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Model: AksaraLLM/aksarallm-1.5b-native-GGUF Source: Original Platform
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Modelfile
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Modelfile
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# Ollama Modelfile for AksaraLLM/aksarallm-1.5b-native-GGUF
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# Quick start:
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# ollama create aksara-aksarallm-1.5b-native -f Modelfile
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# ollama run aksara-aksarallm-1.5b-native "Indonesia adalah"
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#
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# Or pull a quant directly with:
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# huggingface-cli download AksaraLLM/aksarallm-1.5b-native-GGUF aksarallm-1.5b-native.q4_k_m.gguf --local-dir .
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FROM ./aksarallm-1.5b-native.q4_k_m.gguf
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TEMPLATE """{{ if .System }}{{ .System }}
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{{ end }}{{ if .Prompt }}### Instruksi: {{ .Prompt }}
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### Jawaban: {{ end }}{{ .Response }}"""
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SYSTEM """Kamu adalah AksaraLLM, model bahasa Indonesia. Jawab dengan jelas dan ringkas."""
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PARAMETER temperature 0.7
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PARAMETER top_p 0.9
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PARAMETER repeat_penalty 1.1
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PARAMETER num_ctx 8192
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PARAMETER stop "### Instruksi:"
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PARAMETER stop "### Jawaban:"
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README.md
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README.md
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---
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language:
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- id
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license: apache-2.0
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library_name: gguf
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pipeline_tag: text-generation
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base_model: AksaraLLM/aksarallm-1.5b-native
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tags:
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- gguf
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- llama.cpp
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- ollama
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- indonesian
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- aksarallm
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- llama
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---
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# aksarallm-1.5b-native-GGUF
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GGUF quantizations of [`AksaraLLM/aksarallm-1.5b-native`](https://huggingface.co/AksaraLLM/aksarallm-1.5b-native) 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.
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## Files
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| File | Quant | Size | Recommended use |
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|---|---|---|---|
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| `aksarallm-1.5b-native.f16.gguf` | F16 | 4.08 GB | lossless from safetensors |
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| `aksarallm-1.5b-native.q8_0.gguf` | Q8_0 | 2.17 GB | near-lossless, ~2× smaller |
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| `aksarallm-1.5b-native.q6_k.gguf` | Q6_K | 1.77 GB | high quality, ~2.5× smaller |
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| `aksarallm-1.5b-native.q5_k_m.gguf` | Q5_K_M | 1.53 GB | good quality, ~3× smaller |
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| `aksarallm-1.5b-native.q4_k_m.gguf` | Q4_K_M | 1.35 GB | recommended default, ~4× smaller |
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## CPU benchmark (AMD EPYC 7763, 2 threads, AVX2)
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| Quant | Prompt eval (32 tok) | Generation (16 tok) |
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|---|---:|---:|
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| `q4_k_m` | **17.2 tok/s** | **9.7 tok/s** |
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So a 2.04B 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.
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## Quick start — llama.cpp
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```bash
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huggingface-cli download AksaraLLM/aksarallm-1.5b-native-GGUF aksarallm-1.5b-native.q4_k_m.gguf --local-dir .
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./llama-cli -m aksarallm-1.5b-native.q4_k_m.gguf -p "Indonesia adalah" -n 64
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```
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## Quick start — Ollama
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```bash
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huggingface-cli download AksaraLLM/aksarallm-1.5b-native-GGUF aksarallm-1.5b-native.q4_k_m.gguf Modelfile --local-dir .
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ollama create aksara-aksarallm-1.5b-native -f Modelfile
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ollama run aksara-aksarallm-1.5b-native "Lanjutkan: Indonesia adalah negara"
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```
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## Source model
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See [`AksaraLLM/aksarallm-1.5b-native`](https://huggingface.co/AksaraLLM/aksarallm-1.5b-native) for architecture, training data, eval results, and limitations.
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## Conversion provenance
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- Converted with [`convert_hf_to_gguf.py`](https://github.com/ggml-org/llama.cpp/blob/master/convert_hf_to_gguf.py) from llama.cpp
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- Quantized with `llama-quantize` from the same build
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- Architecture detected as `llama`
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- All files listed above are reproducible from the source HF safetensors
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## Note on the from-scratch model
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This is a llama-3-style decoder built and trained from scratch by the AksaraLLM project. It does **not** use the Qwen2 ChatML template — it expects a plain `### Instruksi: ... ### Jawaban: ...` style prompt (set up automatically by the included Modelfile).
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aksarallm-1.5b-native.f16.gguf
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aksarallm-1.5b-native.f16.gguf
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aksarallm-1.5b-native.q4_k_m.gguf
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aksarallm-1.5b-native.q4_k_m.gguf
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aksarallm-1.5b-native.q5_k_m.gguf
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aksarallm-1.5b-native.q5_k_m.gguf
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aksarallm-1.5b-native.q6_k.gguf
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aksarallm-1.5b-native.q6_k.gguf
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aksarallm-1.5b-native.q8_0.gguf
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aksarallm-1.5b-native.q8_0.gguf
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size 2172800576
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