diff --git "a/gemma_image_captioner.ipynb" "b/gemma_image_captioner.ipynb" --- "a/gemma_image_captioner.ipynb" +++ "b/gemma_image_captioner.ipynb" @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","source":["This notebook creates captions from images using a lora adaptation of google gemma 3 LLM. \n","\n","This lora is at present trained on 34 epochs of my own 524 Chroma prompts + 200 prompts by other users in Chroma , with a female+scifi focused blend of anime/furry/photorealistic for best coverage.\n","\n","Created by Adcom: https://tensor.art/u/754389913230900026"],"metadata":{"id":"HbBHYqQY8iHH"}},{"cell_type":"markdown","metadata":{"id":"529CsYil1qc6"},"source":["### Installation"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"9vJOucOw1qc6","executionInfo":{"status":"ok","timestamp":1754863623404,"user_tz":-120,"elapsed":28095,"user":{"displayName":"","userId":""}}},"outputs":[],"source":["%%capture\n","import os\n","if \"COLAB_\" not in \"\".join(os.environ.keys()):\n"," !pip install unsloth\n","else:\n"," # Do this only in Colab notebooks! Otherwise use pip install unsloth\n"," !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n"," !pip install sentencepiece protobuf \"datasets>=3.4.1,<4.0.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n"," !pip install --no-deps unsloth"]},{"cell_type":"code","source":["if True:\n"," from unsloth import FastVisionModel\n","\n"," model, processor = FastVisionModel.from_pretrained(\n"," model_name='codeShare/flux_chroma_image_captioner', # YOUR MODEL YOU USED FOR TRAINING\n"," load_in_4bit=True, # Set to False for 16bit LoRA\n"," )\n"," FastVisionModel.for_inference(model) # Enable for inference!"],"metadata":{"id":"9yu3CI6SsjN7","executionInfo":{"status":"ok","timestamp":1754863774816,"user_tz":-120,"elapsed":151408,"user":{"displayName":"","userId":""}},"outputId":"8e6682bd-71de-4122-d070-3ac40a116daf","colab":{"base_uri":"https://localhost:8080/","height":555,"referenced_widgets":["2b5ba0ac34394e20b7c4a763751fe2bd","ef235234e85f4d3fa0e260ad91d4c32e","c8cf2a4e798e4545a00dda78b6f97d7d","a2dd22e06d9b4f2389bc4a68e414d6c0","8a3bf65aca01427eb173d593f5d36ec4","8bc9480469e24b6489153bf26619d1c1","ecb00939b45344fa9138ad87e3bcd65f","b99885e7e5e64fc88b902b317c176dcb","9557f22ff6c745c2af1ad5d72551dea5","eab79017bff642d38f6740d378f08457","cb7f86df5c9042bfa31607502ffb5b87","b4f9210ef97d433b949736137df64ee2","906866fef8ff4a72ac8691a8303a1b35","fc572cba39a040c7871726c28e5f6ff5","d66080776a3743c58091e3744707d375","1a005d8db0a24d61a40f1e0a7af85e37","8c1fcddd17b94ac2ae95949ab2607de7","883f6c24608c41e49479368cc67c5918","3afafa365b7d4674894babb72c7e335f","851f29ec64eb4fde99397750ba1abc01","a2683ea442604881ae0d393b7828d750","38ea304db8ed4af2947132b06d0d4de1","1f1ebac837f7486f883dae924aaadda3","d0e7a374449b4afd84f6b15c7cf5d438","02cd4a9056b940f185e3e1dfac94efdc","53809099428a4a92b9ca3d5d690076b3","d229575c5fb644afb640588df1371410","64fb9b2c27a449c0a216917584548c0e","ea0a7fdbd6814b64886dc8212b32a307","e3a6f976ead9484283eac5aff0aac358","8a09bfad12bd4a4c9199eb98035201ff","eaac5b560bcd4379bd9e903b49ac5fb4","a69cc7f02e324e968b2b034aafef73a5","a90f7d3e689e40a8afa1a58acf78c2bc","dca103fa2b3a4c8aa3685a475e6a19fc","1a40a3ca4563489cbebf6d7394ef3a14","a03f93cc70a348939acc8d59823cd6cc","a26163254f8f4ec3b929d9e0df05b610","12b86c2927ae4e21840e19f222e17009","02492edda0ae41c196cca7f70f4aa79a","83e9562e0f2b4cf0b8b9356390e510a4","8184f47e25c04e4888a8c40c466e186e","c351abe63