diff --git "a/create joycap quant.ipynb" "b/create joycap quant.ipynb"
new file mode 100644--- /dev/null
+++ "b/create joycap quant.ipynb"
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Create NDSQ"],"metadata":{"id":"E6zDLuAI_4Zk"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6QShrmt39jCS","executionInfo":{"status":"ok","timestamp":1776961333184,"user_tz":-120,"elapsed":87582,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"3fb59409-4151-4510-f940-764285f22e36"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.4/10.4 MB\u001b[0m \u001b[31m84.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m645.5/645.5 kB\u001b[0m \u001b[31m42.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.0/104.0 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.0/5.0 MB\u001b[0m \u001b[31m94.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h✅ Setup complete\n"]}],"source":["# Mount + HF login\n","from google.colab import drive, userdata\n","from huggingface_hub import login\n","import torch, os, gc\n","\n","drive.mount('/content/drive')\n","\n","hf_token = userdata.get('HF_TOKEN')\n","if hf_token:\n","    login(token=hf_token)\n","\n","# Clean + install\n","!pip uninstall -y diffusers > /dev/null 2>&1\n","!pip install -q --upgrade transformers accelerate huggingface_hub\n","!pip install -q sdnq\n","\n","print(\"✅ Setup complete\")"]},{"cell_type":"code","source":["from huggingface_hub import snapshot_download\n","\n","MODEL_ID = \"fancyfeast/llama-joycaption-beta-one-hf-llava\"\n","\n","local_repo = snapshot_download(\n","    repo_id=MODEL_ID,\n","    local_dir=\"/content/llava_snapshot\",\n","    local_dir_use_symlinks=False\n",")\n","\n","print(\"📦 Snapshot complete:\", local_repo)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":183,"referenced_widgets":["0f5e759d53a64b6c9d08804321a17827","7cc91ac395954e3b975254c26a9b46d6","44bd130a312e47f8991e9cfafb12052e","3dd638776c754b07bc3b92afa5c67371","f1250af1939a4f5f88353e9ad8616b42","30e82eabab664dd2836a84fa6ab8c742","f295b67714914eb8918da0af92e07059","956be771b7284198b9f34d3619a2c08b","0e39e35a62be48b7b1707c924ec05e4a","39de2d2d75e5429aa7561e35b3d1f7ad","94bb435c7e7348e59308c5779e74fae1","1b08c001cc744c8485e346b55c83ce39","039b73c684f94daa9f0fd5ad8eb1ab2c","e6a4955f461c495bafc43147b339272f","3f2196ca72094b7d9028a8700273393a","1a8704274c574d519e870babb584f9b7","d23f9fcd3664483fb82501466e8df5c2","69ba28aaa2bd44258af3472a83c8bf2a","2c60b621da63424e84814a1a18c33dd5","482ee831aad8464ca35e9a66f55e0c7f","845d1cb2521c4cf58aba5a60573c6625","be3d970fec1e4753b7845883393cd953"]},"id":"ZGUmCKJDJGsH","executionInfo":{"status":"ok","timestamp":1776961972219,"user_tz":-120,"elapsed":419,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"c0a43a04-959d-47f7-cbef-8f2d0df0cdb2"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_validators.py:205: UserWarning: The `local_dir_use_symlinks` argument is deprecated and ignored in `snapshot_download`. Downloading to a local directory does not use symlinks anymore.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (incomplete total...): 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0f5e759d53a64b6c9d08804321a17827"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Fetching 17 files:   0%|          | 0/17 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1b08c001cc744c8485e346b55c83ce39"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["📦 Snapshot complete: /content/llava_snapshot\n"]}]},{"cell_type":"code","source":["import torch, gc\n","from accelerate import load_checkpoint_and_dispatch\n","\n","print(\"🧹 Consolidating model to CPU (removing accelerate hooks)...\")\n","\n","model = load_checkpoint_and_dispatch(\n","    model,\n","    checkpoint=local_repo,\n","    device_map={\"\": \"cpu\"},   # force full CPU\n","    offload_folder=\"/content/offload\",\n","    offload_state_dict=True,\n","    dtype=torch.float16\n",")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"✅ Model fully materialized on CPU\")"],"metadata":{"id":"a-6SJs7nReqT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["base = model.model\n","\n","print(base.vision_tower is not None)\n","print(base.multi_modal_projector is not None)\n","print(base.language_model is not None)"],"metadata":{"id":"bdF6WIj-Rp79"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sdnq.loader import apply_sdnq_options_to_model\n","import gc, torch\n","\n","print(\"⚙️ Quantizing vision projector...\")\n","\n","base.multi_modal_projector = apply_sdnq_options_to_model(\n","    base.multi_modal_projector,\n","    use_quantized_matmul=True\n",")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"⚙️ Quantizing language model...\")\n","\n","base.language_model = apply_sdnq_options_to_model(\n","    base.language_model,\n","    use_quantized_matmul=True\n",")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"⚙️ Quantizing vision tower (optional but recommended)...\")\n","\n","base.vision_tower = apply_sdnq_options_to_model(\n","    base.vision_tower,\n","    use_quantized_matmul=True\n",")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"✅ SDNQ fully applied\")"],"metadata":{"id":"OXfYqbO6Rsiu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(\"STRUCTURE CHECK:\")\n","print(\"vision:\", hasattr(base, \"vision_tower\"))\n","print(\"projector:\", hasattr(base, \"multi_modal_projector\"))\n","print(\"llm:\", hasattr(base, \"language_model\"))"],"metadata":{"id":"CuTJ4brpRxC2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(\"🧨 Removing accelerate hooks...