63 lines
2.2 KiB
Markdown
63 lines
2.2 KiB
Markdown
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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/Kiel-Pro-0.5B-v3
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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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- qwen2
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---
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# Kiel-Pro-0.5B-v3-GGUF
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GGUF quantizations of [`AksaraLLM/Kiel-Pro-0.5B-v3`](https://huggingface.co/AksaraLLM/Kiel-Pro-0.5B-v3) 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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| `Kiel-Pro-0.5B-v3.f16.gguf` | F16 | 0.99 GB | lossless from safetensors |
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| `Kiel-Pro-0.5B-v3.q8_0.gguf` | Q8_0 | 0.53 GB | near-lossless, ~2× smaller |
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| `Kiel-Pro-0.5B-v3.q6_k.gguf` | Q6_K | 0.51 GB | high quality, ~2.5× smaller |
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| `Kiel-Pro-0.5B-v3.q5_k_m.gguf` | Q5_K_M | 0.42 GB | good quality, ~3× smaller |
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| `Kiel-Pro-0.5B-v3.q4_k_m.gguf` | Q4_K_M | 0.40 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` | **36.7 tok/s** | **20.1 tok/s** |
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So a 494M 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/Kiel-Pro-0.5B-v3-GGUF Kiel-Pro-0.5B-v3.q4_k_m.gguf --local-dir .
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./llama-cli -m Kiel-Pro-0.5B-v3.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/Kiel-Pro-0.5B-v3-GGUF Kiel-Pro-0.5B-v3.q4_k_m.gguf Modelfile --local-dir .
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ollama create aksara-kiel-pro-0.5b-v3 -f Modelfile
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ollama run aksara-kiel-pro-0.5b-v3 "Apa ibukota Indonesia?"
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```
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## Source model
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See [`AksaraLLM/Kiel-Pro-0.5B-v3`](https://huggingface.co/AksaraLLM/Kiel-Pro-0.5B-v3) 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 `qwen2`
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- All files listed above are reproducible from the source HF safetensors
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