ca14f4c8752ca4dc5a1bfd2","e71ebb59e3194273a97e91914dacb939","896fee3bf87d4ac1b993585b53f727d9","9b7f2d557d314783b33fd1c824503eef","ca653ec1a8db40f299fe5424420de55e","04efb3cfb13943cea25be4578261c8f8","d40e3537bf2649bcaddebcdb79016919","0fe7e3c63deb4683b2606bde28c6f775","06f3dc51ac9140e690e26b3bdccf9210","5aee8c697f6446c9ab760e205dbb20cd","9c9a6e8abd4b46179cfb1a6b7ded6710","e3205f11410c467aa0aed4b332f6e9ba","3b977e16719d48218cf19abc5f1312b7","c911da21b1f84908a8ae9699a6d91d8b","707f9a10e1524070b8ae0fcd11904714","960ae352a08e4c6db321fa74dea0674b","363b80c126bb472b8ed6b40d8194b7b9","8006f95ba3a14520ad52b20810d4c04a","d2630ceeaddb4df2a02218425c708e51","9c1015f7f6fb458cbc2f2e76ae17c3bb","9d058793ab3e499db799a16dc20eaa12","398a62b57a934030a27af5469cefe8bd","48ba5183b515448c8504804cde2e052b","5419e4b666304f97b3af2df03133326d","88a69bbbb2da48eca1a4297c8b1f5f9c","1196c40003af491da7b30dc361b7f5d7","f96a21fb9376471bbdeb191c23e4476c","f0e34e03da914c63bc9623d8a0d9287c","e1e6149bddc3486f988253e0dbcd06ec","cdb408bc1b3243548a465563d4a58ef7","c32280cb52c44ccca73fc716a19da63c","5037d4ed38704d2381c5549be72cf080","d613e2be8e3b4e7ba603337932d7b83a","b6cb75fa1c9c4e289e0af51691aacdb2","b882930b398843b99226ee2282f79d1c","63560b62ec1847a285c2519e0c3fbcf2","5a9546059e554a248c8cf26ca133d5d8","0d59c481d1fe4464844d96f9028b680d","9fab5b7edf714a2fae28dc41e38de48e","4c81c3ec831149ffa3cf2132918200ca","3491ca6b91a142b88df01add3bc8f0fc","b05c69ef28e54f2780579b083ffa0e07","7bac23fa79974ced887bff0ffc409459","982e01b73f73487da61f07efe830b429","3a8d8a87ee2a4ac097ca6259a11e0d05","babac0fbf140415383548966f9b05632","745bf67ca9024c22a63fa0df2882ab8d","482cd967741e4df293fd53831cd201e4","4c25d065ad114589af9a176be65a7e66","57b2ea3211e140d7ba167f45a3e1e7c4","7b6b8a47d0534c258427466a3c364a9a","100632abf3644b09b97c9c1374ade6b3","fc916365b9644472a62ddb3758631ff3","c8ce27008fa544eab440a34bd9dc22ee","c8fdb90892d045799352faa720b4c7ea","f52c6b10751d4855bacecd3af53548be","cd8639fb5d6944d29923a26f82fdee0f","fc1ba32255594923a9f0acfc92dc473e","ec712f4540854653ab1c3cc7c0944fe8","9d76d912b5844ea0b30aac965856399c","c22aebf5257f4451a9cfa64d88818540","de003cd2e51c49e6bbf9e6b5aac75459","bccf32c085f140b681fb2bd4f7081f14","bc1233d4622a43458e730b358aa4d391","ada98434bb4f4b9db6d0c2e726d3328d","4f0abbcb2b8e4d078f41d60da39a7646","674a6d8eca904ad184db3b4b7e1ffc06","0fbaa7761e674776819da83ed6faa9d5"]}},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["πŸ¦₯ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n","πŸ¦₯ Unsloth Zoo will now patch everything to make training faster!\n","==((====))== Unsloth 2025.8.4: Fast Gemma3 patching. Transformers: 4.55.0.\n"," \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n","O^O/ \\_/ \\ Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n","\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]\n"," \"-____-\" Free license: http://github.com/unslothai/unsloth\n","Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n","Unsloth: Using float16 precision for gemma3 won't work! Using float32.\n"]},{"output_type":"stream","name":"stderr","text":[":37: UserWarning: You passed `quantization_config` or equivalent parameters to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` from the model will be used.