mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
8a56075b07
This commit updates the hf.sh script usage to include the --outdir option and specifies the models directory as the output directory. The motivation for this is to avoid cluttering the root directory with model files. Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
63 lines
2.7 KiB
Markdown
63 lines
2.7 KiB
Markdown
## 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 --outdir models
|
|
```
|
|
|
|
Run the example using the downloaded model:
|
|
```console
|
|
$ ./gritlm -m models/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
|