCausalLM
INFO 04-11 12:36:19 [model.py:1510] Using max model len 1664
INFO 04-11 12:36:19 [parallel.py:380] Using external launcher for distributed inference.
INFO 04-11 12:36:19 [parallel.py:420] Disabling V1 multiprocessing for external launcher.
INFO 04-11 12:36:19 [scheduler.py:205] Chunked prefill is enabled with max_num_batched_tokens=4096.
INFO 04-11 12:36:20 [core.py:77] Initializing a V1 LLM engine (v0.11.0) with config: model='Navid-AI/Yehia-7B-preview', speculative_config=None, tokenizer='Navid-AI/Yehia-7B-preview', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=1664, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=Navid-AI/Yehia-7B-preview, enable_prefix_caching=True, chunked_prefill_enabled=True, pooler_config=None, compilation_config={"level":3,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":[],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output","vllm.mamba_mixer2","vllm.mamba_mixer","vllm.short_conv","vllm.linear_attention","vllm.plamo2_mamba_mixer","vllm.gdn_attention","vllm.sparse_attn_indexer"],"use_inductor":true,"compile_sizes":[],"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"cudagraph_mode":[2,1],"use_cudagraph":true,"cudagraph_num_of_warmups":1,"cudagraph_capture_sizes":[16,8,4,2,1],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"use_inductor_graph_partition":false,"pass_config":{},"max_capture_size":16,"local_cache_dir":null}
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
INFO 04-11 12:36:20 [parallel_state.py:1208] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0
WARNING 04-11 12:36:21 [topk_topp_sampler.py:66] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.
INFO 04-11 12:36:21 [gpu_model_runner.py:2602] Starting to load model Navid-AI/Yehia-7B-preview...
INFO 04-11 12:36:21 [gpu_model_runner.py:2634] Loading model from scratch...
INFO 04-11 12:36:21 [cuda.py:366] Using Flash Attention backend on V1 engine.
INFO 04-11 12:36:22 [weight_utils.py:392] Using model weights format ['*.safetensors']

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INFO 04-11 12:36:25 [default_loader.py:267] Loading weights took 2.84 seconds
INFO 04-11 12:36:25 [gpu_model_runner.py:2653] Model loading took 13.0406 GiB and 3.608898 seconds
INFO 04-11 12:36:30 [backends.py:548] Using cache directory: /root/.cache/vllm/torch_compile_cache/bf2aab3653/rank_0_0/backbone for vLLM's torch.compile
INFO 04-11 12:36:30 [backends.py:559] Dynamo bytecode transform time: 4.86 s
INFO 04-11 12:36:33 [backends.py:197] Cache the graph for dynamic shape for later use
INFO 04-11 12:36:51 [backends.py:218] Compiling a graph for dynamic shape takes 20.08 s
INFO 04-11 12:37:01 [monitor.py:34] torch.compile takes 24.94 s in total
INFO 04-11 12:37:02 [gpu_worker.py:298] Available KV cache memory: 13.09 GiB
INFO 04-11 12:37:02 [kv_cache_utils.py:1087] GPU KV cache size: 26,816 tokens
INFO 04-11 12:37:02 [kv_cache_utils.py:1091] Maximum concurrency for 1,664 tokens per request: 16.12x

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INFO 04-11 12:37:03 [gpu_model_runner.py:3480] Graph capturing finished in 1 secs, took 0.09 GiB
INFO 04-11 12:37:03 [core.py:210] init engine (profile, create kv cache, warmup model) took 38.23 seconds
INFO 04-11 12:37:04 [llm.py:306] Supported_tasks: ('generate',)
2026-04-11 12:37:04,532 | INFO | train_grpo_sanity | dataset_id=Shaer-AI/ashaar-enhanced-desc-baseform-final-sft-lte20-min500-splits-grpo-meter-count-v1 train_size=8 eval_size=8
2026-04-11 12:37:04,532 | INFO | train_grpo_sanity | source_dataset_id=Shaer-AI/ashaar-with-enhanced-descriptions-baseform-final-sft-lte20-min500-splits test_size=204 hard_diagnostic_size=8
2026-04-11 12:37:04,532 | INFO | train_grpo_sanity | active_rewards=['meter', 'exact_count_bonus']
2026-04-11 12:37:04,532 | INFO | train_grpo_sanity | reward_weights={'meter': 1.0, 'exact_count_bonus': 0.2}
2026-04-11 12:37:04,532 | INFO | train_grpo_sanity | output_repo=Shaer-AI/Shaer-adapters-grpo
2026-04-11 12:37:04,532 | INFO | train_grpo_sanity | train_manifest_path=/root/workspace/Shaer/grpo/outputs/curated_meter_count_locked/cap_3000/selected_manifest.csv
2026-04-11 12:37:04,532 | INFO | train_grpo_sanity | hard_diagnostic_manifest_path=/root/workspace/Shaer/grpo/outputs/curated_meter_count_locked/hard_diagnostic_cap_256/selected_manifest.csv
2026-04-11 12:37:04,532 | INFO | train_grpo_sanity | trainable_params=159907840 total_params=7160467456 trainable_ratio=0.022332
2026-04-11 12:37:04,533 | INFO | train_grpo_sanity | resume_decision={"timestamp_utc": "2026-04-11T12:36:18Z", "resume_mode": "auto", "requested_spec": "", "result": "fresh", "reason": "resume_not_found: FileNotFoundError", "local_resume_path": null, "remote_repo": null, "remote_prefix": null, "compatibility": "unchecked", "config_fingerprint": "f1af89e03892f32e677c238fac409c4e9de7ee84de340974e468cc8fb4c82d8b"}
2026-04-11 12:37:04,533 | INFO | train_grpo_sanity | starting trainer.train()
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}.

