🐛 Fix: Restore Space YAML configuration

README.md

@@ -1,66 +1,13 @@
 ---
-language:
-- en
-- hi
-license: llama3.2
-tags:
-- legal
-- unsloth
-- turboquant
-- gguf
-- edge-ai
-datasets:
-- Techmaestro369/indian-legal-texts-finetuning
-- bharatgenai/BhashaBench-Legal
+title: Vidhik AI Legal Assistant
+emoji: ⚖️
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.16.0
+app_file: app.py
+pinned: false
+python_version: 3.11
 ---
-
-# ⚖️ Vidhik AI: Sovereign Legal SLM (1B)
-
-## Model Summary
-Vidhik AI is a highly optimized, domain-specific Small Language Model (SLM) engineered for the Indian Judiciary and MSME sector. Fine-tuned on a 1B parameter base, it specializes in drafting formal legal notices (e.g., MSMED Act delayed payments) and navigating complex Indian officialese.
-
-**Developer:** Bhishaj Technologies (Gaurav)
-**Base Model:** Llama-3.2-1B-Instruct
-**Quantization:** 4-bit GGUF (Q4_K_M)
-
-## 🛠️ Training & MLOps Architecture
-To bypass local hardware constraints, the model was trained using a hybrid cloud-edge pipeline:
-* **Compute:** Kaggle Dual T4 GPUs (32GB VRAM)
-* **Optimization:** Unsloth for 70% VRAM reduction during fine-tuning.
-* **Method:** PEFT/QLoRA instruction fine-tuning on `indian-legal-texts-finetuning`.
-* **Guardrails:** Model is trained with strict negative stop-sequences and deterministic decoding (`Temperature = 0.0`) to prevent MCQ-loop hallucinations.
-
-## ⚡ Edge Deployment & Google TurboQuant
-This model is specifically compiled to run on legacy/constrained hardware (e.g., NVIDIA GTX 1050 4GB).
-
-By utilizing **Google TurboQuant**, the model compresses the KV-cache to 3-bits during runtime, allowing for 128k context windows (essential for long Indian government gazettes) without triggering OOM (Out of Memory) crashes, maintaining a throughput of ~24.5 tokens/sec.
-
-### Python Usage (TurboQuant Enabled)
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from turboquant import TurboQuantCache
-
-repo_id = "Bhishaj/Vidhik-Llama-1B-GGU"
-tokenizer = AutoTokenizer.from_pretrained(repo_id)
-model = AutoModelForCausalLM.from_pretrained(repo_id, device_map="cuda")
-
-# Initialize TurboQuant 4-bit Cache for 4GB VRAM support
-tq_cache = TurboQuantCache(bits=4, compute_device="cuda")
-
-prompt = "TASK: Draft a formal legal notice for my client 'M/s Vidhik Electronics' under MSMED Act Sections 15 & 16."
-inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-
-with torch.no_grad():
-    outputs = model.generate(
-        **inputs,
-        past_key_values=tq_cache,
-        max_new_tokens=512,
-        temperature=0.0
-    )
-
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-
-## 📊 Evaluation
-Evaluated against **BhashaBench-Legal (BBL)** to ensure alignment with Indian judicial service standards and formal legal tonality.
+Vidhik AI Legal Assistant
+Sovereign Legal SLM running on Transformers-native GGUF.