---
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 Llm - High Precision Document Analyst
\n## 🌟 Overview
This model is a specialized fine-tuning of **google/gemma-2b-it**, engineered for **Zero-Hallucination Document Retrieval**. It has been optimized to handle complex, domain-specific documents (Technical, Legal, or Architectural) with strict adherence to provided context.
\n### 🛠 Primary Design Objectives
- **Factual Integrity**: Programmed to prioritize 'Not Documented' over speculating.
- **Contextual Continuity**: Overlap-aware training prevents information loss across page boundaries.
- **Domain Versatility**: Seamlessly switches between technical and non-technical document styles.
\n## 💻 Professional Usage (Grounded Inference)
To achieve the trained precision level, utilize the following code implementation:
\n```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = 'solvrays/solvrays-llm'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto', torch_dtype=torch.bfloat16)

# Universal Grounding Template
instruction = 'Analyze your internal knowledge base and provide a precise, factual response based strictly on the documentation you have been trained on. If the information is not documented, state that it is not documented.'
query = 'What are the main infrastructure requirements?'

prompt = (f'### Instruction: {instruction}\n'
          f'### Knowledge Context: {query}\n'
          f'### Verified Response:')

inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False, repetition_penalty=1.5)

print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('### Verified Response:')[-1].strip())
```
\n## 📊 Technical Specifications
| Parameter | Configuration |
| :--- | :--- |
| Base Model | google/gemma-2b-it |
| Fine-tuning Method | QLoRA (4-bit quantization) |
| LoRA Rank (r) | 16 |
| LoRA Alpha | 32 |
| Training Epochs | 5 |
| Context Strategy | 512 tokens with 128-token overlap |
\n## ⚠️ Risks & Limitations
- **Context Window**: Strictly limited to the fine-tuned block size (512 tokens). For longer multi-page queries, RAG (Retrieval Augmented Generation) is recommended.
- **Bias**: The model reflects the biases of the provided training documentation.
- **Accuracy**: Always verify critical technical numbers against the original source.
\n--- 
**Architected and Fine-tuned by Bibek Lama Singtan**