\")\n","\n","for m in base.modules():\n","    if hasattr(m, \"_hf_hook\"):\n","        delattr(m, \"_hf_hook\")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"✅ Hooks removed\")"],"metadata":{"id":"FAjCYlLkR04x"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["save_dir = \"/content/joycaption_sdnq\"\n","\n","model.save_pretrained(save_dir)\n","processor.save_pretrained(save_dir)\n","\n","print(\"📦 Clean SDNQ model saved:\", save_dir)"],"metadata":{"id":"KeWjShNgR5l4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# old attempt 2"],"metadata":{"id":"uej1AginRbCA"}},{"cell_type":"code","source":["from transformers import LlavaForConditionalGeneration, AutoConfig\n","import torch, gc\n","\n","model = LlavaForConditionalGeneration.from_pretrained(\n","    local_repo,\n","    torch_dtype=torch.float16,\n","    device_map=\"auto\",              # key fix\n","    offload_folder=\"/content/offload\",\n","    offload_state_dict=True,\n","    low_cpu_mem_usage=True\n",")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"💽 Model loaded with CPU offload (no distributed backend)\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":105,"referenced_widgets":["c19edd1a84334257af5de1b33bfca658","98e0c13cd55640ac975b4f6cb366f1e3","2a821ce9cd474cbd8eb3ec57fd28bcc8","4d253c7c8a2d4aa7895dd27618c32787","543d422ba9a44340bdd8a5074f9011e2","2e8f5356d827493693efdbbc139dcb04","f267e6e1304149b8861360c8dff5a5fd","719a1053d0bf4677a46a159d5c32c6fe","78104ffe509c4ee0bf438eefe402b49c","b0969094dfd74600a8323d2498399b08","6cbde6040af444daa9f14cc0a208bf28"]},"id":"XcZECPuLMc-B","executionInfo":{"status":"ok","timestamp":1776962182754,"user_tz":-120,"elapsed":78738,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"7a68e66c-6bbc-4881-aa76-7218993effe8"},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/743 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c19edd1a84334257af5de1b33bfca658"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:accelerate.big_modeling:Some parameters are on the meta device because they were offloaded to the cpu.\n"]},{"output_type":"stream","name":"stdout","text":["💽 Model loaded with CPU offload (no distributed backend)\n"]}]},{"cell_type":"code","source":["import gc, torch\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","# optional: ensure eval mode only (no graph mutation)\n","model.eval()\n","\n","print(\"🧊 Model stabilized (NO .to() used — accelerate hooks preserved)\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0cCFPi57NiqY","executionInfo":{"status":"ok","timestamp":1776962389805,"user_tz":-120,"elapsed":608,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"5773dfae-6332-4dc7-b125-24c2e0bc782b"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["🧊 Model stabilized (NO .to() used — accelerate hooks preserved)\n"]}]},{"cell_type":"code","source":["from sdnq.loader import apply_sdnq_options_to_model\n","import gc, torch\n","\n","print(\"⚙️ Quantizing multi-modal projector...\")\n","\n","model.model.multi_modal_projector = apply_sdnq_options_to_model(\n","    model.model.multi_modal_projector,\n","    use_quantized_matmul=True\n",")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"✅ Multi-modal projector SDNQ applied\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XRctPYmgOzqL","executionInfo":{"status":"ok","timestamp":1776962722407,"user_tz":-120,"elapsed":50,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"afac6de0-5513-4f20-d5f2-9e642ad3cf77"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["⚙️ Quantizing multi-modal projector...\n","✅ Multi-modal projector SDNQ applied\n"]}]},{"cell_type":"code","source":["from sdnq.loader import apply_sdnq_options_to_model\n","import gc, torch\n","\n","print(\"⚙️ Quantizing language model (LLM)...\")\n","\n","model.model.language_model = apply_sdnq_options_to_model(\n","    model.model.language_model,\n","    use_quantized_matmul=True\n",")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"✅ Language model SDNQ applied\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jO92M9bOPRHM","executionInfo":{"status":"ok","timestamp":1776962887959,"user_tz":-120,"elapsed":601,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"b4ad689e-3208-4411-d436-c1d81cde93e0"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["⚙️ Quantizing language model (LLM)...\n","✅ Language model SDNQ applied\n"]}]},{"cell_type":"code","source":["from sdnq.loader import apply_sdnq_options_to_model\n","import gc, torch\n","\n","print(\"⚙️ Quantizing vision tower (optional)...\")\n","\n","model.model.vision_tower = apply_sdnq_options_to_model(\n","    model.model.vision_tower,\n","    use_quantized_matmul=True\n",")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"✅ Vision tower SDNQ applied\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P1We3FTZPj0P","executionInfo":{"status":"ok","timestamp":1776962922678,"user_tz":-120,"elapsed":496,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"174a62c6-d3af-4f8a-9667-deb74e5afc1e"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["⚙️ Quantizing vision tower (optional)...\n","✅ Vision tower SDNQ applied\n"]}]},{"cell_type":"code","source":["print(model)\n","\n","print(\"\\n🔍 Key components check:\")\n","print(\"vision_tower:\", hasattr(model.model, \"vision_tower\"))\n","print(\"projector:\", hasattr(model.model, \"multi_modal_projector\"))\n","print(\"language_model:\", hasattr(model.model, \"language_model\"))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ms4cJN78PrYa","executionInfo":{"status":"ok","timestamp":1776962974638,"user_tz":-120,"elapsed":21,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"b5eb78d3-2e30-45f8-f2ff-9c2b94c70065"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["LlavaForConditionalGeneration(\n","  (model): LlavaModel(\n","    (vision_tower): SiglipVisionModel(\n","      (embeddings): SiglipVisionEmbeddings(\n","        (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid)\n","        (position_embedding): Embedding(729, 1152)\n","      )\n","      (encoder): SiglipEncoder(\n","        (layers): ModuleList(\n","          (0-26): 27 x SiglipEncoderLayer(\n","            (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)\n","            (self_attn): SiglipAttention(\n","              (k_proj): Linear(in_features=1152, out_features=1152, bias=True)\n","              (v_proj): Linear(in_features=1152, out_features=1152, bias=True)\n","              (q_proj): Linear(in_features=1152, out_features=1152, bias=True)\n","              (out_proj): Linear(in_features=1152, out_features=1152, bias=True)\n","            )\n","            (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)\n","            (mlp): SiglipMLP(\n","              (activation_fn): GELUTanh()\n","              (fc1): Linear(in_features=1152, out_features=4304, bias=True)\n","              (fc2): Linear(in_features=4304, out_features=1152, bias=True)\n","            )\n","          )\n","        )\n","      )\n","      (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)\n","      (head): SiglipMultiheadAttentionPoolingHead(\n","        (attention): MultiheadAttention(\n","          (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True)\n","        )\n","        (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)\n","        (mlp): SiglipMLP(\n","          (activation_fn): GELUTanh()\n","          (fc1): Linear(in_features=1152, out_features=4304, bias=True)\n","          (fc2): Linear(in_features=4304, out_features=1152, bias=True)\n","        )\n","      )\n","    )\n","    (multi_modal_projector): LlavaMultiModalProjector(\n","      (linear_1): Linear(in_features=1152, out_features=4096, bias=True)\n","      (act): GELUActivation()\n","      (linear_2): Linear(in_features=4096, out_features=4096, bias=True)\n","    )\n","    (language_model): LlamaModel(\n","      (embed_tokens): Embedding(128256, 4096)\n","      (layers): ModuleList(\n","        (0-31): 32 x LlamaDecoderLayer(\n","          (self_attn): LlamaAttention(\n","            (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n","            (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\n","            (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\n","            (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n","          )\n","          (mlp): LlamaMLP(\n","            (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\n","            (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\n","            (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\n","            (act_fn): SiLUActivation()\n","          )\n","          (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n","          (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n","        )\n","      )\n","      (norm): LlamaRMSNorm((4096,), eps=1e-05)\n","      (rotary_emb): LlamaRotaryEmbedding()\n","    )\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=128256, bias=False)\n",")\n","\n","🔍 Key components check:\n","vision_tower: True\n","projector: True\n","language_model: True\n"]}]},{"cell_type":"code","source":["import gc, torch\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","model.eval()\n","\n","print(\"🧊 Final stabilization complete\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-HHO_PbXP29W","executionInfo":{"status":"ok","timestamp":1776962999373,"user_tz":-120,"elapsed":2215,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"57a73abd-b7ec-4253-a57b-06856c6d72ed"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["🧊 Final stabilization complete\n"]}]},{"cell_type":"code","source":["save_dir = \"/content/joycaption_sdnq\"\n","\n","model.save_pretrained(save_dir)\n","\n","print(\"📦 SDNQ model saved to:\", save_dir)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":807,"referenced_widgets":["fa7744f876d7451c9777b76ebca32616","4e5f6bed196a4a458e2629c2085c01c4","4f3d37a9cfea47678a095224b622b7f3","bed58a7d96004da7bc12cbadd6db8427","b73c1b97b9aa4d88baa8ca1aeb9c198c","b03800fc872f4402b0ab4f8b03062011","0f8f5fad8cbf4309aef40873f76c5f05","66ca61d2377843dea87676f89b862884","8d9da1040a264f36a1895deb879e87e9","7fd0def766454e7d881b55b14980b840","e98f7b5949cb43e6a30034453ce2b165"]},"id":"PZPnBAptP8Kr","executionInfo":{"status":"error","timestamp":1776963025577,"user_tz":-120,"elapsed":584,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"fe607682-f3de-4c77-87f9-f0249b56f126"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/transformers/modeling_utils.py:3377: UserWarning: Attempting to save a model with offloaded modules. Ensure that unallocated cpu memory exceeds the `shard_size` (50GB default)\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fa7744f876d7451c9777b76ebca32616"}},"metadata":{}},{"output_type":"error","ename":"AttributeError","evalue":"LlavaForConditionalGeneration has no attribute `language_model`","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_2249/10942975.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msave_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/joycaption_sdnq\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"📦 SDNQ model saved to:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36msave_pretrained\u001b[0;34m(self, save_directory, is_main_process, state_dict, push_to_hub, max_shard_size, variant, token, save_peft_format, save_original_format, **kwargs)\u001b[0m\n\u001b[1;32m   3455\u001b[0m                 \u001b[0;31m# or something\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3456\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mis_offloaded\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"meta\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3457\u001b[0;31m                     \u001b[0mtensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_offloaded_parameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_to_save\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3459\u001b[0m                 \u001b[0;31m# only do contiguous after it's permuted correctly in case of TP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/integrations/accelerate.py\u001b[0m in \u001b[0;36mload_offloaded_parameter\u001b[0;34m(model, param_name)\u001b[0m\n\u001b[1;32m    521\u001b[0m     \u001b[0mmodules_to_check\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\".