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Model: di-zhang-fdu/Supra-50M-Instruct-GGUF Source: Original Platform
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README.md
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license: apache-2.0
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base_model: SupraLabs/Supra-50M-Instruct
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library_name: llama.cpp
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tags:
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- gguf
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- llama.cpp
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- quantized
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- supra
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- text-generation
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---
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# Supra-50M-Instruct-GGUF
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GGUF quantizations of [SupraLabs/Supra-50M-Instruct](https://huggingface.co/SupraLabs/Supra-50M-Instruct).
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## Files
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| File | Quantization | Size |
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| --- | --- | ---: |
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| `model.gguf` | F16 base GGUF | 100M |
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| `Q8_0.gguf` | Q8_0 | 54M |
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| `Q6_K.gguf` | Q6_K | 44M |
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| `Q4_K_M.gguf` | Q4_K_M | 36M |
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| `Q4_K_S.gguf` | Q4_K_S | 35M |
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| `Q4_0.gguf` | Q4_0 | 33M |
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| `Q3_K_S.gguf` | Q3_K_S | 30M |
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| `Q2_K.gguf` | Q2_K | 28M |
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Checksums are in `SHA256SUMS`.
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## Conversion
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`model.gguf` is the F16 GGUF from the source repository. The quantized files were generated with `llama.cpp` build `b9550` using `llama-quantize`.
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Some K-quant outputs may contain fallback tensor types where tensor dimensions are not divisible by the required K-quant block size. This is normal `llama.cpp` behavior for this model shape.
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## Usage
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This model was instruction-tuned with the Alpaca prompt format. It is not a ChatML-style multi-turn chat model, so use completion mode and include the prompt template. The GGUF files intentionally do not include `tokenizer.chat_template`, because llama.cpp chat mode would otherwise try to apply the wrong template.
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```text
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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What is the capital of Japan?
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### Response:
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```
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Example with `llama.cpp`:
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```bash
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cat > prompt.txt <<'EOF'
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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What is the capital of Japan?
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### Response:
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EOF
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llama-completion \
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-hf di-zhang-fdu/Supra-50M-Instruct-GGUF:Q4_K_M \
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-f prompt.txt \
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-n 128 \
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--temp 0.7 \
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--top-k 50 \
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--top-p 0.9 \
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--repeat-penalty 1.15 \
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-no-cnv
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```
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For deterministic checks, use `--temp 0 --top-k 1`.
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