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target_lmm/.gitattributes

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target_lmm/config.json

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+{
+  "architectures": [
+    "UnifiedForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 100257,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 4096,
+  "model_type": "olmo2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 16,
+  "pad_token_id": 100277,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 500000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "vocab_size": 100289
+}

target_lmm/generation_config.json

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+{
+  "_from_model_config": true,
+  "eos_token_id": 100257,
+  "pad_token_id": 100277,
+  "transformers_version": "4.51.3"
+}

target_lmm/model.txt

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+UnifiedForCausalLM(
+  (model): UnifiedModel(
+    (embed_tokens): Embedding(100289, 2048, padding_idx=100277)
+    (layers): ModuleList(
+      (0-15): 16 x Olmo2DecoderLayer(
+        (self_attn): Olmo2Attention(
+          (q_proj): Linear(in_features=2048, out_features=2048, bias=False)
+          (k_proj): Linear(in_features=2048, out_features=2048, bias=False)
+          (v_proj): Linear(in_features=2048, out_features=2048, bias=False)
+          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)
+          (q_norm): Olmo2RMSNorm((2048,), eps=1e-06)
+          (k_norm): Olmo2RMSNorm((2048,), eps=1e-06)
+        )
+        (mlp): Olmo2MLP(
+          (gate_proj): Linear(in_features=2048, out_features=8192, bias=False)
+          (up_proj): Linear(in_features=2048, out_features=8192, bias=False)
+          (down_proj): Linear(in_features=8192, out_features=2048, bias=False)
+          (act_fn): SiLU()
+        )
+        (post_attention_layernorm): Olmo2RMSNorm((2048,), eps=1e-06)
+        (post_feedforward_layernorm): Olmo2RMSNorm((2048,), eps=1e-06)
+      )
+    )
+    (norm): Olmo2RMSNorm((2048,), eps=1e-06)
+    (rotary_emb): Olmo2RotaryEmbedding()
+    (visual_encoder): MultiPathCLIPVisionTower(
+      (slow_vision_tower): ConvNextVisionTower(
+        (vision_tower): ConvNeXt(
+          (stem): Sequential(
+            (0): Conv2d(3, 192, kernel_size=(4, 4), stride=(4, 4))
+            (1): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)
+          )
+          (stages): Sequential(
+            (0): ConvNeXtStage(
+              (downsample): Identity()
+              (blocks): Sequential(
+                (0): ConvNeXtBlock(
+                  (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
+                  (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=192, out_features=768, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=768, out_features=192, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (1): ConvNeXtBlock(
+                  (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
+                  (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=192, out_features=768, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=768, out_features=192, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (2): ConvNeXtBlock(
+                  (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
+                  (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=192, out_features=768, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=768, out_features=192, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+              )
+            )
+            (1): ConvNeXtStage(
+              (downsample): Sequential(
+                (0): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)
+                (1): Conv2d(192, 384, kernel_size=(2, 2), stride=(2, 2))
+              )
+              (blocks): Sequential(
+                (0): ConvNeXtBlock(
+                  (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
+                  (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=384, out_features=1536, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=1536, out_features=384, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (1): ConvNeXtBlock(
+                  (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
+                  (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=384, out_features=1536, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=1536, out_features=384, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (2): ConvNeXtBlock(
+                  (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
+                  (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=384, out_features=1536, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=1536, out_features=384, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+              )
+            )
+            (2): ConvNeXtStage(
+              (downsample): Sequential(
+                (0): LayerNorm2d((384,), eps=1e-06, elementwise_affine=True)
+                (1): Conv2d(384, 768, kernel_size=(2, 2), stride=(2, 2))
+              )
+              (blocks): Sequential(
+                (0): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (1): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (2): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (3): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (4): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (5): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (6): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (7): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (8): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (9): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (10): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (11): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (12): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (13): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (14): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (15): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (16): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (17): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (18): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (19): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (20): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (21): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (22): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (23): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (24): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (25): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (26): ConvNeXtBlock(
+                  (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
+                  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+              )
+            )
+            (3): ConvNeXtStage(
+              (downsample): Sequential(
+                (0): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True)
+                (1): Conv2d(768, 1536, kernel_size=(2, 2), stride=(2, 2))
+              )
+              (blocks): Sequential(
+                (0): ConvNeXtBlock(
+                  (conv_dw): Conv2d(1536, 1536, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1536)
+                  (norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=1536, out_features=6144, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=6144, out_features=1536, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (1): ConvNeXtBlock(
+                  (conv_dw): Conv2d(1536, 1536, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1536)
+                  (norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=1536, out_features=6144, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=6144, out_features=1536, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+                (2): ConvNeXtBlock(
+                  (conv_dw): Conv2d(1536, 1536, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1536)
+                  (norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): Linear(in_features=1536, out_features=6144, bias=True)
+                    (act): GELU()
+                    (drop1): Dropout(p=0.0, inplace=False)
+                    (norm): Identity()
+                    (fc2): Linear(in_features=6144, out_features=1536, bias=True)
+                    (drop2): Dropout(p=0.0, inplace=False)
+                  )
+                  (shortcut): Identity()
+                  (drop_path): Identity()
+                )
+              )
+            )
+          )
+          (norm_pre): Identity()
+          (head): NormMlpClassifierHead(
+            (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity())
+            (norm): LayerNorm2d((1536,), eps=1e-06, elementwise_affine=True)
+            (flatten): Flatten(start_dim=1, end_dim=-1)
+            (pre_logits): Sequential(
+              (fc): Linear(in_features=1536, out_features=1536, bias=True)
+              (act): GELU()
+            )
+            (drop): Dropout(p=0.0, inplace=False)
+            (fc): Linear(in_features=1536, out_features=1000, bias=True)
+          )
+        )
+      )
+      (fast_vision_tower): CLIPVisionTower(
+        (vision_tower): CLIPVisionModel(
+          (vision_model): CLIPVisionTransformer(
+            (embeddings): CLIPVisionEmbeddings(
+              (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)
+              (position_embedding): Embedding(577, 1024)
+            )
+            (pre_layrnorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+            (encoder): CLIPEncoder(
+              (layers): ModuleList(
+                (0-23): 24 x CLIPEncoderLayer(
+                  (self_attn): CLIPSdpaAttention(
+                    (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
+                    (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
+                    (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
+                    (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
+                  )
+                  (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+                  (mlp): CLIPMLP(
+                    (activation_fn): QuickGELUActivation()
+                    (fc1): Linear(in_features=1024, out_features=4096, bias=True)
+                    (fc2): Linear(in_features=4096, out_features=1024, bias=True)
+                  )
+                  (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+                )
+              )
+            )
+            (post_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+          )
+        )
+      )
+      (align_stages_latent): ModuleList(
+        (0-2): 3 x S2FStitchAlignModuleV2(
+          (slow_conv): Conv2d(1536, 1536, kernel_size=(1, 1), stride=(1, 1))
+          (slow_proj): Conv2d(1536, 1024, kernel_size=(1, 1), stride=(1, 1))
+          (fast_conv): Conv2d(1024, 1024, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1024)
+          (fast_proj): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))
+          (gate): Sequential(
+            (0): Linear(in_features=2048, out_features=512, bias=True)
+            (1): GELU(approximate='none')
+            (2): Linear(in_features=512, out_features=1, bias=True)
+          )
+        )
+      )
+      (align_stages): ModuleList(
+        (0): MultiPathAlignModule(
+          (fast_proj): Linear(in_features=1024, out_features=1024, bias=True)
+          (slow_proj): Linear(in_features=1536, out_features=1024, bias=True)
+        )
+      )
+    )
+    (vl_projector): Sequential(
+      (0): Linear(in_features=1024, out_features=2048, bias=True)
+      (1): GELU(approximate='none')
+      (2): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+  (lm_head): Linear(in_features=2048, out_features=100289, bias=False)
+)

