Upload WinARDet compression model checkpoints

.gitattributes

@@ -33,3 +33,36 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+WinARDet/DOTA2_polygon/sft_seg_400_8-12-8_l6_e4_lr10_rescale/Results/DOTA2_Polygon/answer.jsonl filter=lfs diff=lfs merge=lfs -text
+WinARDet/SKU110K/sft_base/Results/Sku110k/answer.jsonl filter=lfs diff=lfs merge=lfs -text
+WinARDet/SKU110K/sft_base/Results/Sku110k/fast_eval.tsv filter=lfs diff=lfs merge=lfs -text
+WinARDet/SKU110K/sft_base_oversample300/Results/Sku110k/answer.jsonl filter=lfs diff=lfs merge=lfs -text
+WinARDet/SKU110K/sft_base_oversample300/Results/Sku110k/fast_eval.tsv filter=lfs diff=lfs merge=lfs -text
+WinARDet/SKU110K/sft_base_oversample300/Results/Sku110k/fast_eval_recheck_20261012.tsv filter=lfs diff=lfs merge=lfs -text
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+WinARDet/SODA-A_polygon/sft_base_oversample/Results/SODA-A_Polygon/answer.jsonl filter=lfs diff=lfs merge=lfs -text
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+WinARDet/dense/sft_base/Results/DOTA2_SODA-A/answer.jsonl filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_base/Results/DOTA2_SODA-A/fast_eval.tsv filter=lfs diff=lfs merge=lfs -text
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+WinARDet/dense/sft_base_oversample300/Results/DOTA2_SODA-A/answer.jsonl filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_base_oversample300/Results/DOTA2_SODA-A/fast_eval.tsv filter=lfs diff=lfs merge=lfs -text
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+WinARDet/dense/sft_base_oversample300/Results/Rsdense/answer.jsonl filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_base_oversample300/Results/Rsdense/fast_eval.tsv filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_base_oversample300/Results/Rsdense/fast_eval_recheck_20261012.tsv filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_base_oversample300_grpo/log.txt filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_base_oversample300_grpo/model/Results/DOTA2_SODA-A/answer.jsonl filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_base_oversample300_grpo/model/Results/DOTA2_SODA-A/fast_eval.tsv filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_base_oversample300_grpo/model/Results/Rsdense/answer.jsonl filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_base_oversample300_grpo/model/Results/Rsdense/fast_eval.tsv filter=lfs diff=lfs merge=lfs -text
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+WinARDet/dense/sft_seg_300_6-8-6_l6_e4_lr10_rescale/Results/DOTA2_SODA-A/fast_eval.tsv filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_seg_300_6-8-6_l6_e4_lr10_rescale/Results/DOTA2_SODA-A/fast_eval_recheck_20261012.tsv filter=lfs diff=lfs merge=lfs -text
+WinARDet/dense/sft_seg_300_6-8-6_l6_e4_lr10_rescale/Results/Rsdense/answer.jsonl filter=lfs diff=lfs merge=lfs -text
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WinARDet/DOTA2_polygon/sft_base_oversample400/Results/Dota2_Polygon/answer.jsonl

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WinARDet/DOTA2_polygon/sft_base_oversample400/Results/Dota2_Polygon/poly_eval_results.json

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+{
+  "metric": "fast_rotated_iou_recall_precision_f1",
+  "data_path": "/home/u1120250365/Rex-Omni/finetuning/work_dirs/compression/DOTA2_polygon/sft_base_oversample400/Results/Dota2_Polygon/answer.jsonl",
+  "iou_threshold": 0.5,
+  "angle_version": "oc",
+  "clockwise": true,
+  "confidence_handling": "same_as_other_metric_all_predictions_counted_no_score_threshold",
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WinARDet/DOTA2_polygon/sft_base_oversample400/added_tokens.json

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WinARDet/DOTA2_polygon/sft_base_oversample400/chat_template.json