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/root/workspace/Shaer/grpo/.venv/lib/python3.11/site-packages/torch/utils/checkpoint.py:85: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

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{'loss': 0.0268, 'grad_norm': 4.5472893714904785, 'learning_rate': 1e-05, 'num_tokens': 3887.0, 'completions/mean_length': 282.125, 'completions/min_length': 86.0, 'completions/max_length': 640.0, 'completions/clipped_ratio': 0.25, 'completions/mean_terminated_length': 162.83334350585938, 'completions/min_terminated_length': 86.0, 'completions/max_terminated_length': 237.0, 'rewards/meter/mean': 0.22368116676807404, 'rewards/meter/std': 0.30434706807136536, 'rewards/exact_count_bonus/mean': 0.25, 'rewards/exact_count_bonus/std': 0.4629100561141968, 'reward': 0.2736811637878418, 'reward_std': 0.25279727578163147, 'frac_reward_zero_std': 0.0, 'sampling/sampling_logp_difference/mean': 0.15930314362049103, 'sampling/sampling_logp_difference/max': 1.4203417301177979, 'sampling/importance_sampling_ratio/min': 0.24163144826889038, 'sampling/importance_sampling_ratio/mean': 0.9976902008056641, 'sampling/importance_sampling_ratio/max': 2.0, 'kl': 0.9608133509755135, 'entropy': 3.2345626056194305, 'clip_ratio/low_mean': 0.09094957076013088, 'clip_ratio/low_min': 0.09094957076013088, 'clip_ratio/high_mean': 0.10928299836814404, 'clip_ratio/high_max': 0.10928299836814404, 'clip_ratio/region_mean': 0.20023256912827492, 'reward_total_mean': 0.2736811637878418, 'reward_meter_mean': 0.22368116676807404, 'reward_meter_std': 0.30434706807136536, 'reward_exact_count_bonus_mean': 0.25, 'reward_exact_count_bonus_std': 0.4629100561141968, 'epoch': 0.5}

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{'eval_loss': 0.0866735428571701, 'eval_runtime': 15.7215, 'eval_samples_per_second': 0.509, 'eval_steps_per_second': 0.127, 'eval_num_tokens': 3887.0, 'eval_completions/mean_length': 106.125, 'eval_completions/min_length': 69.25, 'eval_completions/max_length': 156.0, 'eval_completions/clipped_ratio': 0.0, 'eval_completions/mean_terminated_length': 106.125, 'eval_completions/min_terminated_length': 69.25, 'eval_completions/max_terminated_length': 156.0, 'eval_rewards/meter/mean': 0.23453541472554207, 'eval_rewards/meter/std': 0.22889509424567223, 'eval_rewards/exact_count_bonus/mean': 0.9375, 'eval_rewards/exact_count_bonus/std': 0.125, 'eval_reward': 0.42203541100025177, 'eval_reward_std': 0.2391926608979702, 'eval_frac_reward_zero_std': 0.0, 'eval_sampling/sampling_logp_difference/mean': 0.034631784074008465, 'eval_sampling/sampling_logp_difference/max': 0.24606388807296753, 'eval_sampling/importance_sampling_ratio/min': 0.7994033098220825, 'eval_sampling/importance_sampling_ratio/mean': 0.9995981901884079, 'eval_sampling/importance_sampling_ratio/max': 1.2397247850894928, 'eval_kl': 1.3679395914077759, 'eval_entropy': 2.1008258759975433, 'eval_clip_ratio/low_mean': 0.0, 'eval_clip_ratio/low_min': 0.0, 'eval_clip_ratio/high_mean': 0.0, 'eval_clip_ratio/high_max': 0.0, 'eval_clip_ratio/region_mean': 0.0, 'eval_reward_total_mean': 0.42203541100025177, 'eval_reward_meter_mean': 0.23453541472554207, 'eval_reward_meter_std': 0.22889509424567223, 'eval_reward_exact_count_bonus_mean': 0.9375, 'eval_reward_exact_count_bonus_std': 0.125, 'epoch': 0.5}

