Access large language models from the command-line
llm -m gpt-4o 'say hi in Spanish'
#490
gpt-4-turbo
alias is now a model ID, which indicates the latest version of OpenAI's GPT-4 Turbo text and image model. Your existing logs.db
database may contain records under the previous model ID of gpt-4-turbo-preview
. #493
llm logs -r/--response
option for outputting just the last captured response, without wrapping it in Markdown and accompanying it with the prompt. #431
plugins <plugin-directory>
since version 0.13:
llama-3-sonar-large-32k-online
which can search for things online and llama-3-70b-instruct
.<enter>
to execute or ctrl+c
to cancel, see this post for details.No module named 'readline'
error on Windows. #407
See also LLM 0.13: The annotated release notes.
3-small
and 3-large
and three variants of those with different dimension sizes, 3-small-512
, 3-large-256
and 3-large-1024
. See OpenAI embedding models for details. #394
gpt-4-turbo
model alias now points to gpt-4-turbo-preview
, which uses the most recent OpenAI GPT-4 turbo model (currently gpt-4-0125-preview
). #396
gpt-4-1106-preview
and gpt-4-0125-preview
.-o json_object 1
option which will cause their output to be returned as a valid JSON object. #373
keys.json
file for storing API keys is now created with 600
file permissions. #351
>1.0
. It is possible this could cause compatibility issues with LLM plugins that also depend on that library. #325
llm chat
command. #376
LLM_OPENAI_SHOW_RESPONSES=1
environment variable now outputs much more detailed information about the HTTP request and response made to OpenAI (and OpenAI-compatible) APIs. #404
llm chat -m gpt-4-turbo
or llm chat -m 4t
. #323
-o seed 1
option for OpenAI models which sets a seed that can attempt to evaluate the prompt deterministically. #324
llm embed -c "text"
did not correctly pick up the configured default embedding model. #317
LLM now supports the new OpenAI gpt-3.5-turbo-instruct
model, and OpenAI completion (as opposed to chat completion) models in general. #284
llm -m gpt-3.5-turbo-instruct 'Reasons to tame a wild beaver:'
OpenAI completion models like this support a -o logprobs 3
option, which accepts a number between 1 and 5 and will include the log probabilities (for each produced token, what were the top 3 options considered by the model) in the logged response.
llm -m gpt-3.5-turbo-instruct 'Say hello succinctly' -o logprobs 3
You can then view the logprobs
that were recorded in the SQLite logs database like this:
sqlite-utils "$(llm logs path)" \
'select * from responses order by id desc limit 1' | \
jq '.[0].response_json' -r | jq
Truncated output looks like this:
[
{
"text": "Hi",
"top_logprobs": [
{
"Hi": -0.13706253,
"Hello": -2.3714375,
"Hey": -3.3714373
}
]
},
{
"text": " there",
"top_logprobs": [
{
" there": -0.96057636,
"!\"": -0.5855763,
".\"": -3.2574513
}
]
}
]
Also in this release:
llm.user_dir()
function, used by plugins, now ensures the directory exists before returning it. #275
LLM_OPENAI_SHOW_RESPONSES=1
environment variable for displaying the full HTTP response returned by OpenAI compatible APIs. #286
llm embed-multi
command now has a --batch-size X
option for setting the batch size to use when processing embeddings - useful if you have limited memory available. #273
collection.embed_multi()
method also now accepts an optional batch_size=int
argument.llm embed-multi --files
relating to handling of directories. Thanks, ealvar3z. #274, #280
The two major features in this release are the llm chat command and support for embedding binary data.
The new llm chat
command starts an ongoing chat conversation with a model in your terminal. It works with all models supported by LLM and its plugins, including locally installed models such as Llama 2. #231
This offers a big performance boost for local models, since they don't need to be freshly loaded into memory for each prompt.
Here's an example chat with Llama 2 13B, provided by the llm-mlc plugin.
llm chat -m mlc-chat-Llama-2-13b-chat-hf-q4f16_1
This starts a chat session:
Type 'exit' or 'quit' to exit
Type '!multi' to enter multiple lines, then '!end' to finish
> Who are you?
Hello! I'm just an AI, here to assist you with any questions you may have.
My name is LLaMA, and I'm a large language model trained to provide helpful
and informative responses to a wide range of questions and topics. I'm here
to help you find the information you need, while ensuring a safe and
respectful conversation. Please feel free to ask me anything, and I'll do my
best to provide a helpful and accurate response.
> Tell me a joke about otters
Sure, here's a joke about otters:
Why did the otter go to the party?
Because he heard it was a "whale" of a time!
(Get it? Whale, like a big sea mammal, but also a "wild" or "fun" time.
Otters are known for their playful and social nature, so it's a lighthearted
and silly joke.)
I hope that brought a smile to your face! Do you have any other questions or
topics you'd like to discuss?
> exit
Chat sessions are logged to SQLite - use llm logs
to view them. They can accept system prompts, templates and model options - consult the chat documentation for details.
LLM's embeddings feature has been expanded to provide support for embedding binary data, in addition to text. #254
This enables models like CLIP, supported by the new llm-clip plugin.
CLIP is a multi-modal embedding model which can embed images and text into the same vector space. This means you can use it to create an embedding index of photos, and then search for the embedding vector for "a happy dog" and get back images that are semantically closest to that string.
To create embeddings for every JPEG in a directory stored in a photos
collection, run:
llm install llm-clip
llm embed-multi photos --files photos/ '*.jpg' --binary -m clip
Now you can search for photos of racoons using:
llm similar photos -c 'raccoon'
This spits out a list of images, ranked by how similar they are to the string "raccoon":
{"id": "IMG_4801.jpeg", "score": 0.28125139257127457, "content": null, "metadata": null}
{"id": "IMG_4656.jpeg", "score": 0.26626441704164294, "content": null, "metadata": null}
{"id": "IMG_2944.jpeg", "score": 0.2647445926996852, "content": null, "metadata": null}
...
llm
starts running. #256
llm plugins --all
option includes builtin plugins in the list of plugins. #259
llm embed-db
family of commands has been renamed to llm collections
. #229
llm embed-multi --files
now has an --encoding
option and defaults to falling back to latin-1
if a file cannot be processed as utf-8
. #225
llm chat
now works for models with API keys. #247
llm chat -o
for passing options to a model. #244
llm chat --no-stream
option. #248
LLM_LOAD_PLUGINS
environment variable. #256
llm plugins --all
option for including builtin plugins. #259
llm embed-db
has been renamed to llm collections
. #229
llm embed -c
option was treated as a filepath, not a string. Thanks, mhalle. #263