@@ -0,0 +1,3 @@
+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

WinARDet/DOTA2_polygon/sft_base_oversample400/config.json

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+{
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+    "Qwen2_5_VLForConditionalGeneration"
+  ],
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+  "eos_token_id": 151645,
+  "hidden_act": "silu",
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+  "max_position_embeddings": 128000,
+  "max_window_layers": 70,
+  "model_type": "qwen2_5_vl",
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+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
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+      24,
+      24
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+    "rope_type": "default",
+    "type": "default"
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+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
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+      15,
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+      31
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+    "in_channels": 3,
+    "in_chans": 3,
+    "intermediate_size": 3420,
+    "model_type": "qwen2_5_vl",
+    "num_heads": 16,
+    "out_hidden_size": 2048,
+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "spatial_patch_size": 14,
+    "temporal_patch_size": 2,
+    "tokens_per_second": 2,
+    "torch_dtype": "bfloat16",
+    "window_size": 112
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+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

WinARDet/DOTA2_polygon/sft_base_oversample400/generation_config.json

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+{
+  "_from_model_config": true,
+  "eos_token_id": 151645,
+  "pad_token_id": 151643,
+  "transformers_version": "4.51.3"
+}

WinARDet/DOTA2_polygon/sft_base_oversample400/info.txt

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+[08/06 05:20:30.020]: git:
+  sha: N/A, status: clean, branch: N/A
+
+[08/06 05:20:30.020]: Command: train.py --config configs/compression/sft_base_polygon_overssample.py --deepspeed scripts/zero2.json --data_flatten False --tune_mm_vision True --tune_mm_mlp True --tune_mm_llm True --bf16 --output_dir /root/autodl-tmp/work_dir_dota2_polygon_oversample --num_train_epochs 4 --per_device_train_batch_size 1 --gradient_accumulation_steps 16 --eval_strategy no --save_strategy steps --save_steps 1000 --save_total_limit 5 --learning_rate 4e-5 --mm_projector_lr 4e-5 --vision_tower_lr 4e-6 --optim adamw_torch --warmup_ratio 0.05 --weight_decay 0.05 --max_grad_norm 1 --lr_scheduler_type cosine --logging_steps 1 --model_max_length 40000 --gradient_checkpointing True --dataloader_num_workers 4 --run_name rexomni_sft_polygon --report_to wandb
+[08/06 05:20:31.000]: number of training params:3754622976
+[08/06 05:20:31.002]: Training params:
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+  "model.norm.weight": 2048
+}
+[08/06 05:20:31.004]: Model structure:
+[08/06 05:20:31.004]: Qwen2_5_VLForConditionalGeneration(
+  (visual): Qwen2_5_VisionTransformerPretrainedModel(
+    (patch_embed): Qwen2_5_VisionPatchEmbed(
+      (proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)
+    )
+    (rotary_pos_emb): Qwen2_5_VisionRotaryEmbedding()
+    (blocks): ModuleList(
+      (0-31): 32 x Qwen2_5_VLVisionBlock(
+        (norm1): Qwen2RMSNorm((1280,), eps=1e-06)
+        (norm2): Qwen2RMSNorm((1280,), eps=1e-06)
+        (attn): Qwen2_5_VLVisionFlashAttention2(
+          (qkv): Linear(in_features=1280, out_features=3840, bias=True)
+          (proj): Linear(in_features=1280, out_features=1280, bias=True)
+        )
+        (mlp): Qwen2_5_VLMLP(
+          (gate_proj): Linear(in_features=1280, out_features=3420, bias=True)
+          (up_proj): Linear(in_features=1280, out_features=3420, bias=True)
+          (down_proj): Linear(in_features=3420, out_features=1280, bias=True)
+          (act_fn): SiLU()
+        )
+      )
+    )
+    (merger): Qwen2_5_VLPatchMerger(
+      (ln_q): Qwen2RMSNorm((1280,), eps=1e-06)
+      (mlp): Sequential(
+        (0): Linear(in_features=5120, out_features=5120, bias=True)
+        (1): GELU(approximate='none')
+        (2): Linear(in_features=5120, out_features=2048, bias=True)
+      )
+    )
+  )
+  (model): Qwen2_5_VLModel(
+    (embed_tokens): Embedding(151936, 2048, padding_idx=151643)
+    (layers): ModuleList(
+      (0-35): 36 x Qwen2_5_VLDecoderLayer(
+        (self_attn): Qwen2_5_VLFlashAttention2(
+          (q_proj): Linear(in_features=2048, out_features=2048, bias=True)
+          (k_proj): Linear(in_features=2048, out_features=256, bias=True)
+          (v_proj): Linear(in_features=2048, out_features=256, bias=True)
+          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)
+          (rotary_emb): Qwen2_5_VLRotaryEmbedding()
+        )
+        (mlp): Qwen2MLP(
+          (gate_proj): Linear(in_features=2048, out_features=11008, bias=False)
+          (up_proj): Linear(in_features=2048, out_features=11008, bias=False)
+          (down_proj): Linear(in_features=11008, out_features=2048, bias=False)
+          (act_fn): SiLU()
+        )
+        (input_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
+        (post_attention_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
+      )
+    )
+    (norm): Qwen2RMSNorm((2048,), eps=1e-06)
+    (rotary_emb): Qwen2_5_VLRotaryEmbedding()
+  )
+  (lm_head): Linear(in_features=2048, out_features=151936, bias=False)
+)
+[08/06 05:25:09.706]: Training dataset size: 20881