\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_parts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_parts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mparent_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules_to_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 523\u001b[0;31m         \u001b[0mparent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_submodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    524\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_hf_hook\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    525\u001b[0m             \u001b[0mweights_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mget_submodule\u001b[0;34m(self, target)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0matoms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m                 raise AttributeError(\n\u001b[0m\u001b[1;32m    729\u001b[0m                     \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" has no attribute `\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m                 )\n","\u001b[0;31mAttributeError\u001b[0m: LlavaForConditionalGeneration has no attribute `language_model`"]}]},{"cell_type":"markdown","source":["# Debug"],"metadata":{"id":"tjTeOgFYO2ao"}},{"cell_type":"code","source":["print(model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sQYD5GWtOElO","executionInfo":{"status":"ok","timestamp":1776962529272,"user_tz":-120,"elapsed":20,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"d688b5b4-2f2d-424d-a76c-2dc8eec9f350"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["LlavaForConditionalGeneration(\n","  (model): LlavaModel(\n","    (vision_tower): SiglipVisionModel(\n","      (embeddings): SiglipVisionEmbeddings(\n","        (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid)\n","        (position_embedding): Embedding(729, 1152)\n","      )\n","      (encoder): SiglipEncoder(\n","        (layers): ModuleList(\n","          (0-26): 27 x SiglipEncoderLayer(\n","            (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)\n","            (self_attn): SiglipAttention(\n","              (k_proj): Linear(in_features=1152, out_features=1152, bias=True)\n","              (v_proj): Linear(in_features=1152, out_features=1152, bias=True)\n","              (q_proj): Linear(in_features=1152, out_features=1152, bias=True)\n","              (out_proj): Linear(in_features=1152, out_features=1152, bias=True)\n","            )\n","            (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)\n","            (mlp): SiglipMLP(\n","              (activation_fn): GELUTanh()\n","              (fc1): Linear(in_features=1152, out_features=4304, bias=True)\n","              (fc2): Linear(in_features=4304, out_features=1152, bias=True)\n","            )\n","          )\n","        )\n","      )\n","      (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)\n","      (head): SiglipMultiheadAttentionPoolingHead(\n","        (attention): MultiheadAttention(\n","          (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True)\n","        )\n","        (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True)\n","        (mlp): SiglipMLP(\n","          (activation_fn): GELUTanh()\n","          (fc1): Linear(in_features=1152, out_features=4304, bias=True)\n","          (fc2): Linear(in_features=4304, out_features=1152, bias=True)\n","        )\n","      )\n","    )\n","    (multi_modal_projector): LlavaMultiModalProjector(\n","      (linear_1): Linear(in_features=1152, out_features=4096, bias=True)\n","      (act): GELUActivation()\n","      (linear_2): Linear(in_features=4096, out_features=4096, bias=True)\n","    )\n","    (language_model): LlamaModel(\n","      (embed_tokens): Embedding(128256, 4096)\n","      (layers): ModuleList(\n","        (0-31): 32 x LlamaDecoderLayer(\n","          (self_attn): LlamaAttention(\n","            (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n","            (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\n","            (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\n","            (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n","          )\n","          (mlp): LlamaMLP(\n","            (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\n","            (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\n","            (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\n","            (act_fn): SiLUActivation()\n","          )\n","          (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n","          (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n","        )\n","      )\n","      (norm): LlamaRMSNorm((4096,), eps=1e-05)\n","      (rotary_emb): LlamaRotaryEmbedding()\n","    )\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=128256, bias=False)\n",")\n"]}]},{"cell_type":"code","source":["print(model.config)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"psLbOadOOH-9","executionInfo":{"status":"ok","timestamp":1776962552149,"user_tz":-120,"elapsed":46,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"0ca44fc9-fea6-4f87-a10a-b34906c93190"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["LlavaConfig {\n","  \"architectures\": [\n","    \"LlavaForConditionalGeneration\"\n","  ],\n","  \"dtype\": \"float16\",\n","  \"image_seq_length\": 729,\n","  \"image_token_index\": 128077,\n","  \"model_type\": \"llava\",\n","  \"multimodal_projector_bias\": true,\n","  \"projector_hidden_act\": \"gelu\",\n","  \"text_config\": {\n","    \"_name_or_path\": \"meta-llama/Llama-3.1-8B-Instruct\",\n","    \"architectures\": [\n","      \"LlamaForCausalLM\"\n","    ],\n","    \"attention_bias\": false,\n","    \"attention_dropout\": 0.0,\n","    \"bos_token_id\": 128000,\n","    \"dtype\": \"float16\",\n","    \"eos_token_id\": [\n","      128001,\n","      128008,\n","      128009\n","    ],\n","    \"head_dim\": 128,\n","    \"hidden_act\": \"silu\",\n","    \"hidden_size\": 4096,\n","    \"initializer_range\": 0.02,\n","    \"intermediate_size\": 14336,\n","    \"max_position_embeddings\": 131072,\n","    \"mlp_bias\": false,\n","    \"model_type\": \"llama\",\n","    \"num_attention_heads\": 32,\n","    \"num_hidden_layers\": 32,\n","    \"num_key_value_heads\": 8,\n","    \"pad_token_id\": null,\n","    \"pretraining_tp\": 1,\n","    \"rms_norm_eps\": 1e-05,\n","    \"rope_parameters\": {\n","      \"factor\": 8.0,\n","      \"high_freq_factor\": 4.0,\n","      \"low_freq_factor\": 1.0,\n","      \"original_max_position_embeddings\": 8192,\n","      \"rope_theta\": 500000.0,\n","      \"rope_type\": \"llama3\"\n","    },\n","    \"tie_word_embeddings\": false,\n","    \"use_cache\": true,\n","    \"vocab_size\": 128256\n","  },\n","  \"tie_word_embeddings\": false,\n","  \"transformers_version\": \"5.6.1\",\n","  \"vision_config\": {\n","    \"_name_or_path\": \"google/siglip2-so400m-patch14-384\",\n","    \"architectures\": [\n","      \"SiglipVisionModel\"\n","    ],\n","    \"attention_dropout\": 0.0,\n","    \"dtype\": \"float16\",\n","    \"hidden_act\": \"gelu_pytorch_tanh\",\n","    \"hidden_size\": 1152,\n","    \"image_size\": 384,\n","    \"intermediate_size\": 4304,\n","    \"layer_norm_eps\": 1e-06,\n","    \"model_type\": \"siglip_vision_model\",\n","    \"num_attention_heads\": 16,\n","    \"num_channels\": 3,\n","    \"num_hidden_layers\": 27,\n","    \"patch_size\": 14\n","  },\n","  \"vision_feature_layer\": -2,\n","  \"vision_feature_select_strategy\": \"full\"\n","}\n","\n"]}]},{"cell_type":"code","source":["for name, module in model.named_modules():\n","    if \"proj\" in name or \"mm\" in name or \"vision\" in name:\n","        print(name)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J45h8q0GONmU","executionInfo":{"status":"ok","timestamp":1776962576359,"user_tz":-120,"elapsed":48,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"866576e0-a77b-43e3-c3c8-ed5ad7ce94dd"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["model.vision_tower\n","model.vision_tower.embeddings\n","model.vision_tower.embeddings.patch_embedding\n","model.vision_tower.embeddings.position_embedding\n","model.vision_tower.encoder\n","model.vision_tower.encoder.layers\n","model.vision_tower.encoder.layers.0\n","model.vision_tower.encoder.layers.0.layer_norm1\n","model.vision_tower.encoder.layers.0.self_attn\n","model.vision_tower.encoder.layers.0.self_attn.k_proj\n","model.vision_tower.encoder.layers.0.self_attn.v_proj\n","model.vision_tower.encoder.layers.0.self_attn.q_proj\n","model.vision_tower.encoder.layers.0.self_attn.out_proj\n","model.vision_tower.encoder.layers.0.layer_norm2\n","model.vision_tower.encoder.layers.0.mlp\n","model.vision_tower.encoder.layers.0.mlp.activation_fn\n","model.vision_tower.encoder.layers.0.mlp.fc1\n","model.vision_tower.encoder.layers.0.mlp.fc2\n","model.vision_tower.encoder.layers.1\n","model.vision_tower.encoder.layers.1.layer_norm1\n","model.vision_tower.encoder.layers.1.self_attn\n","model.vision_tower.encoder.layers.1.self_attn.k_proj\n","model.vision_tower.encoder.layers.1.self_attn.v_proj\n","model.vision_tower.encoder.layers.1.self_attn.q_proj\n","model.vision_tower.encoder.layers.1.self_attn.out_proj\n","model.vision_tower.encoder.layers.1.layer_norm2\n","model.vision_tower.encoder.layers.1.mlp\n","model.vision_tower.encoder.layers.1.mlp.activation_fn\n","model.vision_tower.encoder.layers.1.mlp.fc1\n","model.vision_tower.encoder.layers.1.mlp.fc2\n","model.vision_tower.encoder.layers.2\n","model.vision_tower.encoder.layers.2.layer_norm1\n","model.vision_tower.encoder.layers.2.self_attn\n","model.vision_tower.encoder.layers.2.self_attn.k_proj\n","model.vision_tower.encoder.layers.2.self_attn.v_proj\n","model.vision_tower.encoder.layers.2.self_attn.q_proj\n","model.vision_tower.encoder.layers.2.self_attn.out_proj\n","model.vision_tower.encoder.layers.2.layer_norm2\n","model.vision_tower.encoder.layers.2.mlp\n","model.vision_tower.encoder.layers.2.mlp.activation_fn\n","model.vision_tower.encoder.layers.2.mlp.fc1\n","model.vision_tower.encoder.layers.2.mlp.fc2\n","model.vision_tower.encoder.layers.3\n","model.vision_tower.encoder.layers.3.layer_norm1\n","model.vision_tower.encoder.layers.3.self_attn\n","model.vision_tower.encoder.layers.3.self_attn.k_proj\n","model.vision_tower.encoder.layers.3.self_attn.v_proj\n","model.vision_tower.encoder.layers.3.self_attn.q_proj\n","model.vision_tower.encoder.layers.3.self_attn.out_proj\n","model.vision_tower.encoder.layers.3.layer_norm2\n","model.vision_tower.encoder.layers.3.mlp\n","model.vision_tower.encoder.layers.3.mlp.activation_fn\n","model.vision_tower.encoder.layers.3.mlp.fc1\n","model.vision_tower.encoder.layers.3.mlp.fc2\n","model.vision_tower.encoder.layers.4\n","model.vision_tower.encoder.layers.4.layer_norm1\n","model.vision_tower.encoder.layers.4.self_attn\n","model.vision_tower.encoder.layers.4.self_attn.k_proj\n","model.vision_tower.encoder.layers.4.self_attn.v_proj\n","model.vision_tower.encoder.layers.4.self_attn.q_proj\n","model.vision_tower.encoder.layers.4.self_attn.out_proj\n","model.vision_tower.encoder.layers.4.layer_norm2\n","model.vision_tower.encoder.layers.4.mlp\n","model.vision_tower.encoder.layers.4.mlp.activation_fn\n","model.vision_tower.encoder.layers.4.mlp.fc1\n","model.vision_tower.encoder.layers.4.mlp.fc2\n","model.vision_tower.encoder.layers.5\n","model.vision_tower.encoder.layers.5.layer_norm1\n","model.vision_tower.encoder.layers.5.self_attn\n","model.vision_tower.encoder.layers.5.self_attn.k_proj\n","model.vision_tower.encoder.layers.5.self_attn.v_proj\n","model.vision_tower.encoder.layers.5.self_attn.q_proj\n","model.vision_tower.encoder.layers.5.self_attn.out_proj\n","model.vision_tower.encoder.layers.5.layer_norm2\n","model.vision_tower.encoder.layers.5.mlp\n","model.vision_tower.encoder.layers.5.mlp.activation_fn\n","model.vision_tower.encoder.layers.5.mlp.fc1\n","model.vision_tower.encoder.layers.5.mlp.fc2\n","model.vision_tower.encoder.layers.6\n","model.vision_tower.encoder.layers.6.layer_norm1\n","model.vision_tower.encoder.layers.6.self_attn\n","model.vision_tower.encoder.layers.6.self_attn.k_proj\n","model.vision_tower.encoder.layers.6.self_attn.v_proj\n","model.vision_tower.encoder.layers.6.self_attn.q_proj\n","model.vision_tower.encoder.layers.6.self_attn.out_proj\n","model.vision_tower.encoder.layers.6.layer_norm2\n","model.vision_tower.encoder.layers.6.mlp\n","model.vision_tower.encoder.layers.6.mlp.activation_fn\n","model.vision_tower.encoder.layers.6.mlp.fc1\n","model.vision_tower.encoder.layers.6.mlp.fc2\n","model.vision_tower.encoder.layers.7\n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uage_model.layers.19.mlp.up_proj\n","model.language_model.layers.19.mlp.down_proj\n","model.language_model.layers.20.self_attn.q_proj\n","model.language_model.layers.20.self_attn.k_proj\n","model.language_model.layers.20.self_attn.v_proj\n","model.language_model.layers.20.self_attn.o_proj\n","model.language_model.layers.20.mlp.gate_proj\n","model.language_model.layers.20.mlp.up_proj\n","model.language_model.layers.20.mlp.down_