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+size 3620508440

target_lmm/saved_config.json

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+{
+    "model_args": {
+        "model_name_or_path": "allenai/OLMo-2-0425-1B-Instruct",
+        "llm_name": "default_olmo2",
+        "adapter_type": null,
+        "inputs_embeds_with_mmask": false,
+        "speech_proj_ckpt_dir": null,
+        "visual_proj_ckpt_dir": "models/vp/olmo_1b_vp_tsvqa/non_lora_trainables.bin",
+        "pretrained_ckpt_path": null,
+        "visual_encoder_type": "llava_hr_1b",
+        "speech_encoder_type": null,
+        "d_model": 2048
+    },
+    "data_args": {
+        "video_frame_nums": 8,
+        "data_image_size": 1024,
+        "multi_frames": false,
+        "tasks": "p4ms_vqa,llava_vqa,synthdog_en,ocrvqa,text_ocr,text_caps",
+        "mel_size": 128,
+        "is_test": false,
+        "pack_conversations": true
+    },
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+        "overwrite_output_dir": false,
+        "do_train": false,
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+        "eval_strategy": "no",
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+        "adam_epsilon": 1e-08,
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+        "log_level": "passive",
+        "log_level_replica": "warning",
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+        "logging_dir": "models/hackathon_models//olmo2_vqa_1b/runs/Apr14_18-24-59_jrc0913",
+        "logging_strategy": "steps",
+        "logging_first_step": false,
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+        "save_only_model": false,
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+        "use_cpu": false,
+        "use_mps_device": false,
+        "seed": 123412341,
+        "data_seed": null,
+        "jit_mode_eval": false,
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+        "fp16": false,
+        "fp16_opt_level": "O1",
+        "half_precision_backend": "auto",
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+        "tpu_metrics_debug": false,
+        "debug": [],
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+            "xla": false,
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+            "use_seedable_sampler": true,
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+        "deepspeed": "src/lmms/deepspeed/stage2.json",
+        "label_smoothing_factor": 0.0,
+        "optim": "adamw_torch",
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+        "adafactor": false,
+        "group_by_length": false,
+        "length_column_name": "length",
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+        "use_liger_kernel": false,
+        "eval_use_gather_object": false,
+        "average_tokens_across_devices": false,
+        "mm_projector_lr": null,
+        "freeze_mm_mlp_adapter": false,
+        "cache_dir": null,
+        "group_by_modality_length": false,
+        "model_max_length": 512,
+        "double_quant": true,
+        "quant_type": "nf4",
+        "bits": 32,
+        "lora_trainable": "q_proj,k_proj,v_proj,o_proj,gate_proj,down_proj,up_proj",
+        "visual_branch": true,
+        "speech_branch": false,
+        "save_modules": "model",
+        "exp_desc": "exp",
+        "global_adaptive_clipping": false
+    }
+}