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WinARDet/DOTA2_polygon/sft_base_oversample400/training_args.bin

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WinARDet/DOTA2_polygon/sft_seg_400_8-12-8_l6_e4_lr10_rescale/Results/DOTA2_Polygon/poly_eval_results.json

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+  "data_path": "/home/u1120250365/Rex-Omni/finetuning/work_dirs/compression/DOTA2_polygon/sft_seg_400_8-12-8_l6_e4_lr10_rescale_seed42/Results/DOTA2_Polygon/answer.jsonl",
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WinARDet/DOTA2_polygon/sft_seg_400_8-12-8_l6_e4_lr10_rescale/added_tokens.json

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WinARDet/DOTA2_polygon/sft_seg_400_8-12-8_l6_e4_lr10_rescale/chat_template.json

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+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

WinARDet/DOTA2_polygon/sft_seg_400_8-12-8_l6_e4_lr10_rescale/config.json

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+{
+  "architectures": [
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+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
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+  "vocab_size": 151936
+}

WinARDet/DOTA2_polygon/sft_seg_400_8-12-8_l6_e4_lr10_rescale/generation_config.json

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+{
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+  "eos_token_id": 151645,
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+  "transformers_version": "4.51.3"
+}

WinARDet/DOTA2_polygon/sft_seg_400_8-12-8_l6_e4_lr10_rescale/info.txt

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+[08/21 16:18:00.178]: git:
+  sha: N/A, status: clean, branch: N/A
+
+[08/21 16:18:00.179]: Command: train_seg_seed42.py --config configs/compression/sft_sacom_polygon.py --deepspeed scripts/zero2.json --data_flatten False --tune_mm_vision True --tune_mm_mlp True --tune_mm_llm True --bf16 --output_dir work_dirs/compression/DOTA2_polygon/sft_seg_400_8-12-8_l6_e4_lr10_rescale_seed42 --num_train_epochs 4 --per_device_train_batch_size 1 --gradient_accumulation_steps 16 --eval_strategy no --save_strategy steps --save_steps 1000 --save_total_limit 5 --learning_rate 4e-5 --mm_projector_lr 4e-5 --vision_tower_lr 4e-6 --context_compressor_lr 4e-4 --optim adamw_torch --warmup_ratio 0.05 --weight_decay 0.05 --max_grad_norm 1 --lr_scheduler_type cosine --logging_steps 1 --model_max_length 40000 --gradient_checkpointing True --dataloader_num_workers 4 --run_name rexomni_polygon_compression --report_to wandb
+[08/21 16:18:00.179]: Segment-causal compression keeps standard 2D causal mask; using flash_attention_2.
+[08/21 16:18:02.180]: Initializing context_compressor with fixed seed 42.
+[08/21 16:18:05.113]: number of training params:4074334208
+[08/21 16:18:05.117]: Training params:
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+}
+[08/21 16:18:05.119]: Model structure:
+[08/21 16:18:05.119]: Qwen2_5_VLForConditionalGeneration(
+  (visual): Qwen2_5_VisionTransformerPretrainedModel(
+    (patch_embed): Qwen2_5_VisionPatchEmbed(
+      (proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)
+    )
+    (rotary_pos_emb): Qwen2_5_VisionRotaryEmbedding()
+    (blocks): ModuleList(
+      (0-31): 32 x Qwen2_5_VLVisionBlock(
+        (norm1): Qwen2RMSNorm((1280,), eps=1e-06)
+        (norm2): Qwen2RMSNorm((1280,), eps=1e-06)
+        (attn): Qwen2_5_VLVisionFlashAttention2(