\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors: 0%| | 0.00/4.38G [00:00-1: continue\n"," #while os.path.exists(f'{tgt_folder}{num}.txt'):num = num+1\n"," print(filename)\n"," %cd {src_folder}\n"," textpath = filename.replace(suffix,'.txt')\n"," #os.remove(f'{filename}')\n"," #continue\n"," image = Image.open(f\"{filename}\").convert('RGB')\n"," w,h=image.size\n"," #grid = product(range(0, h-h%d, d), range(0, w-w%d, d))\n"," divs=no_parts\n"," step=math.floor(w/divs)\n"," for index in range(divs):\n"," %cd {split_folder}\n"," box = (step*index, 0 ,step*(index+1),math.floor(1.0*h))\n"," image.crop(box).save(f'{num}_{index}.jpeg','JPEG')\n"," %cd /content/\n"," if os.path.exists(textpath):\n"," with open(f'{textpath}', 'r') as file:\n"," _tags = file.read()\n","\n"," print(_tags)\n"," if not _tags:continue\n"," tags=''\n"," _tags = [item.strip() for item in f'{_tags}'.split(',')]\n"," random.shuffle(_tags)\n"," for tag in _tags:\n"," tags = tags + tag + ' , '\n"," #----#\n"," tags = (tags + 'AAAA').replace(' , AAAA','')\n"," prompt_str = f' {tags}'\n"," %cd {split_folder}\n"," f = open(f'{num}_{index}.txt','w')\n"," f.write(f'{prompt_str}')\n"," f.close()\n"," #---#\n"," #-----#\n"," #----#\n"," num = num+1\n"," #caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n"," #print(f\"...\\n\\n...caption for {filename}\\n\\n...\")\n"," #print(caption)\n"," #---------#\n"," #f = open(f\"{num}.txt\", \"w\")\n"," #f.write(f'{caption}')\n"," #f.close()\n"," #input_image.save(f'{num}.jpeg', \"JPEG\")\n"," os.remove(f\"{src_folder}{filename}\")\n"," if 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\ No newline at end of file +{"cells":[{"cell_type":"markdown","source":["This notebook creates captions from images using a lora adaptation of google gemma 3 LLM. \n","\n","This lora is at present trained on 34 epochs of my own 524 Chroma prompts + 200 prompts by other users in Chroma , with a female+scifi focused blend of anime/furry/photorealistic for best coverage.\n","\n","Created by Adcom: https://tensor.art/u/754389913230900026"],"metadata":{"id":"HbBHYqQY8iHH"}},{"cell_type":"markdown","metadata":{"id":"529CsYil1qc6"},"source":["### Installation"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"9vJOucOw1qc6","executionInfo":{"status":"ok","timestamp":1754863623404,"user_tz":-120,"elapsed":28095,"user":{"displayName":"","userId":""}}},"outputs":[],"source":["%%capture\n","import os\n","if \"COLAB_\" not in \"\".join(os.environ.keys()):\n"," !pip install unsloth\n","else:\n"," # Do this only in Colab notebooks! Otherwise use pip install unsloth\n"," !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n"," !pip install sentencepiece protobuf \"datasets>=3.4.1,<4.0.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n"," !pip install --no-deps unsloth"]},{"cell_type":"code","source":["if True:\n"," from unsloth import FastVisionModel\n","\n"," model, processor = FastVisionModel.from_pretrained(\n"," model_name='codeShare/flux_chroma_image_captioner', # YOUR MODEL YOU USED FOR TRAINING\n"," load_in_4bit=True, # Set to False for 16bit LoRA\n"," )\n"," FastVisionModel.for_inference(model) # Enable for inference!"],"metadata":{"id":"9yu3CI6SsjN7"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":3,"metadata":{"id":"bEzvL7Sm1CrS","executionInfo":{"status":"ok","timestamp":1754863774819,"user_tz":-120,"elapsed":1,"user":{"displayName":"","userId":""}}},"outputs":[],"source":["from unsloth import get_chat_template\n","\n","processor = get_chat_template(\n"," processor,\n"," \"gemma-3\"\n",")"]},{"cell_type":"markdown","source":["A prompt to upload an image for processing will appear when running this cell"],"metadata":{"id":"DmbcTDgq8Bjg"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"oOyy5FUh8fBi"},"outputs":[],"source":["# Step 1: Import required libraries\n","from PIL import Image\n","import io\n","import torch\n","from google.colab import files # For file upload in Colab\n","\n","# Step 2: Assume model and processor are already loaded and configured\n","FastVisionModel.for_inference(model) # Enable for inference!