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README.md

@@ -1,44 +1,69 @@
 ---
-license: apache-2.0
-library_name: transformers
-base_model: google/gemma-2b
-tags:
-- text-generation
-- standalone
-- merged-weights
-- pdf-optimized
-- gemma
-language:
-- en
-pipeline_tag: text-generation
----
+        license: apache-2.0
+        library_name: transformers
+        base_model: google/gemma-2b
+        tags:
+          - text-generation
+          - standalone
+          - merged-weights
+          - pdf-optimized
+          - gemma
+          - vision-guided-training
+        language:
+          - en
+        pipeline_tag: text-generation
+        ---
+
+        # 🚀 Solvrays Finetuned Pdf (Standalone Merged Weight)
+
+        ## 🌟 Overview
+        This model is a high-performance, standalone version of **Gemma 2B**, meticulously fine-tuned for **complex document understanding and technical metadata extraction**. Unlike standard PEFT adapters, this version features **merged weights**, enabling seamless integration into production pipelines without the overhead of loading separate adapter layers.
+
+        ### 🛠 Key Features
+        - **Zero-Overhead Inference**: Merged weights allow loading as a native CausalLM.
+        - **Document Intelligence**: Fine-tuned on technical PDF structures, including infrastructure guides and architectural documentation.
+        - **Vision-Guided Data Pipeline**: Trained on text recovered through a hybrid Digital/OCR pipeline for maximum data fidelity.
+        - **Optimized Context**: Tailored for high-precision extraction and summary tasks from technical corpora.
+
+        ## 💻 Quick Start (Inference)
+        You can deploy this model using standard Hugging Face `transformers` logic.
+
+        ```python
+        from transformers import AutoTokenizer, AutoModelForCausalLM
+        import torch
 
-# Solvrays Finetuned Pdf (Standalone)
+        model_id = "singtan/solvrays-finetuned-pdf"
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id, 
+            device_map="auto", 
+            torch_dtype=torch.float16, 
+            trust_remote_code=True
+        )
 
-## Description
-This is a **STANDALONE** version of Gemma 2B, specifically fine-tuned for high-context document understanding. Unlike adapter-based models, this version has its weights **merged**, allowing for simplified loading and inference.
+        prompt = "Analyze the provided technical documentation and summarize the key infrastructure recommendations."
+        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
-## Usage (Simplified)
-You can load this model using a single line of code, exactly like any standard Hugging Face model.
+        with torch.no_grad():
+            outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
 
-### Inference Code
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+        print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+        ```
 
-model_id = "singtan/solvrays-finetuned-pdf"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)
+        ## 📊 Training Specifications
+        - **Base Model**: google/gemma-2b
+        - **Training Strategy**: QLoRA (4-bit quantization) followed by FP16 weight merging.
+        - **Final Loss Performance**: N/A
+        - **Learning Rate**: 0.0001
+        - **Epochs**: 3
+        - **Hardware**: Optimized for NVIDIA L4/V100/H100 environments.
 
-prompt = "What are the key insights from the documentation?"
-inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-outputs = model.generate(**inputs, max_new_tokens=150)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
+        ## ⚠️ Limitations & Bias
+        While optimized for technical documentation, this model remains a generative LLM and may produce hallucinations if the input context is missing or highly ambiguous. It is recommended to use **Retrieval-Augmented Generation (RAG)** or **strict prompting** for mission-critical data extraction.
 
-## Performance
-- **Final Training Loss**: N/A
-- **Optimized for**: Technical documentation and PDF-source data.
+        ## 📜 License
+        This model follows the **Apache-2.0** license. Usage must adhere to the Google Gemma Prohibited Use Policy.
 
-## Authors
-Fine-tuned and Merged by **Bibek**.
+        ---
+        **Fine-tuned and Merged by Bibek Lama Singtan**
+