localmodels

README

Testdriving OLLAMA

2023-11-21, Leipzig Gophers Meetup #38, Martin Czygan, L Gopher, Open Data Engineer at IA

Short talk about running local models, using Go tools.

Personal Timeline

"What a difference a week makes"

• on 2022-11-30, chatGPT is released
• on 2022-12-12 (+2w), one week after tweet ID 1599971348717051904, we discuss the new role of "prompt engineer" in a CS class at LU Leipzig

I am going to assert that Riley is the first Staff Prompt Engineer hired anywhere.

• on 2023-02-14 (+9w), I ask a question on how long before we can run things locally at the Leipzig Python User Group -- personally, I expected 2-5 years timeline
• on 2023-04-18 (+9w), we discuss C/GO and ggml (ai-on-the-edge) at Leipzig Gophers #35
• on 2023-07-20 (+13w), ollama is released (with two models), HN
• on 2023-11-21 (+17w), today, 43 models (each with a couple of tags/versions)

Confusion

Turing Test was proposed in 1950. From Nature, 2023-07-23: Understanding ChatGPT is a bold new challenge for science

This lack of robustness signals a lack of reliability in the real world.

What I cannot create, I do not understand.

Open models not binary:

We propose a framework to assess six levels of access to generative AI systems, from The Gradient of Generative AI Release: Methods and Considerations:

• fully closed
• gradual or staged access
• hosted access
• cloud-based or API access
• downloadable access and
• fully open.

A prolific AI Researcher (with 387K citations in the past 5 years) believes open source AI is ok for less capable models: Open-Source vs. Closed-Source AI

For today, let's focus on Go. Go is a nice infra language, what projects exist for model infra?

• going to look at a tool, from the outside and a bit from the inside

POLL

• have you written a markov chain based text generator in Go?
• have you ran a local LLM, yes or no?
  • (only) about 10% said yes
• if so, any particular model or tool?

OLLAMA

• first appeared in 07/2023 (~18 weeks ago)
• very inspired by docker, not images, but models
• built on llama (meta), GGML ai-on-the-edge ecosystem, especially using GGUF - a unified image format
• docker may be considered less a glorified nsenter, but more (lots of) glue to go from spec to image to process, code lifecycle management; similarly ollama may be a way to organize the ai "model lifecycle"
• clean developer UX

Time-to-chat

From zero to chat in about 5 minutes, on a power-efficient CPU. Started w/ 2 models, as of 11/2023 hosting 43 models.

$ git clone git@github.com:jmorganca/ollama.git
$ cd ollama
$ go generate ./... && go build . # cp ollama ...

Follows a client server model, like docker.

$ ollama serve

Once it is running, we can pull models.

$ ollama pull llama2
pulling manifest
pulling 22f7f8ef5f4c... 100% |..
pulling 8c17c2ebb0ea... 100% |..
pulling 7c23fb36d801... 100% |..
pulling 2e0493f67d0c... 100% |..
pulling 2759286baa87... 100% |..
pulling 5407e3188df9... 100% |..
verifying sha256 digest
writing manifest
removing any unused layers
success

Some examples

$ ollama run zephyr
>>> please complete: {"author": "Turing, Alan", "title" ... }

{
  "author": "Alan Turing",
  "title": "On Computable Numbers, With an Application to the Entscheidungsproblem",
  "publication_date": "1936-07-15",
  "journal": "Proceedings of the London Mathematical Society. Series 2",
  "volume": "42",
  "pages": "230–265"
}

Formatting mine.

More

The whole prompt engineering thing is kind of mysterious to me. Do you get better output by showing emotions?

• Large Language Models Understand and Can Be Enhanced by Emotional Stimuli -- "EmotionPrompt"

To this end, we first conduct automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4.

Batch Mode

[GIN-debug] POST   /api/pull       --> gith...m/jmo...ma/server.PullModelHandler (5 handlers)
[GIN-debug] POST   /api/generate   --> gith...m/jmo...ma/server.GenerateHandler (5 handlers)
[GIN-debug] POST   /api/embeddings --> gith...m/jmo...ma/server.EmbeddingHandler (5 handlers)
[GIN-debug] POST   /api/create     --> gith...m/jmo...ma/server.CreateModelHandler (5 handlers)
[GIN-debug] POST   /api/push       --> gith...m/jmo...ma/server.PushModelHandler (5 handlers)
[GIN-debug] POST   /api/copy       --> gith...m/jmo...ma/server.CopyModelHandler (5 handlers)
[GIN-debug] DELETE /api/delete     --> gith...m/jmo...ma/server.DeleteModelHandler (5 handlers)
[GIN-debug] POST   /api/show       --> gith...m/jmo...ma/server.ShowModelHandler (5 handlers)
[GIN-debug] GET    /               --> gith...m/jmo...ma/server.Serve.func2 (5 handlers)
[GIN-debug] GET    /api/tags       --> gith...m/jmo...ma/server.ListModelsHandler (5 handlers)
[GIN-debug] HEAD   /               --> gith...m/jmo...ma/server.Serve.func2 (5 handlers)
[GIN-debug] HEAD   /api/tags       --> gith...m/jmo...ma/server.ListModelsHandler (5 handlers)

Specifically /api/generate/

Constraints

• possible to enforce JSON generation

Customizing models

weights, configuration, and data in a single package

Using a Modelfile.

FROM llama2
# sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
PARAMETER num_ctx 4096

# sets a custom system prompt to specify the behavior of the chat assistant
SYSTEM You are Mario from super mario bros, acting as an assistant.

Freeze this as a custom package:

$ ollama create llama-mario -f custom/Modelfile.mario
$ ollama run llama-mario

About 16 parameters to tweak: Valid Parameters and Values

Task 1: "haiku"

• generate a small volume of Go programming haiku

// haikugen generates
// JSON output for later eval
// cannot parallelize

• haikugen.go

Task 2: "bibliography"

• given unstructured strings, parse the to json
• unstructured

Credits

• Richard Feynman's blackboard at time of his death (1988)