+          (qkv): Linear(in_features=1280, out_features=3840, bias=True)
+          (proj): Linear(in_features=1280, out_features=1280, bias=True)
+        )
+        (mlp): Qwen2_5_VLMLP(
+          (gate_proj): Linear(in_features=1280, out_features=3420, bias=True)
+          (up_proj): Linear(in_features=1280, out_features=3420, bias=True)
+          (down_proj): Linear(in_features=3420, out_features=1280, bias=True)
+          (act_fn): SiLU()
+        )
+      )
+    )
+    (merger): Qwen2_5_VLPatchMerger(
+      (ln_q): Qwen2RMSNorm((1280,), eps=1e-06)
+      (mlp): Sequential(
+        (0): Linear(in_features=5120, out_features=5120, bias=True)
+        (1): GELU(approximate='none')
+        (2): Linear(in_features=5120, out_features=2048, bias=True)
+      )
+    )
+  )
+  (model): Qwen2_5_VLModel(
+    (embed_tokens): Embedding(151936, 2048, padding_idx=151643)
+    (layers): ModuleList(
+      (0-35): 36 x Qwen2_5_VLDecoderLayer(
+        (self_attn): Qwen2_5_VLFlashAttention2(
+          (q_proj): Linear(in_features=2048, out_features=2048, bias=True)
+          (k_proj): Linear(in_features=2048, out_features=256, bias=True)
+          (v_proj): Linear(in_features=2048, out_features=256, bias=True)
+          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)
+          (rotary_emb): Qwen2_5_VLRotaryEmbedding()
+        )
+        (mlp): Qwen2MLP(
+          (gate_proj): Linear(in_features=2048, out_features=11008, bias=False)
+          (up_proj): Linear(in_features=2048, out_features=11008, bias=False)
+          (down_proj): Linear(in_features=11008, out_features=2048, bias=False)
+          (act_fn): SiLU()
+        )
+        (input_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
+        (post_attention_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
+      )
+    )
+    (norm): Qwen2RMSNorm((2048,), eps=1e-06)
+    (rotary_emb): Qwen2_5_VLRotaryEmbedding()
+  )
+  (lm_head): Linear(in_features=2048, out_features=151936, bias=False)
+  (context_compressor): ObjectMemoryCompressor(
+    (segment_proj): Sequential(
+      (0): Linear(in_features=2048, out_features=2048, bias=True)
+      (1): GELU(approximate='none')
+      (2): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+    (cross_attn_layers): ModuleList(
+      (0-5): 6 x MultiheadAttention(
+        (out_proj): NonDynamicallyQuantizableLinear(in_features=2048, out_features=2048, bias=True)
+      )
+    )
+    (query_norms): ModuleList(
+      (0-5): 6 x RMSNorm()
+    )
+    (kv_norms): ModuleList(
+      (0-5): 6 x RMSNorm()
+    )
+    (ffn_norms): ModuleList(
+      (0-5): 6 x RMSNorm()
+    )
+    (ffns): ModuleList(
+      (0-5): 6 x Sequential(
+        (0): Linear(in_features=2048, out_features=8192, bias=True)
+        (1): GELU(approximate='none')
+        (2): Linear(in_features=8192, out_features=2048, bias=True)
+      )
+    )
+    (output_norm): RMSNorm()
+    (output_proj): Sequential(
+      (0): Linear(in_features=2048, out_features=2048, bias=True)
+      (1): GELU(approximate='none')
+      (2): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+)
+[08/21 16:31:48.396]: Training dataset size: 20881

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WinARDet/SKU110K/sft_base/chat_template.json

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+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

WinARDet/SKU110K/sft_base/config.json

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WinARDet/SKU110K/sft_base/generation_config.json