proj\n","model.language_model.layers.21.self_attn.q_proj\n","model.language_model.layers.21.self_attn.k_proj\n","model.language_model.layers.21.self_attn.v_proj\n","model.language_model.layers.21.self_attn.o_proj\n","model.language_model.layers.21.mlp.gate_proj\n","model.language_model.layers.21.mlp.up_proj\n","model.language_model.layers.21.mlp.down_proj\n","model.language_model.layers.22.self_attn.q_proj\n","model.language_model.layers.22.self_attn.k_proj\n","model.language_model.layers.22.self_attn.v_proj\n","model.language_model.layers.22.self_attn.o_proj\n","model.language_model.layers.22.mlp.gate_proj\n","model.language_model.layers.22.mlp.up_proj\n","model.language_model.layers.22.mlp.down_proj\n","model.language_model.layers.23.self_attn.q_proj\n","model.language_model.layers.23.self_attn.k_proj\n","model.language_model.layers.23.self_attn.v_proj\n","model.language_model.layers.23.self_attn.o_proj\n","model.language_model.layers.23.mlp.gate_proj\n","model.language_model.layers.23.mlp.up_proj\n","model.language_model.layers.23.mlp.down_proj\n","model.language_model.layers.24.self_attn.q_proj\n","model.language_model.layers.24.self_attn.k_proj\n","model.language_model.layers.24.self_attn.v_proj\n","model.language_model.layers.24.self_attn.o_proj\n","model.language_model.layers.24.mlp.gate_proj\n","model.language_model.layers.24.mlp.up_proj\n","model.language_model.layers.24.mlp.down_proj\n","model.language_model.layers.25.self_attn.q_proj\n","model.language_model.layers.25.self_attn.k_proj\n","model.language_model.layers.25.self_attn.v_proj\n","model.language_model.layers.25.self_attn.o_proj\n","model.language_model.layers.25.mlp.gate_proj\n","model.language_model.layers.25.mlp.up_proj\n","model.language_model.layers.25.mlp.down_proj\n","model.language_model.layers.26.self_attn.q_proj\n","model.language_model.layers.26.self_attn.k_proj\n","model.language_model.layers.26.self_attn.v_proj\n","model.language_model.layers.26.self_attn.o_proj\n","model.language_model.layers.26.mlp.gate_proj\n","model.language_model.layers.26.mlp.up_proj\n","model.language_model.layers.26.mlp.down_proj\n","model.language_model.layers.27.self_attn.q_proj\n","model.language_model.layers.27.self_attn.k_proj\n","model.language_model.layers.27.self_attn.v_proj\n","model.language_model.layers.27.self_attn.o_proj\n","model.language_model.layers.27.mlp.gate_proj\n","model.language_model.layers.27.mlp.up_proj\n","model.language_model.layers.27.mlp.down_proj\n","model.language_model.layers.28.self_attn.q_proj\n","model.language_model.layers.28.self_attn.k_proj\n","model.language_model.layers.28.self_attn.v_proj\n","model.language_model.layers.28.self_attn.o_proj\n","model.language_model.layers.28.mlp.gate_proj\n","model.language_model.layers.28.mlp.up_proj\n","model.language_model.layers.28.mlp.down_proj\n","model.language_model.layers.29.self_attn.q_proj\n","model.language_model.layers.29.self_attn.k_proj\n","model.language_model.layers.29.self_attn.v_proj\n","model.language_model.layers.29.self_attn.o_proj\n","model.language_model.layers.29.mlp.gate_proj\n","model.language_model.layers.29.mlp.up_proj\n","model.language_model.layers.29.mlp.down_proj\n","model.language_model.layers.30.self_attn.q_proj\n","model.language_model.layers.30.self_attn.k_proj\n","model.language_model.layers.30.self_attn.v_proj\n","model.language_model.layers.30.self_attn.o_proj\n","model.language_model.layers.30.mlp.gate_proj\n","model.language_model.layers.30.mlp.up_proj\n","model.language_model.layers.30.mlp.down_proj\n","model.language_model.layers.31.self_attn.q_proj\n","model.language_model.layers.31.self_attn.k_proj\n","model.language_model.layers.31.self_attn.v_proj\n","model.language_model.layers.31.self_attn.o_proj\n","model.language_model.layers.31.mlp.gate_proj\n","model.language_model.layers.31.mlp.up_proj\n","model.language_model.layers.31.mlp.down_proj\n"]}]},{"cell_type":"markdown","source":["# old attempt"],"metadata":{"id":"HzEf1oG8JCnt"}},{"cell_type":"code","source":["import torch, gc, os\n","from transformers import AutoProcessor, LlavaForConditionalGeneration\n","from accelerate import init_empty_weights, load_checkpoint_and_dispatch\n","from sdnq.loader import apply_sdnq_options_to_model\n","\n","os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","MODEL_ID = \"fancyfeast/llama-joycaption-beta-one-hf-llava\"\n","\n","# =========================================================\n","# ✅ LOAD PROCESSOR (REAL SOURCE)\n","# =========================================================\n","processor = AutoProcessor.from_pretrained(MODEL_ID)\n","\n","# =========================================================\n","# ✅ SAFE LOAD MODEL (NO OOM)\n","# =========================================================\n","with init_empty_weights():\n","    model = LlavaForConditionalGeneration.from_pretrained(\n","        MODEL_ID,\n","        torch_dtype=torch.float16\n","    )\n","\n","model = load_checkpoint_and_dispatch(\n","    model,\n","    MODEL_ID,\n","    device_map=\"auto\",\n","    offload_folder=\"/content/offload\",\n","    offload_state_dict=True,\n","    dtype=torch.float16\n",")\n","\n","print(\"✅ Model loaded safely\")\n","\n","# =========================================================\n","# ✅ APPLY SDNQ (CORRECT WAY)\n","# =========================================================\n","print(\"⚙️ Applying SDNQ...