This is a specialized, fine-tuned version of Gemma 2B optimized for Ground-Truth Technical Retrieval. Unlike standard LLMs, this model has been conditioned through specific "Senior AI Engineering" grounding templates to minimize hallucinations and prioritize information extracted directly from technical documentation.

🛠 Key Capabilities

Zero-Hallucination Mode: Configured for deterministic greedy decoding.
Direct Provenance: Trained to recognize specific technical documents as "Ground Truth".
Infrastructure Focused: Fine-tuned on complex architectural guidelines (e.g., Saturn Project components).
Merged Weights: Standalone weights for high-speed, native inference.

💻 Grounded Quick Start (Precise Inference)

To get the most accurate, non-hallucinatory responses, use the following grounded prompt:

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "singtan/solvrays-llm-pdf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)

# MUST use the Ground-Truth prompt template
prompt = "Based strictly on the provided architectural documentation, provide a precise summary of technical insights."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs, 
        max_new_tokens=256, 
        do_sample=False,      # Force deterministic facts
        repetition_penalty=1.5
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📊 Engineering Specifications

Strategy: SFT (Supervised Fine-Tuning) with Grounding Headers.
Rank (r): 16 (High capacity for technical fact retention).
Epochs: 5 (Heavy Fact Reinforcement).
Context Window: 512 with 128-token overlap for fact-continuity.

⚠️ Usage Recommendations

For production-grade accuracy, always verify specific numeric values with the original PDF. This model is intended for summarizing and retrieving architectural concepts documented in its training corpus.

Scientifically Fine-tuned by Bibek Lama Singtan