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Add details on perplexity to README.md (#395)

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@ -240,6 +240,40 @@ or
`shasum -a 256 --ignore-missing -c SHA256SUMS` on macOS `shasum -a 256 --ignore-missing -c SHA256SUMS` on macOS
### Perplexity (Measuring model quality)
You can pass `--perplexity` as a command line option to measure perplexity over the given prompt. For more background,
see https://huggingface.co/docs/transformers/perplexity. However, in general, lower perplexity is better for LLMs.
#### Measurements
https://github.com/ggerganov/llama.cpp/pull/270 is the unofficial tracking page for now. llama.cpp is measuring very well
compared to the baseline implementations. Quantization has a small negative impact to quality, but, as you can see, running
13B at q4_0 beats the 7B f16 model by a significant amount.
All measurements are done against wikitext2 test dataset (https://paperswithcode.com/dataset/wikitext-2), with default options (512 length context).
Note that the changing the context length will have a significant impact on perplexity (longer context = better perplexity).
```
Perplexity - model options
5.5985 - 13B, q4_0
5.9565 - 7B, f16
6.3001 - 7B, q4_1
6.5949 - 7B, q4_0
6.5995 - 7B, q4_0, --memory_f16
```
#### How to run
1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
2. Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
3. Output:
```
Calculating perplexity over 655 chunks
24.43 seconds per pass - ETA 4.45 hours
[1]4.5970,[2]5.1807,[3]6.0382,...
```
And after 4.45 hours, you will have the final perplexity.
### Android ### Android
You can easily run `llama.cpp` on Android device with [termux](https://play.google.com/store/apps/details?id=com.termux). You can easily run `llama.cpp` on Android device with [termux](https://play.google.com/store/apps/details?id=com.termux).
@ -290,7 +324,6 @@ docker run -v /llama/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models
## Limitations ## Limitations
- We don't know yet how much the quantization affects the quality of the generated text
- Probably the token sampling can be improved - Probably the token sampling can be improved
- The Accelerate framework is actually currently unused since I found that for tensor shapes typical for the Decoder, - The Accelerate framework is actually currently unused since I found that for tensor shapes typical for the Decoder,
there is no benefit compared to the ARM_NEON intrinsics implementation. Of course, it's possible that I simply don't there is no benefit compared to the ARM_NEON intrinsics implementation. Of course, it's possible that I simply don't