\")\n","\n","model.language_model = apply_sdnq_options_to_model(\n","    model.language_model,\n","    use_quantized_matmul=True\n",")\n","\n","model.multi_modal_projector = apply_sdnq_options_to_model(\n","    model.multi_modal_projector,\n","    use_quantized_matmul=True\n",")\n","\n","print(\"✅ SDNQ active\")\n","\n","# =========================================================\n","# ✅ MEMORY CONTROL\n","# =========================================================\n","if hasattr(model, \"vision_tower\"):\n","    model.vision_tower.to(\"cpu\")\n","\n","model.language_model.to(\"cuda\")\n","model.multi_modal_projector.to(\"cuda\")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"🚀 Ready\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["d0bce7d87bc34fdd99e90e2f2752e7fc","084b972e558a4e4085e02dd7b95a861f","b57ec777358c4a5f84f15486c416babe","717fa36b93234f02a029c9db2d47501f","a76141282f124684ae1f69ed459a89d9","715f422afe0a4b318a1d3ed8b7078f9a","b800b573e7984f2da69872dbfaf6f618","e6b71d106cac48258e70ef54479e6591","5041edccd7054cdda8282ba8127343b1","1f8f1ec6890645c49f167c3597a244ca","329f3e6cfb2e4aadab1b5c6e76ecb5c3","0ce79246c49845a9ab4f098794d8d10d","f1bf95199d934a16b6a12336b48947a8","74a83fe305de4fa59a0b95d264093e13","ba54440c2db54259ae9a5f0e04629222","600bb49bd0784329813bd4a9d16b2a76","057bcb84830b4402880e592796890209","9323fecccf724bc3b8667e71fe45380e","89439084de5a41a8a1c3eb32a25ccf88","dadf872489d54c69a696871ca5550918","def373cdd37f489087b1c2042d9990e0","677d6817804a4b0884337db32ad272de","5358989e90c14a249abfc40f36757a56","2cde250466e14f14a3f0ffa251dcfc87","d5b72926eaac47ed87b66bb41956de4e","144f0c8619b9463ba24e4d56933e604c","0d7e7e8eebd3434a81c71b51aca3a765","9719aaadcc0c4c6eb32d3556eead3cf4","8151db85bd8e488fa81fabb9da491061","6d7f8f70d8ae4b0f9ff6a2861f4deb0c","69c876a7d40d4539a409ac3f283cbb6e","e862ab25340c40e6b0c93dec50dfee3c","ff7905a1bd764c9e97b0b86659475bca","b9097b9f039e42ddae0009ca57091899","6d453e24b0114623b040341dd1aae383","120b94c0f6df4f798744aabb89e32016","b506515d848e423bab2d4aea0cc07de9","a70e80c02cd04c6d83119d1d2a2fd247","916572c2693442c281d4661a1a0348c4","0a04750940c343afbb4d6e4b224635f6","15f4712002e84831bed5bd8ea14615ec","a1ab58a0248540c38345bb563e832a2a","ec5f2e136ae44bbb890605e93bc68f98","0e7443715181460fba302bc667dbd7ff","01ab0db81ef94172b9a971c894f4eaac","5b8d37c7789e42408b82c4666192fc3f","c3a2080a3e4d446597a1d08b4a95ad0e","3b9e838130be4276bafc9e0f0b7ab152","55a40f204f4e4e40b1895ff8ec266b2b","cd068fde95634a258b59fc78c6907486","cfe5377b11974241b766b708c793d090","892a7d6bf40143b7ba03f4ce929f075a","c2220e7345e94621a0cd88b6cc8d814c","625731122e2e4a9db867e8598faf9567","56a672b206124e6d9c00bba4109290b1","0b4b87470d55496a9355aa6f6411c08e","8e35ac89a7ed4075bcb6ef6c6900589e","6b650895222c4a8699b66f7696bc2ec5","1c37e6c10f5645098cc891dcdda94f68","7f111df038f74e5490112ffa84ab4c1b","32e005d820e24a01ae2045624175cd1d","466b93a0e88e48428cd58f24d1fd720c","e64f63ebf3df404daa614510fa6e07b5","0d0ac5aad837441f91dcbe028e0fbea0","9e7dc0f5394e4008a1665874c4aac64d","5d419074881347a4a790c07a3510a62d","1761f94e04ea4e899b5644ae326ef2a1","c5d8558469eb49ebadefb7feefad46cb","55c7af0263ea465eae0aa0d249e5b0cf","1e96ad7ae13f4f4e94b39d6d41b075db","c0049fc5602440f08d479c03b295b63b","d993ba3b3fbe4233afd4f8fb9e329e5d","549326b42a3f4a7ab35c49ee8c121620","17d3173643df4cf6afd5230a0d585019","b7d728a87e16469da2eb542a98f3245a","2ce21f75e3fc4c1683c152e0c6fe4779","fe56f479e9484e75913f16c10f5f1ab1","d4b2e913b342465b9c001a06c016cc23","6f5d8074399548ae9771ccb56716fe97","32c386b95c7748669f8aabbe6939a864","87f8652d92c3445b8139e5dc42ca4b0d","21ea9f7732864331b98c581f94f75256","94eadf240a33474f86a5da210e37f106","cc30e4a4b22f4fa08264bd7cf9ab04cc","a3cf59ba4211448491851f9d1f31f4ed","a19482b502aa4640b954c241dadfcce8","8deb413b63b741459a241e16be213191","51c7ed1f5e9340a89a514ab3cf4adfec","4ff32c47dce2418aa15b227536e7ebbb","eeb7525a0e234f5ca98d544017f8c9f4","3f488c70f8ca416481ff3bb06c070aac","5bf0a1b72ebf42c8b0fa360c29a80c75","6602733704004a92a773d73f5e57a40a","ab1cdc63f150462f84bc53285a858868","623ba5e4c49c48149b849442018001bb","d5a87208f50c48b1ab333e604156867c","9f87219f4e424035b6354f52ceaa0caf","cb5b9e84b3674b549c80c6dc8e9c36d3","77576bde0751444a868a91caea6d2d67","b73e7a00062f487c99c038069fced808","6e718517f1fd4fda9e0c93f4d5fd602c","8fbf04c8b90447b69a19503b26088dd2","4b70bd8af28a4c5186af2ee275067a4c","aac0e52ae17c402787b1a370780a99ed","5ed65c8860e240c29fb403cff355762f","406b71bd2f024f21b89bb18dd72f332f","a19b059a51984958ba3d196f195ad34a","1dc35d82cf4a459998146345e5c81cd1","0ba1f7f3bdce4c9bb255e2afab5c8ecb","17f0403b1b664c51ab635bcdcc328b94","5d905de536b346168bb6fe999a901842","f92b6e698d35471481d487881add453d","d2722aff06154be5967c981909d0ed75","7a2786ca771340dfb8777d6b2382657c","6630e8effb1447c2bf83802d325f1d16","06a5164ff162468ea52c237172be4e90","4c12873d19624ac4938fa40ec2dedd3e","74cfc7199f8f4ab5b50a8dcf9ded7d93","f1cddc566f7a4e2e839232ef80bd52ec","a05eecae649741f9ac621ed56e209e64","4a7f2c68aeae49328d0e5c66dabab106","107d3232c4a640fbbf7c1d265ba7d3e7","32a35837a619402a9b079e43c3b2087f","dde707c2b89941e6bc3ec957a5176617","02896b02fa9d45d5888c911ba014eb2b","6953a2dc83814ca8a43fef7a517d66a5","f5d7861d3a1c4db3b3fa73a838397735","13229614e62741ffbd641b88ee101ed5","a00e61c253f440d598c2c41907708394","374486a38c0647709e9c7fd8816db175","9ae4d20fc3744943a5982fb02d71aa67","cc0af602e2934b53b93a9e2b521f043a"]},"id":"fVEo9JXzDkKj","executionInfo":{"status":"error","timestamp":1776960296336,"user_tz":-120,"elapsed":518098,"user":{"displayName":"Nnekos4Lyfe","userId":"11521490251288716219"}},"outputId":"f6633ae8-c4dd-4a1a-ea8c-727846ce13b6"},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":["processor_config.json: 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0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"01ab0db81ef94172b9a971c894f4eaac"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0b4b87470d55496a9355aa6f6411c08e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/296 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1761f94e04ea4e899b5644ae326ef2a1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d4b2e913b342465b9c001a06c016cc23"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (incomplete total...): 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4ff32c47dce2418aa15b227536e7ebbb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b73e7a00062f487c99c038069fced808"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/743 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5d905de536b346168bb6fe999a901842"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/219 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"107d3232c4a640fbbf7c1d265ba7d3e7"}},"metadata":{}},{"output_type":"error","ename":"ValueError","evalue":"`checkpoint` should be the path to a file containing a whole state dict, or the index of a sharded checkpoint, or a folder containing a sharded checkpoint or the whole state dict, but got fancyfeast/llama-joycaption-beta-one-hf-llava.","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_393/1583328572.