language, license, library_name, pipeline_tag, base_model, tags

language	license	library_name	pipeline_tag	base_model	tags
id	apache-2.0	gguf	text-generation	AksaraLLM/AksaraLLM-Qwen-1.5B	gguf llama.cpp ollama indonesian aksarallm qwen2

AksaraLLM-Qwen-1.5B-GGUF

GGUF quantizations of AksaraLLM/AksaraLLM-Qwen-1.5B for inference with llama.cpp, Ollama, LM Studio, and other GGUF runtimes.

Files

File	Quant	Size	Recommended use
AksaraLLM-Qwen-1.5B.f16.gguf	F16	3.56 GB	lossless from safetensors
AksaraLLM-Qwen-1.5B.q8_0.gguf	Q8_0	1.89 GB	near-lossless, ~2× smaller
AksaraLLM-Qwen-1.5B.q6_k.gguf	Q6_K	1.46 GB	high quality, ~2.5× smaller
AksaraLLM-Qwen-1.5B.q5_k_m.gguf	Q5_K_M	1.29 GB	good quality, ~3× smaller
AksaraLLM-Qwen-1.5B.q4_k_m.gguf	Q4_K_M	1.12 GB	recommended default, ~4× smaller

CPU benchmark (AMD EPYC 7763, 2 threads, AVX2)

Quant	Prompt eval (32 tok)	Generation (16 tok)
q4_k_m	23.7 tok/s	11.8 tok/s

So a 1.78B model at q4_k_m runs comfortably on a CPU laptop. Larger quants (q5_k_m, q6_k, q8_0) trade a bit of speed for better quality.

Quick start — llama.cpp

huggingface-cli download AksaraLLM/AksaraLLM-Qwen-1.5B-GGUF AksaraLLM-Qwen-1.5B.q4_k_m.gguf --local-dir .
./llama-cli -m AksaraLLM-Qwen-1.5B.q4_k_m.gguf -p "Indonesia adalah" -n 64

Quick start — Ollama

huggingface-cli download AksaraLLM/AksaraLLM-Qwen-1.5B-GGUF AksaraLLM-Qwen-1.5B.q4_k_m.gguf Modelfile --local-dir .
ollama create aksara-aksarallm-qwen-1.5b -f Modelfile
ollama run aksara-aksarallm-qwen-1.5b "Apa ibukota Indonesia?"

Source model

See AksaraLLM/AksaraLLM-Qwen-1.5B for architecture, training data, eval results, and limitations.

Conversion provenance

• Converted with convert_hf_to_gguf.py from llama.cpp
• Quantized with llama-quantize from the same build
• Architecture detected as qwen2
• All files listed above are reproducible from the source HF safetensors