From: Daniel Bevenius <redacted>
Date: Sat, 16 Mar 2024 15:46:29 +0000 (+0100)
Subject: gritlm : add initial README.md (#6086)
X-Git-Tag: upstream/0.0.4488~2041
X-Git-Url: https://git.djapps.eu/?a=commitdiff_plain;h=b5f4ae09c3244ae1644b67c03ed9f4227ab25ad2;p=pkg%2Fggml%2Fsources%2Fllama.cpp

gritlm : add initial README.md (#6086)

* gritlm: add initial README.md to examples/gritlm

This commit adds a suggestion for an initial README.md for the gritlm
example.

Signed-off-by: Daniel Bevenius <redacted>

* squash! gritlm: add initial README.md to examples/gritlm

Use the `scripts/hf.sh` script to download the model file.

Signed-off-by: Daniel Bevenius <redacted>

* squash! gritlm: add initial README.md to examples/gritlm

Fix editorconfig-checker error in examples/gritlm/README.md.

Signed-off-by: Daniel Bevenius <redacted>

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Signed-off-by: Daniel Bevenius <redacted>
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diff --git a/examples/gritlm/README.md b/examples/gritlm/README.md
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+## Generative Representational Instruction Tuning (GRIT) Example
+[gritlm] a model which can generate embeddings as well as "normal" text
+generation depending on the instructions in the prompt.
+
+* Paper: https://arxiv.org/pdf/2402.09906.pdf
+
+### Retrieval-Augmented Generation (RAG) use case
+One use case for `gritlm` is to use it with RAG. If we recall how RAG works is
+that we take documents that we want to use as context, to ground the large
+language model (LLM), and we create token embeddings for them. We then store
+these token embeddings in a vector database.
+
+When we perform a query, prompt the LLM, we will first create token embeddings
+for the query and then search the vector database to retrieve the most
+similar vectors, and return those documents so they can be passed to the LLM as
+context. Then the query and the context will be passed to the LLM which will
+have to _again_ create token embeddings for the query. But because gritlm is used
+the first query can be cached and the second query tokenization generation does
+not have to be performed at all.
+
+### Running the example
+Download a Grit model:
+```console
+$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf
+```
+
+Run the example using the downloaded model:
+```console
+$ ./gritlm -m gritlm-7b_q4_1.gguf
+
+Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605
+Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103
+Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.112
+Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.547
+
+Oh, brave adventurer, who dared to climb
+The lofty peak of Mt. Fuji in the night,
+When shadows lurk and ghosts do roam,
+And darkness reigns, a fearsome sight.
+
+Thou didst set out, with heart aglow,
+To conquer this mountain, so high,
+And reach the summit, where the stars do glow,
+And the moon shines bright, up in the sky.
+
+Through the mist and fog, thou didst press on,
+With steadfast courage, and a steadfast will,
+Through the darkness, thou didst not be gone,
+But didst climb on, with a steadfast skill.
+
+At last, thou didst reach the summit's crest,
+And gazed upon the world below,
+And saw the beauty of the night's best,
+And felt the peace, that only nature knows.
+
+Oh, brave adventurer, who dared to climb
+The lofty peak of Mt. Fuji in the night,
+Thou art a hero, in the eyes of all,
+For thou didst conquer this mountain, so bright.
+```
+
+[gritlm]: https://github.com/ContextualAI/gritlm