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Model: solvrays/solvrays-finetuned-pdf
Source: Original Platform

README.md

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+---
+base_model: google/gemma-2b-it
+language: en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- precision-grounding
+- document-qa
+- zero-hallucination
+- legal-tech
+- technical-analysis
+---
+
+# 📂 Solvrays Finetuned Pdf - Document AI
+
+## 🌟 Model Overview
+This model is a high-precision fine-tuning of **google/gemma-2b-it**, specifically architected for **Zero-Hallucination Technical Retrieval**. It has been trained on a proprietary dataset of technical and architectural documentation to ensure deep contextual grounding.
+
+### 🚀 Key Capabilities
+- **Technical Grounding**: Prioritizes factual documentation over generative speculation.
+- **Chunk-Aware Memory**: Optimized for overlapping document segments (256-token window).
+- **Deterministic Precision**: Best used with `do_sample=False` for architectural accuracy.
+
+## 💻 Professional Implementation
+The model requires specific prompt construction to trigger its 'Knowledge Retrieval' mode:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = 'solvrays/solvrays-finetuned-pdf'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    device_map='auto', 
+    torch_dtype=torch.bfloat16, 
+    quantization_config={'load_in_4bit': True}
+)
+
+def query_model(user_query):
+    # High-Precision Retrieval Template
+    prompt = f'### Knowledge Retrieval Content: {user_query}\n### Verified Response: '
+    inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True).split('### Verified Response:')[-1].strip()
+```
+
+## 📊 Technical Specifications
+| Feature | Configuration |
+| :--- | :--- |
+| **Base Model** | google/gemma-2b-it |
+| **Precision** | BrainFloat16 (BF16) |
+| **Fine-tuning** | QLoRA (4-bit Normalized Float) |
+| **LoRA Rank (r)** | 16 |
+| **LoRA Alpha** | 32 |
+| **Target Modules** | q, k, v, o, gate, up, down |
+| **Training Epochs** | 25 |
+
+## 🛠 Training Environment
+- **Hardware**: NVIDIA L4 x 2 (Dual GPU Architecture)
+- **Optimizer**: Paged AdamW 8-bit
+- **Context Length**: 256 tokens per block
+
+## ⚠️ Constraints & Risk Mitigation
+- **Out-of-Scope**: This model is not intended for general conversation or creative writing. It is a specialized document analyst.
+- **Hallucination Control**: If information is not present in the internal weights, the model is trained to state 'Not Documented' or provide an empty response for verification.
+- **Numerical Accuracy**: Always cross-verify critical measurements with original PDF source material.
+
+---
+**Senior AI Architect & Developer**: Solvrays