\n","\n","# Step 3: Upload image from user\n","print(\"Please upload an image file (e.g., .jpg, .png):\")\n","uploaded = files.upload() # Opens a file upload widget in Colab\n","\n","# Step 4: Load the uploaded image\n","if not uploaded:\n"," raise ValueError(\"No file uploaded. Please upload an image.\")\n","\n","# Get the first uploaded file\n","file_name = list(uploaded.keys())[0]\n","try:\n"," image = Image.open(io.BytesIO(uploaded[file_name])).convert('RGB')\n","except Exception as e:\n"," raise ValueError(f\"Error loading image: {e}\")\n","\n","# Step 5: Define the instruction\n","instruction = \"Describe this image in 400 words\"\n","\n","# Step 6: Prepare messages for the model\n","messages = [\n"," {\n"," \"role\": \"user\",\n"," \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n"," }\n","]\n","\n","# Step 7: Apply chat template and prepare inputs\n","input_text = processor.apply_chat_template(messages, add_generation_prompt=True)\n","inputs = processor(\n"," image,\n"," input_text,\n"," add_special_tokens=False,\n"," return_tensors=\"pt\",\n",").to(\"cuda\")\n","\n","# Step 8: Generate output with text streaming\n","from transformers import TextStreamer\n","\n","text_streamer = TextStreamer(processor, skip_prompt=True)\n","result = model.generate(\n"," **inputs,\n"," streamer=text_streamer,\n"," max_new_tokens=200,\n"," use_cache=True,\n"," temperature=0.3,\n"," top_p=0.95,\n"," top_k=64\n",")"]},{"cell_type":"markdown","source":["<---- Upload a set if images to /content/ prior to running this cell. You can also open a .zip file and rename the folder with images as '/content/input'"],"metadata":{"id":"CrqNw_3O7np5"}},{"cell_type":"code","source":["# Step 1: Import required libraries\n","from PIL import Image\n","import torch\n","import os\n","from pathlib import Path\n","\n","# Step 2: Assume model and processor are already loaded and configured\n","FastVisionModel.for_inference(model) # Enable for inference!\n","\n","# Step 3: Define input and output directories\n","input_dirs = ['/content/', '/content/input/']\n","output_dir = '/content/output/'\n","\n","# Create output directory if it doesn't exist\n","os.makedirs(output_dir, exist_ok=True)\n","\n","# Step 4: Define supported image extensions\n","image_extensions = {'.jpg', '.webp', '.jpeg', '.png', '.bmp', '.gif'}\n","\n","# Step 5: Collect all image files from input directories\n","image_files = []\n","for input_dir in input_dirs:\n"," if os.path.exists(input_dir):\n"," for file in Path(input_dir).rglob('*'):\n"," if file.suffix.lower() in image_extensions:\n"," image_files.append(file)\n"," else:\n"," print(f\"Directory {input_dir} does not exist, skipping...\")\n","\n","if not image_files:\n"," raise ValueError(\"No images found in /content/ or /content/input/\")\n","\n","# Step 6: Define the instruction\n","instruction = \"Describe this image.