Native-Hindi Qwen3.5-9B for your laptop. Quantized from pankajpandey-dev/qwen3.5-9b-hindi-instruct. Answers directly in Devanagari — no English thinking, no code-switching.

Which file?

File	Size	RAM needed	Use when
Q4_K_M	~5.7 GB	~8 GB	Recommended — best size/quality on CPU
Q5_K_M	~6.7 GB	~9 GB	More quality headroom
Q8_0	~10 GB	~12 GB	Near-lossless

Run it

llama.cpp (build from ~March 2026 or newer for Qwen3.5 support):

llama-cli -m qwen3.5-9b-hindi-Q4_K_M.gguf --jinja -cnv --repeat-penalty 1.1

LM Studio: search this repo name, download Q4_K_M, set repeat penalty 1.1, chat. Python (llama-cpp-python 0.3.32 or newer):

python

from llama_cpp import Llama
llm = Llama(model_path="qwen3.5-9b-hindi-Q4_K_M.gguf", n_ctx=4096)
r = llm.create_chat_completion(
    messages=[{"role": "user", "content": "योग के चार लाभ बताइए।"}],
    repeat_penalty=1.1)
print(r["choices"][0]["message"]["content"])

Ollama: ollama run hf.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF:Q4_K_M — needs an Ollama build with Qwen3.5 support; if it errors, use llama.cpp or LM Studio. Tip: use repeat penalty 1.1 — long letter-style outputs can loop without it.

🇮🇳 About the Hindi LLM Series

Weekly open releases making small LLMs speak fluent, native Hindi — trained on free/low-cost GPUs, shipped as GGUF for laptops and edge devices. Built by pankajpandey-dev (contact links on profile).

This release: Model · GGUF · LoRA · Series: 🇮🇳 Hindi LLM Collection