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+}

WinARDet/SKU110K/sft_base/info.txt

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+[06/18 09:20:43.119]: git:
+  sha: N/A, status: clean, branch: N/A
+
+[06/18 09:20:43.119]: Command: train.py --config configs/compression/sft_base_sku110k.py --deepspeed scripts/zero2.json --data_flatten False --tune_mm_vision True --tune_mm_mlp True --tune_mm_llm True --bf16 --output_dir work_dirs/compression/SKU110K/sft_base --num_train_epochs 4 --per_device_train_batch_size 1 --gradient_accumulation_steps 16 --eval_strategy no --save_strategy steps --save_steps 1000 --save_total_limit 5 --learning_rate 4e-5 --mm_projector_lr 4e-5 --vision_tower_lr 4e-6 --optim adamw_torch --warmup_ratio 0.05 --weight_decay 0.05 --max_grad_norm 1 --lr_scheduler_type cosine --logging_steps 1 --model_max_length 15000 --gradient_checkpointing True --dataloader_num_workers 4 --run_name rexomni_sft --report_to wandb
+[06/18 09:20:45.123]: number of training params:3754622976
+[06/18 09:20:45.126]: Training params:
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+  "model.norm.weight": 2048
+}
+[06/18 09:20:45.127]: Model structure:
+[06/18 09:20:45.127]: Qwen2_5_VLForConditionalGeneration(
+  (visual): Qwen2_5_VisionTransformerPretrainedModel(
+    (patch_embed): Qwen2_5_VisionPatchEmbed(
+      (proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)
+    )
+    (rotary_pos_emb): Qwen2_5_VisionRotaryEmbedding()
+    (blocks): ModuleList(
+      (0-31): 32 x Qwen2_5_VLVisionBlock(
+        (norm1): Qwen2RMSNorm((1280,), eps=1e-06)
+        (norm2): Qwen2RMSNorm((1280,), eps=1e-06)
+        (attn): Qwen2_5_VLVisionFlashAttention2(
+          (qkv): Linear(in_features=1280, out_features=3840, bias=True)
+          (proj): Linear(in_features=1280, out_features=1280, bias=True)
+        )
+        (mlp): Qwen2_5_VLMLP(
+          (gate_proj): Linear(in_features=1280, out_features=3420, bias=True)
+          (up_proj): Linear(in_features=1280, out_features=3420, bias=True)
+          (down_proj): Linear(in_features=3420, out_features=1280, bias=True)
+          (act_fn): SiLU()
+        )
+      )
+    )
+    (merger): Qwen2_5_VLPatchMerger(
+      (ln_q): Qwen2RMSNorm((1280,), eps=1e-06)
+      (mlp): Sequential(
+        (0): Linear(in_features=5120, out_features=5120, bias=True)
+        (1): GELU(approximate='none')
+        (2): Linear(in_features=5120, out_features=2048, bias=True)
+      )
+    )
+  )
+  (model): Qwen2_5_VLModel(
+    (embed_tokens): Embedding(151936, 2048, padding_idx=151643)
+    (layers): ModuleList(
+      (0-35): 36 x Qwen2_5_VLDecoderLayer(
+        (self_attn): Qwen2_5_VLFlashAttention2(
+          (q_proj): Linear(in_features=2048, out_features=2048, bias=True)
+          (k_proj): Linear(in_features=2048, out_features=256, bias=True)
+          (v_proj): Linear(in_features=2048, out_features=256, bias=True)
+          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)
+          (rotary_emb): Qwen2_5_VLRotaryEmbedding()
+        )
+        (mlp): Qwen2MLP(
+          (gate_proj): Linear(in_features=2048, out_features=11008, bias=False)
+          (up_proj): Linear(in_features=2048, out_features=11008, bias=False)
+          (down_proj): Linear(in_features=11008, out_features=2048, bias=False)
+          (act_fn): SiLU()
+        )
+        (input_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
+        (post_attention_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
+      )
+    )
+    (norm): Qwen2RMSNorm((2048,), eps=1e-06)
+    (rotary_emb): Qwen2_5_VLRotaryEmbedding()
+  )
+  (lm_head): Linear(in_features=2048, out_features=151936, bias=False)
+)
+[06/18 09:20:45.165]: Training dataset size: 8769

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WinARDet/SKU110K/sft_base/preprocessor_config.json

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WinARDet/SKU110K/sft_base/trainer_state.json

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