Add/Update generated README.md

README.md

@@ -1,202 +1,75 @@
+
 ---
-base_model: microsoft/git-base
-library_name: peft
+# Auto-generated fields, verify and update as needed
+license: apache-2.0
+tags:
+- generated-by-script
+- peft # Assume PEFT adapter unless explicitly a full model repo
+- image-captioning # Add more specific task tags if applicable
+base_model:
+- microsoft/git-base # Heuristic guess for decoder, VERIFY MANUALLY
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.14.0
+# Model: ashimdahal/microsoft-git-base_microsoft-git-base
+
+This repository contains model artifacts for a run named `microsoft-git-base_microsoft-git-base`, likely a PEFT adapter.
+
+## Training Source
+This model was trained as part of the project/codebase available at:
+https://github.com/ashimdahal/captioning_image/blob/main
+
+## Base Model Information (Heuristic)
+* **Processor/Vision Encoder (Guessed):** `microsoft/git-base`
+* **Decoder/Language Model (Guessed):** `microsoft/git-base`
+
+**⚠️ Important:** The `base_model` tag in the metadata above is initially empty. The models listed here are *heuristic guesses* based on the training directory name (`microsoft-git-base_microsoft-git-base`). Please verify these against your training configuration and update the `base_model:` list in the YAML metadata block at the top of this README with the correct Hugging Face model identifiers.
+
+## How to Use (Example with PEFT)
+
+```python
+from transformers import AutoProcessor, AutoModelForVision2Seq, Blip2ForConditionalGeneration # Or other relevant classes
+from peft import PeftModel, PeftConfig
+import torch
+
+# --- Configuration ---
+# 1. Specify the EXACT base model identifiers used during training
+# base_processor_id = "microsoft/git-base" # <-- Replace with correct HF ID
+# base_model_id = "microsoft/git-base" # <-- Replace with correct HF ID (e.g., Salesforce/blip2-opt-2.7b)
+
+# 2. Specify the PEFT adapter repository ID (this repo)
+# adapter_repo_id = "ashimdahal/microsoft-git-base_microsoft-git-base"
+
+# --- Load Base Model and Processor ---
+# processor = AutoProcessor.from_pretrained(base_processor_id)
+
+# Load the base model (ensure it matches the type used for training)
+# Example for BLIP-2 OPT:
+# base_model = Blip2ForConditionalGeneration.from_pretrained(
+#     base_model_id,
+#     torch_dtype=torch.float16 # Or torch.bfloat16 or float32, match training/inference needs
+# )
+# Or for other model types:
+# base_model = AutoModelForVision2Seq.from_pretrained(base_model_id, torch_dtype=torch.float16)
+
+# --- Load PEFT Adapter ---
+# Load the adapter config and merge the adapter weights into the base model
+# model = PeftModel.from_pretrained(base_model, adapter_repo_id)
+# model = model.merge_and_unload() # Merge weights for inference (optional but often recommended)
+# model.eval() # Set model to evaluation mode
+
+# --- Inference Example ---
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+# model.to(device)
+#
+# image = ... # Load your image (e.g., using PIL)
+# text = "a photo of" # Optional prompt start
+#
+# inputs = processor(images=image, text=text, return_tensors="pt").to(device, torch.float16) # Match model dtype
+#
+# generated_ids = model.generate(**inputs, max_new_tokens=50)
+# generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+# print(f"Generated Caption: {{generated_text}}")
+
+```
+
+*More model-specific documentation, evaluation results, and usage examples should be added here.*