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                                             /root/workspace/Shaer/grpo/.venv/lib/python3.11/site-packages/torch/utils/checkpoint.py:85: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

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{'loss': 0.0936, 'grad_norm': 10.63160228729248, 'learning_rate': 5e-06, 'num_tokens': 5744.0, 'completions/mean_length': 71.875, 'completions/min_length': 35.0, 'completions/max_length': 146.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 71.875, 'completions/min_terminated_length': 35.0, 'completions/max_terminated_length': 146.0, 'rewards/meter/mean': 0.3511555790901184, 'rewards/meter/std': 0.30735185742378235, 'rewards/exact_count_bonus/mean': 1.0, 'rewards/exact_count_bonus/std': 0.0, 'reward': 0.5511556267738342, 'reward_std': 0.16572850942611694, 'frac_reward_zero_std': 0.0, 'sampling/sampling_logp_difference/mean': 0.18746040761470795, 'sampling/sampling_logp_difference/max': 1.7690210342407227, 'sampling/importance_sampling_ratio/min': 0.17049983143806458, 'sampling/importance_sampling_ratio/mean': 0.9942735433578491, 'sampling/importance_sampling_ratio/max': 2.0, 'kl': 1.4957870244979858, 'entropy': 2.25409172475338, 'clip_ratio/low_mean': 0.12490623071789742, 'clip_ratio/low_min': 0.12490623071789742, 'clip_ratio/high_mean': 0.11229794658720493, 'clip_ratio/high_max': 0.11229794658720493, 'clip_ratio/region_mean': 0.23720417730510235, 'reward_total_mean': 0.5511556267738342, 'reward_meter_mean': 0.3511555790901184, 'reward_meter_std': 0.30735185742378235, 'reward_exact_count_bonus_mean': 1.0, 'reward_exact_count_bonus_std': 0.0, 'epoch': 1.0}

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{'eval_loss': 0.06863964349031448, 'eval_runtime': 14.0, 'eval_samples_per_second': 0.571, 'eval_steps_per_second': 0.143, 'eval_num_tokens': 5744.0, 'eval_completions/mean_length': 97.8125, 'eval_completions/min_length': 69.0, 'eval_completions/max_length': 140.75, 'eval_completions/clipped_ratio': 0.0, 'eval_completions/mean_terminated_length': 97.8125, 'eval_completions/min_terminated_length': 69.0, 'eval_completions/max_terminated_length': 140.75, 'eval_rewards/meter/mean': 0.40700408816337585, 'eval_rewards/meter/std': 0.3741379380226135, 'eval_rewards/exact_count_bonus/mean': 1.0, 'eval_rewards/exact_count_bonus/std': 0.0, 'eval_reward': 0.6070040911436081, 'eval_reward_std': 0.30008064955472946, 'eval_frac_reward_zero_std': 0.0, 'eval_sampling/sampling_logp_difference/mean': 0.03424238506704569, 'eval_sampling/sampling_logp_difference/max': 0.24058371782302856, 'eval_sampling/importance_sampling_ratio/min': 0.7956851571798325, 'eval_sampling/importance_sampling_ratio/mean': 0.9988586455583572, 'eval_sampling/importance_sampling_ratio/max': 1.2537521719932556, 'eval_kl': 1.46183642745018, 'eval_entropy': 2.2587124407291412, 'eval_clip_ratio/low_mean': 0.0, 'eval_clip_ratio/low_min': 0.0, 'eval_clip_ratio/high_mean': 0.0, 'eval_clip_ratio/high_max': 0.0, 'eval_clip_ratio/region_mean': 0.0, 'eval_reward_total_mean': 0.6070040911436081, 'eval_reward_meter_mean': 0.40700408816337585, 'eval_reward_meter_std': 0.3741379380226135, 'eval_reward_exact_count_bonus_mean': 1.0, 'eval_reward_exact_count_bonus_std': 0.0, 'epoch': 1.0}

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                                             2026-04-11 12:38:07,651 | INFO | train_grpo_sanity | metrics_logged mode=train step=2

                                             
{'train_runtime': 62.6885, 'train_samples_per_second': 0.255, 'train_steps_per_second': 0.032, 'train_loss': 0.060226328670978546, 'epoch': 1.0}

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