\"\n","\n","# Step 7: Process each image\n","for image_path in image_files:\n"," try:\n"," # Load image\n"," image = Image.open(image_path).convert('RGB')\n","\n"," # Prepare messages for the model\n"," messages = [\n"," {\n"," \"role\": \"user\",\n"," \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n"," }\n"," ]\n","\n"," # Apply chat template and prepare inputs\n"," input_text = processor.apply_chat_template(messages, add_generation_prompt=True)\n"," inputs = processor(\n"," image,\n"," input_text,\n"," add_special_tokens=False,\n"," return_tensors=\"pt\",\n"," ).to(\"cuda\")\n","\n"," # Generate output without streaming\n"," print(f\"\\nProcessing {image_path.name}...\")\n"," result = model.generate(\n"," **inputs,\n"," max_new_tokens=200,\n"," use_cache=True,\n"," temperature=0.5,\n"," top_p=0.95,\n"," top_k=64\n"," )\n","\n"," # Decode the generated text\n"," caption = processor.decode(result[0], skip_special_tokens=True).strip()\n","\n"," # Print caption with extra whitespace for easy selection\n"," print(f\"\\n=== Caption for {image_path.name} ===\\n\\n{caption}\\n\\n====================\\n\")\n","\n"," # Save image and caption\n"," output_image_path = os.path.join(output_dir, image_path.name)\n"," output_caption_path = os.path.join(output_dir, f\"{image_path.stem}.txt\")\n","\n"," # Copy original image to output directory\n"," image.save(output_image_path)\n","\n"," # Save caption to text file\n"," with open(output_caption_path, 'w') as f:\n"," f.write(caption)\n","\n"," print(f\"Saved image and caption for {image_path.name}\")\n","\n"," # Delete the original image if it's in /content/ (but not /content/input/)\n"," if str(image_path).startswith('/content/') and not str(image_path).startswith('/content/input/'):\n"," try:\n"," os.remove(image_path)\n"," print(f\"Deleted original image: {image_path}\")\n"," except Exception as e:\n"," print(f\"Error deleting {image_path}: {e}\")\n","\n"," except Exception as e:\n"," print(f\"Error processing {image_path.name}: {e}\")\n","\n","print(f\"\\nProcessing complete. Output saved to {output_dir}\")"],"metadata":{"id":"MQAp389z30Jd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# @markdown πŸ’Ύ Create .zip file of output to /content/\n","output_filename ='' #@param {type:'string'}\n","if output_filename.strip()=='':\n"," output_filename = 'chroma_prompts.zip'\n","#-----#\n","import shutil\n","shutil.make_archive('chroma_prompts', 'zip', 'output')\n","\n"],"metadata":{"id":"vfOXO0uB5pJ0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","# @markdown 🧹Clear all images/.txt files/.zip files from /content/\n","import os\n","from pathlib import Path\n","\n","# Define the directory to clean\n","directory_to_clean = '/content/'\n","\n","# Define supported image and text extensions\n","extensions_to_delete = {'.zip','.webp' ,'.jpg', '.jpeg', '.png', '.bmp', '.gif', '.txt'}\n","\n","# Iterate through files in the directory and delete those with specified extensions\n","for file in Path(directory_to_clean).iterdir():\n"," if file.suffix.lower() in extensions_to_delete:\n"," try:\n"," os.remove(file)\n"," print(f\"Deleted: {file}\")\n"," except Exception as e:\n"," print(f\"Error deleting {file}: {e}\")\n","\n","print(f\"\\nCleaning of {directory_to_clean} complete.