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Model: Abiray/Sutra-Instruct-350M-GGUF
Source: Original Platform

README.md

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+---
+language:
+- en
+license: mit
+base_model: Abhiray/Sutra-Instruct-350M
+tags:
+- gguf
+- llama.cpp
+- quantized
+- gpt2
+- sutra
+pipeline_tag: text-generation
+library_name: gguf
+---
+
+# Sutra-Instruct-350M-GGUF
+
+This repository contains the official GGUF quantizations of [**Sutra-Instruct-350M**](https://huggingface.co/Abhiray/Sutra-Instruct-350M), a 350-million parameter model trained entirely from scratch by Abhiray.
+
+The **Sutra** (सूत्र) series is designed to be a high-speed, rule-based instruction model. These GGUF versions are optimized for ultra-low latency inference on consumer-grade hardware, including CPUs and mobile devices.
+
+---
+
+## 📁 Available Quantizations
+
+| File Name | Quant Method | Size | Description |
+| :--- | :--- | :--- | :--- |
+| **sutra-fp16.gguf** | None (F16) | ~779 MB | Original weights. RECOMMENDED MOST Best for maximum quality and accuracy. |
+| **sutra-Q8_0.gguf** | Q8_0 | ~417 MB | Standard 8-bit. Near-lossless; RECOMMENDED for most users. |
+| **sutra-Q4_K_M.gguf** | Q4_K_M | ~258 MB | 4-bit Medium. Its for just testing not recommended much. |
+
+---
+
+## 🚀 Quick Start (Inference)
+
+### 1. Using llama.cpp
+Run the model directly from your terminal using `llama-cli`:
+
+```bash
+./llama-cli -m sutra-Q8_0.gguf -p "Instruction: What is the law of gravity?\n\nResponse:" -n 400 --temp 0.55 --repeat-penalty 1.2
+```
+
+### 2. Using Ollama (Plug-and-Play)
+To use this with Ollama, create a file named Modelfile with the following content:
+
+FROM ./sutra-Q8_0.gguf
+TEMPLATE """Instruction:
+{{ .Prompt }}
+
+Response:
+"""
+PARAMETER stop "Instruction:"
+PARAMETER stop "\nInstruction:"
+PARAMETER temperature 0.55
+PARAMETER repeat_penalty 1.2 
+PARAMETER top_k 50
+PARAMETER num_predict 512
+
+Then, initialize and run the model:
+
+ollama create sutra -f Modelfile
+ollama run sutra
+
+⚙️ Optimized Settings
+To prevent the model from looping or hallucinating, we strongly recommend these inference parameters:
+
+Temperature: 0.55
+
+Repeat Penalty: 1.2
+
+Top-K: 50
+
+Max Tokens: 512