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README.md

@@ -1,61 +1,6 @@
----
-license: mit
-datasets:
-- lodestones/e621-captions
-- lodestones/pixelprose
-language:
-- en
-base_model:
-- google/gemma-3-4b-it
-tags:
-- flux
-- flux_chroma
-- chroma
-- image_to_prompt
-- captioning
-- lora
-- gemma
-- image_caption
-- image_classification
-- google_colab
-- jupyter
-- unslouth
-- dataset_processing
----
-
-A proof of concept generating captions using Google Gemma 3 on Google Colab Free Tier for captioning prompts akin to training data of FLUX Chroma: https://huggingface.co/lodestones/Chroma
-
-Try the Chroma model at: https://tensor.art/models/891236315830428357
-
-This dataset was built using 200 images from Redcaps : https://huggingface.co/datasets/lodestones/pixelprose
-
-And 200 LLM captioned e621 images: https://huggingface.co/datasets/lodestones/e621-captions/tree/main
-
-The total trained images are just 400 total , randomly selected , so this LoRa adaptation is very basic! You can likely train a better version yourself with listed tools on Google Colab Free Tier T4. 
-
-
-Want to train your own LoRa from a JSON or .parquet set if data?  Use this notebook found in this repo: https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/train_on_parquet.ipynb
-
-//----//
-
-I made some .parquets of the captions here for easier browsing: https://huggingface.co/datasets/codeShare/chroma_prompts
-
-To use this Gemma LoRa adaptation got to the Google Colab Jupyter notebook in this repo: https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/gemma_image_captioner.ipynb
-
-To train your own LoRa adaptation of the Gemma on Google Colab Free Tier T4 , visit :  https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B)-Vision.ipynb
-
-
 ---
 base_model: unsloth/gemma-3-4b-pt-unsloth-bnb-4bit
 library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
-- unsloth
 ---
 
 # Model Card for Model ID
@@ -254,4 +199,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 [More Information Needed]
 ### Framework versions
 
-- PEFT 0.16.0
+- PEFT 0.15.2

adapter_config.json

@@ -1,6 +1,9 @@
 {
   "alpha_pattern": {},
-  "auto_mapping": null,
+  "auto_mapping": {
+    "base_model_class": "Gemma3ForConditionalGeneration",
+    "parent_library": "transformers.models.gemma3.modeling_gemma3"
+  },
   "base_model_name_or_path": "unsloth/gemma-3-4b-pt-unsloth-bnb-4bit",
   "bias": "none",
   "corda_config": null,
@@ -20,14 +23,12 @@
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
-  "qalora_group_size": 16,
   "r": 16,
   "rank_pattern": {},
   "revision": null,
   "target_modules": "(?:.*?(?:vision|image|visual|patch|language|text).*?(?:self_attn|attention|attn|mlp|feed_forward|ffn|dense).*?(?:k_proj|v_proj|q_proj|out_proj|fc1|fc2|o_proj|gate_proj|up_proj|down_proj).*?)|(?:\\bmodel\\.layers\\.[\\d]{1,}\\.(?:self_attn|attention|attn|mlp|feed_forward|ffn|dense)\\.(?:(?:k_proj|v_proj|q_proj|out_proj|fc1|fc2|o_proj|gate_proj|up_proj|down_proj)))",
-  "task_type": "CAUSAL_LM",
+  "task_type": null,
   "trainable_token_indices": null,
   "use_dora": false,
-  "use_qalora": false,
   "use_rslora": false
 }

adapter_model.safetensors

@@ -1,3 +1,3 @@
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tokenizer.json

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