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deee7612da
* Quantized version of mistral. * Integrate the quantized mistral variant. * Use the quantized weight files. * Tweak the quantization command. * Fix the dtype when computing the rotary embeddings. * Update the readme with the quantized version. * Fix the decoding of the remaining tokens.
91 lines
2.2 KiB
Markdown
91 lines
2.2 KiB
Markdown
# candle-mistral: 7b LLM with Apache 2.0 licensed weights
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Mistral-7B-v0.1 is a pretrained generative LLM with 7 billion parameters. It outperforms all the publicly available 13b models
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as of 2023-09-28. Weights (and the original Python model code) are released under the permissive Apache 2.0 license.
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- [Blog post](https://mistral.ai/news/announcing-mistral-7b/) from Mistral announcing the model release.
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- [Model card](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the
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HuggingFace Hub.
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This example supports the initial model as well as a quantized variant.
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## Running the example
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```bash
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$ cargo run --example mistral --release --features cuda -- --prompt 'Write helloworld code in Rust' --sample-len 150
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Generated text:
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Write helloworld code in Rust
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=============================
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This is a simple example of how to write "Hello, world!" program in Rust.
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## Compile and run
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``bash
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$ cargo build --release
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Compiling hello-world v0.1.0 (/home/user/rust/hello-world)
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Finished release [optimized] target(s) in 0.26s
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$ ./target/release/hello-world
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Hello, world!
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``
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## Source code
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``rust
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fn main() {
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println!("Hello, world!");
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}
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``
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## License
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This example is released under the terms
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```
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## Running the quantized version of the model
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```bash
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$ cargo run --example mistral --features accelerate --release -- \
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$ --prompt "Here is a sample quick sort implementation in rust " --quantized -n 400
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avx: false, neon: true, simd128: false, f16c: false
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temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
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retrieved the files in 562.292µs
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loaded the model in 1.100323667s
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Here is a sample quick sort implementation in rust
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``rust
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fn quick_sort(arr: &mut [i32]) {
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if arr.len() <= 1 {
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return;
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}
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let pivot = arr[0];
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let mut left = vec![];
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let mut right = vec![];
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for i in 1..arr.len() {
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if arr[i] < pivot {
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left.push(arr[i]);
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} else {
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right.push(arr[i]);
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}
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}
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quick_sort(&mut left);
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quick_sort(&mut right);
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let mut i = 0;
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for _ in &left {
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arr[i] = left.pop().unwrap();
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i += 1;
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}
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for _ in &right {
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arr[i] = right.pop().unwrap();
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i += 1;
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}
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}
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``
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226 tokens generated (10.91 token/s)
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```
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