From cb13d73a720c42d1958bff79b6869d77b26b8cea Mon Sep 17 00:00:00 2001 From: Mihai Date: Fri, 6 Oct 2023 21:39:33 +0300 Subject: [PATCH 1/2] server : docs fix default values and add n_probs (#3506) --- examples/server/README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 9ee62d06a..8a079ae26 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -114,9 +114,9 @@ node index.js `top_k`: Limit the next token selection to the K most probable tokens (default: 40). - `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9). + `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95). - `n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: 128, -1 = infinity). + `n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity). `n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt. @@ -156,6 +156,8 @@ node index.js `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced (default: []). + `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) + - **POST** `/tokenize`: Tokenize a given text. *Options:* From 1faaae8c2bdc4a21302e367e0754c3fe74a8113e Mon Sep 17 00:00:00 2001 From: BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com> Date: Fri, 6 Oct 2023 15:13:36 -0400 Subject: [PATCH 2/2] readme : update models, cuda + ppl instructions (#3510) --- README.md | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index e436818fa..056279562 100644 --- a/README.md +++ b/README.md @@ -95,6 +95,7 @@ as the main playground for developing new features for the [ggml](https://github - [X] [Aquila-7B](https://huggingface.co/BAAI/Aquila-7B) / [AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) - [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) +- [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim) **Bindings:** @@ -377,7 +378,7 @@ Building the program with BLAS support may lead to some performance improvements - #### cuBLAS - This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). + This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). - Using `make`: ```bash make LLAMA_CUBLAS=1 @@ -613,6 +614,18 @@ For more information, see [https://huggingface.co/docs/transformers/perplexity]( The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512. The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads. +#### How to run + +1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research +2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw` +3. Output: +``` +perplexity : 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. + ### Interactive mode If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter. @@ -775,18 +788,6 @@ If your issue is with model generation quality, then please at least scan the fo - [Aligning language models to follow instructions](https://openai.com/research/instruction-following) - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) -#### How to run - -1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research -2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw` -3. Output: -``` -perplexity : 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 #### Building the Project using Android NDK