commit 72a2b4c1a855006934ee7a5ca021df60b6c6cf59
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Mon Jun 22 15:07:28 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF
    Source: Original Platform

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+---
+base_model: pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2
+base_model_relation: quantized
+language:
+- hi
+- en
+license: apache-2.0
+library_name: gguf
+pipeline_tag: text-generation
+tags:
+- qwen3
+- hindi
+- indic
+- india
+- gguf
+- llama.cpp
+- ollama
+- lm-studio
+- instruction-tuned
+---
+
+# 🇮🇳 Qwen3-4B Hindi Instruct v2 — GGUF
+
+GGUF quantizations of **[Qwen3-4B-Hindi-Instruct-v2](https://huggingface.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2)** — a Hindi instruction-tuned Qwen3-4B model. These run **locally on CPU or GPU** with llama.cpp, Ollama, and LM Studio — no Python or heavy setup needed.
+
+Part of the **Hindi LLM Series**, focused on bringing Indic-language models to local and edge devices.
+
+## Available Quants
+
+| File | Quant | Size | Recommended for |
+|------|-------|------|-----------------|
+| `Qwen3-4B-Hindi-v2.Q4_K_M.gguf` | Q4_K_M | 2.5 GB | **Best balance** — start here |
+| `Qwen3-4B-Hindi-v2.Q5_K_M.gguf` | Q5_K_M | 2.9 GB | Higher quality, slightly larger |
+| `Qwen3-4B-Hindi-v2.Q8_0.gguf` | Q8_0 | 4.3 GB | Near-lossless, maximum quality |
+
+If unsure, download **Q4_K_M** — it's the best size-to-quality tradeoff for most machines.
+
+## How to Run
+
+### Ollama
+```bash
+huggingface-cli download pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF Qwen3-4B-Hindi-v2.Q4_K_M.gguf --local-dir .
+ollama create qwen3-hindi -f Modelfile
+ollama run qwen3-hindi "भारत के बारे में एक रोचक तथ्य बताओ।"
+```
+
+### llama.cpp
+```bash
+./llama-cli -m Qwen3-4B-Hindi-v2.Q4_K_M.gguf -p "भारत की राजधानी क्या है?" -cnv
+```
+
+### LM Studio
+Search for this repo in LM Studio, download the Q4_K_M file, and chat directly in the GUI.
+
+## About the Model
+
+This is a Hindi instruction fine-tune of Qwen3-4B (LoRA via Unsloth, 10K Hindi instruction pairs), quantized to GGUF for efficient local inference. It handles both Hindi (Devanagari) and English.
+
+For full model details and the original 16-bit weights, see the **[base model card](https://huggingface.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2)**.
+
+## License
+
+Apache 2.0 — commercial use allowed.
+
+---
+
+*Part of the 🇮🇳 Hindi LLM Series by [pankajpandey-dev](https://huggingface.co/pankajpandey-dev).*