Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,86 @@
+---
+
+license: apache-2.0
+language:
+  - en
+  - hi
+library_name: llama.cpp
+tags:
+  - gguf
+  - llama.cpp
+  - qwen
+  - quantized
+  - q4_k_m
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+
+---
+
+# Qwen2.5-1.5B-Instruct Q4_K_M GGUF
+
+This repository contains a GGUF quantized version of **Qwen2.5-1.5B-Instruct** optimized for local inference with llama.cpp and compatible applications.
+
+## Model Details
+
+* Base Model: Qwen2.5-1.5B-Instruct
+* Format: GGUF
+* Quantization: Q4_K_M
+* Intended Use: Local inference, experimentation, educational projects, and edge deployment
+
+## Quantization Information
+
+The model was converted from the original Hugging Face checkpoint to GGUF format and quantized using llama.cpp's `Q4_K_M` quantization scheme.
+
+Q4_K_M provides a strong balance between:
+
+* Model quality
+* Memory usage
+* Inference speed
+
+making it suitable for consumer hardware and mobile devices.
+
+## Files
+
+| File             | Description                 |
+| ---------------- | --------------------------- |
+| qwen-q4_k_m.gguf | Q4_K_M quantized GGUF model |
+
+## Usage with llama.cpp
+
+```bash
+./llama-cli \
+  -m qwen-q4_k_m.gguf \
+  -p "Explain machine learning in simple terms."
+```
+
+## Usage with Ollama
+
+Create a Modelfile:
+
+```text
+FROM ./qwen-q4_k_m.gguf
+```
+
+Then:
+
+```bash
+ollama create qwen-local -f Modelfile
+ollama run qwen-local
+```
+
+## Hardware Requirements
+
+Approximate requirements:
+
+* Disk Space: ~1–2 GB
+* RAM: 2–4 GB minimum
+* Recommended RAM: 4 GB+
+
+Actual requirements depend on context length and runtime settings.
+
+## Base Model
+
+This repository contains a quantized derivative of the original Qwen model. Please refer to the original model repository for training details, benchmarks, limitations, and licensing information.
+
+## Disclaimer
+
+This repository only provides a quantized GGUF conversion of the original model. All credit for model training and development belongs to the original Qwen team.