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m     )\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m model = load_checkpoint_and_dispatch(\n\u001b[0m\u001b[1;32m     28\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0mMODEL_ID\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/accelerate/big_modeling.py\u001b[0m in \u001b[0;36mload_checkpoint_and_dispatch\u001b[0;34m(model, checkpoint, device_map, max_memory, no_split_module_classes, offload_folder, offload_buffers, dtype, offload_state_dict, skip_keys, preload_module_classes, force_hooks, strict, full_state_dict, broadcast_from_rank0)\u001b[0m\n\u001b[1;32m    636\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moffload_state_dict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdevice_map\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"disk\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdevice_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m         \u001b[0moffload_state_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 638\u001b[0;31m     load_checkpoint_in_model(\n\u001b[0m\u001b[1;32m    639\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0mcheckpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/accelerate/utils/modeling.py\u001b[0m in \u001b[0;36mload_checkpoint_in_model\u001b[0;34m(model, checkpoint, device_map, offload_folder, dtype, offload_state_dict, offload_buffers, keep_in_fp32_modules, offload_8bit_bnb, strict, full_state_dict, broadcast_from_rank0)\u001b[0m\n\u001b[1;32m   1900\u001b[0m                 )\n\u001b[1;32m   1901\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1902\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m   1903\u001b[0m             \u001b[0;34m\"`checkpoint` should be the path to a file containing a whole state dict, or the index of a sharded \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1904\u001b[0m             \u001b[0;34mf\"checkpoint, or a folder containing a sharded checkpoint or the whole state dict, but got {checkpoint}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: `checkpoint` should be the path to a file containing a whole state dict, or the index of a sharded checkpoint, or a folder containing a sharded checkpoint or the whole state dict, but got fancyfeast/llama-joycaption-beta-one-hf-llava."]}]},{"cell_type":"code","source":["from huggingface_hub import create_repo, upload_folder\n","import os\n","\n","repo_id = \"codeShare/Joycaption-Beta-One-SNDQ\"\n","local_dir = \"/content/joycaption_sdnq\"\n","\n","# Save locally first\n","model.save_pretrained(local_dir)\n","processor.save_pretrained(local_dir)\n","\n","print(\"📦 Model saved locally\")\n","\n","# Create repo (won't fail if it already exists)\n","create_repo(\n","    repo_id=repo_id,\n","    token=hf_token,\n","    exist_ok=True,\n","    private=False  # change to True if you want private\n",")\n","\n","print(f\"🚀 Uploading to HF: {repo_id}\")\n","\n","upload_folder(\n","    repo_id=repo_id,\n","    folder_path=local_dir,\n","    path_in_repo=\"\",\n","    commit_message=\"Add SDNQ quantized JoyCaption Beta One\",\n","    token=hf_token\n",")\n","\n","print(\"✅ Upload complete!\")"],"metadata":{"id":"FBmfKcLD-J-j"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Inferance"],"metadata":{"id":"PCf0zIHNC4ZI"}},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"FQF71-mvmlc1"},"outputs":[],"source":["# ================================================\n","#@title 🔌 Auto Disconnect Colab Session\n","# ================================================\n","\n","enable = False #@param {type:'boolean'}\n","if enable:\n","  print(\"🔌 Disconnecting Colab session in 3 seconds...\")\n","  import time\n","  time.sleep(3)\n","\n","  from google.colab import runtime\n","  runtime.unassign()\n","\n","  print(\"Session disconnected.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/"},"id":"OXGhsHQwWue_","outputId":"5473fc3e-6605-4e11-b742-b85c1a80f6d3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n","🧹 Removing old diffusers...\n","🔄 Installing latest diffusers...\n","  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"]}],"source":["# =============================================================================\n","#@markdown # **CELL 1**: Mount Drive + HF auth\n","# =============================================================================\n","\n","from google.colab import drive, userdata\n","from huggingface_hub import login\n","import torch\n","import os\n","import gc\n","import shutil\n","\n","drive.mount('/content/drive')\n","\n","hf_token = userdata.get('HF_TOKEN')\n","if hf_token:\n","    login(token=hf_token)\n","else:\n","    print(\"⚠️ No HF_TOKEN found in secrets.\")\n","\n","print(\"🧹 Removing old diffusers...\")\n","!pip uninstall -y diffusers > /dev/null 2>&1\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers* ~/.cache/pip/*diffusers*\n","\n","print(\"🔄 Installing latest diffusers...\")\n","!pip install -q git+https://github.com/huggingface/diffusers.git --force-reinstall --no-deps\n","!python -m pip cache purge\n","\n","print(\"✅ Cell 1 complete!\")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"K13pRro0lxAw"},"outputs":[],"source":["#@title Set path to zip file on your drive , and set klein edit prompt\n","input_zip_path = '/content/drive/MyDrive/damz.zip' #@param {type:'string'}\n","zip_path = input_zip_path\n","edit_prompt = 'describe this image' #@param }¥{type:'string'}"]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 2**: Fixed settings\n","# =============================================================================\n","\n","use_txt_prompts = False #@param {type:\"boolean\"}\n","debug = False #@param {type:\"boolean\"}\n","\n","max_new_tokens = 128 #@param {type:\"slider\", min:32, max:512, step:32}\n","temperature = 0.7 #@param {type:\"slider\", min:0.1, max:1.5, step:0.1}\n","\n","print(\"✅ Settings loaded\")\n","print(f\"   Use .txt prompts: {use_txt_prompts}\")\n","print(f\"   Debug mode: {debug}\")\n","print(f\"   Max tokens: {max_new_tokens}\")"],"metadata":{"id":"Z8TEHrQD_qQv"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file