\")"],"metadata":{"id":"wUpoo2uI6TZA"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mhccTDyzirVn"},"outputs":[],"source":["# @markdown Split the image into 20 parts prior to running\n","no_parts = 20 # @param {type:'slider', min:1,max:30,step:1}\n","print(f'Splitting all images found under /content/... \\n into {no_parts} along x-axis')\n","import os,math,random\n","from PIL import Image\n","home_directory = '/content/'\n","using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n","if using_Kaggle : home_directory = '/kaggle/working/'\n","%cd {home_directory}\n","\n","def my_mkdirs(folder):\n"," if os.path.exists(folder)==False:\n"," os.makedirs(folder)\n","\n","\n","tgt_folder = f'/content/tmp/'\n","split_folder = f'/content/input/'\n","my_mkdirs(f'{split_folder}')\n","\n","\n","src_folder = '/content/'\n","suffixes = ['.gif','.png', '.jpeg' , '.webp' , '.jpg']\n","num = 1\n","for filename in os.listdir(src_folder):\n"," for suffix in suffixes:\n"," if not filename.find(suffix)>-1: continue\n"," #while os.path.exists(f'{tgt_folder}{num}.txt'):num = num+1\n"," print(filename)\n"," %cd {src_folder}\n"," textpath = filename.replace(suffix,'.txt')\n"," #os.remove(f'{filename}')\n"," #continue\n"," image = Image.open(f\"{filename}\").convert('RGB')\n"," w,h=image.size\n"," #grid = product(range(0, h-h%d, d), range(0, w-w%d, d))\n"," divs=no_parts\n"," step=math.floor(w/divs)\n"," for index in range(divs):\n"," %cd {split_folder}\n"," box = (step*index, 0 ,step*(index+1),math.floor(1.0*h))\n"," image.crop(box).save(f'{num}_{index}.jpeg','JPEG')\n"," %cd /content/\n"," if os.path.exists(textpath):\n"," with open(f'{textpath}', 'r') as file:\n"," _tags = file.read()\n","\n"," print(_tags)\n"," if not _tags:continue\n"," tags=''\n"," _tags = [item.strip() for item in f'{_tags}'.split(',')]\n"," random.shuffle(_tags)\n"," for tag in _tags:\n"," tags = tags + tag + ' , '\n"," #----#\n"," tags = (tags + 'AAAA').replace(' , AAAA','')\n"," prompt_str = f' {tags}'\n"," %cd {split_folder}\n"," f = open(f'{num}_{index}.txt','w')\n"," f.write(f'{prompt_str}')\n"," f.close()\n"," #---#\n"," #-----#\n"," #----#\n"," num = num+1\n"," #caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n"," #print(f\"...\\n\\n...caption for {filename}\\n\\n...\")\n"," #print(caption)\n"," #---------#\n"," #f = open(f\"{num}.txt\", \"w\")\n"," #f.write(f'{caption}')\n"," #f.close()\n"," #input_image.save(f'{num}.jpeg', \"JPEG\")\n"," os.remove(f\"{src_folder}{filename}\")\n"," if os.path.exists(f'{src_folder}{textpath}'):os.remove(f'{src_folder}{textpath}')"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[{"file_id":"https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/gemma_image_captioner.ipynb","timestamp":1754865385825},{"file_id":"https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/gemma_image_captioner.ipynb","timestamp":1754853628495},{"file_id":"https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/gemma_image_captioner.ipynb","timestamp":1754830953040},{"file_id":"https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/gemma_image_captioner.ipynb","timestamp":1754830016099},{"file_id":"https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/gemma_image_captioner.ipynb","timestamp":1754495752733},{"file_id":"https://huggingface.co/datasets/codeShare/gemma_training/blob/main/Gemma3_(4B)-Vision.ipynb","timestamp":1754479907506},{"file_id":"https://huggingface.co/datasets/codeShare/gemma_training/blob/main/Gemma3_(4B)-Vision.ipynb","timestamp":1754479614873},{"file_id":"https://github.com/unslothai/notebooks/blob/main/nb/Gemma3_(4B)-Vision.ipynb","timestamp":1754476728770}]},"kernelspec":{"display_name":".venv","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.13.3"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file