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38 lines
1.5 KiB
Markdown
38 lines
1.5 KiB
Markdown
# candle-quantized-llama: Fast Inference of quantized LLaMA models
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This example provides a quantized LLaMA model similar to
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[llama.cpp](https://github.com/ggerganov/llama.cpp). This is based on candle
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built-in quantization methods. Supported features include:
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- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support.
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- SIMD optimizations on Apple Silicon and x86.
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- Support using the `gguf` and `ggml` file formats.
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The weights are automatically downloaded for you from the [HuggingFace
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Hub](https://huggingface.co/) on the first run. There are various command line
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flags to use local files instead, run with `--help` to learn about them.
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## Running some example.
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```bash
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cargo run --example quantized --release -- --prompt "The best thing about coding in rust is "
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> avx: true, neon: false, simd128: false, f16c: true
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> temp: 0.80 repeat-penalty: 1.10 repeat-last-n: 64
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> loaded 291 tensors (3.79GB) in 2.17s
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> params: HParams { n_vocab: 32000, n_embd: 4096, n_mult: 256, n_head: 32, n_layer: 32, n_rot: 128, ftype: 2 }
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> The best thing about coding in rust is 1.) that I don’t need to worry about memory leaks, 2.) speed and 3.) my program will compile even on old machines.
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```
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## Command-line flags
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Run with `--help` to see all options.
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- `--which`: specify the model to use, e.g. `7b`, `13-chat`, `7b-code`.
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- `--prompt interactive`: interactive mode where multiple prompts can be
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entered.
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- `--model mymodelfile.gguf`: use a local model file rather than getting one
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from the hub.
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