SobanHM
/

LLaVA-via

@@ -1,207 +1,207 @@
 ---
-base_model: llava-hf/llava-1.5-7b-hf
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:llava-hf/llava-1.5-7b-hf
-- lora
-- transformers
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.17.1

 ---
+language: en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: image-to-text
 tags:
+  - llava
+  - llava-1.5
+  - vision-language-model
+  - multimodal
+  - peft
+  - lora
+  - transformers
+  - image-to-text
+  - scene-understanding
+  - navigation-guidance
+  - assistive-ai
+  - visually-impaired
+base_model: llava-hf/llava-1.5-7b-hf
 ---
+# LLaVA-via: Fine-Tuned LoRA Adapter for Scene Narration and Navigation Guidance
+## Overview
+**LLaVA-via** is a **PEFT LoRA adapter** fine-tuned on top of **LLaVA-1.5-7B** to improve scene narration and navigation guidance for assistive vision-language applications.
+The objective of this work is to generate contextually grounded, coherent, and navigation-aware responses that can support visually impaired assistance scenarios. Rather than answering general visual questions, the adapter is optimized to describe surrounding environments and provide guidance-oriented information from input images.
+This repository contains **only the LoRA adapter** and associated tokenizer/processor configuration files. It does **not** contain the full LLaVA model weights.
+---
+# Base Model
+This adapter must be loaded on top of the following Hugging Face model:
+**Base Model**
+```
+llava-hf/llava-1.5-7b-hf
+```
+The base model is **not included** in this repository.
+---
+# Repository Contents
+This repository includes:
+* `adapter_model.safetensors` — Fine-tuned LoRA adapter weights
+* `adapter_config.json` — PEFT configuration
+* Tokenizer files
+* Processor configuration
+* Image preprocessor configuration
+* Chat template
+* Model card (README)
+Training checkpoints, optimizer states, and scheduler files are intentionally excluded.
+---
+# Intended Use
+LLaVA-via is intended for research and development involving assistive vision-language systems, including:
+* Scene narration
+* Environmental understanding
+* Navigation guidance
+* Assistive AI research
+* Vision-language model benchmarking
+Example applications include:
+* Assistive mobile applications
+* Smart glasses
+* Vision-language research
+* Accessibility-focused AI systems
+* Human-centered AI
+---
+# Fine-Tuning Method
+* **Architecture:** LLaVA-1.5-7B
+* **Fine-tuning:** PEFT LoRA
+* **Task:** Vision-Language Conditional Generation
+* **LoRA Rank (r):** 16
+* **LoRA Alpha:** 32
+* **Target Modules:** `q_proj`, `v_proj`
+* **LoRA Dropout:** 0.05
+---
+# Evaluation
+The fine-tuned adapter was evaluated using multiple automatic metrics covering lexical similarity, semantic similarity, vision-language alignment, and hallucination analysis.
+| Metric                             |          Score |
+| ---------------------------------- | -------------: |
+| BLEU-1                             |         0.3125 |
+| BLEU-2                             |         0.1842 |
+| BLEU-3                             |         0.1228 |
+| BLEU-4                             |         0.0860 |
+| METEOR                             |         0.3165 |
+| ROUGE-L                            |         0.2755 |
+| **BERTScore-F1**                   |     **0.8872** |
+| **SBERTScore**                     |     **0.7116** |
+| CLIPScore (ViT-B/32)               |         0.2839 |
+| CLIPScore (ViT-B/16)               |         0.2329 |
+| CLIPScore (ViT-L/14)               |         0.1828 |
+| PickScore (CLIP-ViT-H-14)          |         0.2030 |
+| **SPECS-VIA Score**                | **3.2770 / 5** |
+| **PickScore-VIA**                  |     **0.8132** |
+| **CHAIR-i (Semantically Refined)** |      **0.410** |
+The evaluation includes lexical overlap metrics (BLEU, METEOR, ROUGE-L), semantic similarity metrics (BERTScore and SBERTScore), vision-language alignment metrics (CLIPScore and PickScore), a task-specific evaluation (SPECS-VIA and PickScore-VIA), and hallucination analysis using the semantically refined CHAIR-i metric.
+---
+# Loading the Adapter
+The adapter can be loaded using the Hugging Face Transformers and PEFT libraries.
+```python
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+from peft import PeftModel
+base_model = LlavaForConditionalGeneration.from_pretrained(
+    "llava-hf/llava-1.5-7b-hf"
+)
+processor = AutoProcessor.from_pretrained(
+    "llava-hf/llava-1.5-7b-hf"
+)
+model = PeftModel.from_pretrained(
+    base_model,
+    "SobanHM/LLaVA-via"
+)
+```
+---
+# Example Use Cases
+The adapter is designed for prompts such as:
+* Describe the surrounding environment.
+* Identify important objects in the scene.
+* Provide navigation guidance.
+* Describe possible obstacles.
+* Explain the scene for a visually impaired user.
+---
+# Limitations
+This repository provides a fine-tuned PEFT LoRA adapter evaluated using offline automatic metrics.
+The reported evaluation does **not** include:
+* Real-time deployment on electronic white canes
+* Smart glasses deployment
+* Mobile-device latency evaluation
+* Embedded hardware benchmarking
+* User studies involving visually impaired participants
+Accordingly, inference latency, energy consumption, real-time responsiveness, and human-subject usability remain outside the scope of the reported experiments.
+---
+# Citation
+If you use this repository in your research, please cite this work and the original LLaVA and PEFT publications.
+A BibTeX entry for this work may be added once the associated thesis or publication becomes available.
+---
+# Authors
+**Soban Hussain**
+Department of Computer Science
+Sukkur IBA University
+**Praih Alias Faiza**
+Department of Computer Science
+Sukkur IBA University
+**Tasmia**
+Department of Computer Science
+Sukkur IBA University
+---
+# Acknowledgements
+This work builds upon the following open-source projects:
+* LLaVA
+* Hugging Face Transformers
+* PEFT
+* PyTorch
+We gratefully acknowledge the developers and research community whose contributions made this work possible.