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Model: AksaraLLM/AksaraLLM-Qwen-1.5B-GGUF
Source: Original Platform

README.md

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+---
+language:
+- id
+license: apache-2.0
+library_name: gguf
+pipeline_tag: text-generation
+base_model: AksaraLLM/AksaraLLM-Qwen-1.5B
+tags:
+- gguf
+- llama.cpp
+- ollama
+- indonesian
+- aksarallm
+- qwen2
+---
+# AksaraLLM-Qwen-1.5B-GGUF
+
+GGUF quantizations of [`AksaraLLM/AksaraLLM-Qwen-1.5B`](https://huggingface.co/AksaraLLM/AksaraLLM-Qwen-1.5B) for inference with [llama.cpp](https://github.com/ggml-org/llama.cpp), [Ollama](https://ollama.ai), [LM Studio](https://lmstudio.ai), 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
+
+```bash
+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
+
+```bash
+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`](https://huggingface.co/AksaraLLM/AksaraLLM-Qwen-1.5B) for architecture, training data, eval results, and limitations.
+
+## Conversion provenance
+
+- Converted with [`convert_hf_to_gguf.py`](https://github.com/ggml-org/llama.cpp/blob/master/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