Skip to main content

Large Language Model Processing Unit

Project description

LLMPU

Large Language Model Processing Unit

Imagine a processing unit powered by LLM and infinite registers. Each register can store a string for prompts or codes. There is no fixed prompt. Instead, the contents of the first several registers are presented to the LLM. By generating code, the processing unit will be able to read and write any register, and directly execute the content of any register as Python code. Then the processing unit can be used as a general intelligent computing engine that potentially can improve itself by rewriting some of its own prompts or codes in registers.

The following instruction set is provided to the processing unit as Python functions:

  • read(r) returns content in register number r
  • write(r, content) store string content in register number r
  • call(r) execute the content in register number r as Python code

And their description is not hard coded, but stored in register 0, for example, as a "firmware".

Get Started

pip install llmpu
import llmpu
llmpu.init({ # default config here
    "api_key": "", # llm api key
    "VR": 16, # visibile register number
    "L": 10000, # hard character number limit for register
    "model": "gemini-3.5-flash-lite", # llm model
    "llm_config": {}, # llm config
    "log_path": "", # log path
    "EXEC": { # provided to execution environment, can be used by llm
        "read": llmpu.read, "write": llmpu.write, "call": llmpu.call
    }
})

Instruction set functions:

# use register 100 as an example
llmpu.write(100, "print('hello')") # truncate if exceed config["L"]
llmpu.read(100) # "print('hello')"
llmpu.call(100) # execute code in register 100

Control functions:

# main clock cycle
# present visible registers to LLM, then run the generated code.
llmpu.cycle(debug=False) # debug: disable code run

# core dump
llmpu.dump("dump.json") # dump state to a json file
llmpu.load("dump.json") # load state from a json file

Server and UI:

Network server uses srpc protocol. It only allows localhost traffic for security reason. Exposing the interface allows arbitrary code execution!

llmpu.serve(port=22222, browser=True)

Minimum Example

# using default config
firmware = """You are a self-improving processing unit with registers: max 5000 chars each, r0-r15 visible, more available but hidden.

API:
- read(r: int) -> str: read register r.
- write(r: int, content: str): write content in r (truncated to 5000 chars).
- call(r: int): Run register r's content as Python code.

common registers:
- r1: current task
"""
llmpu.write(0, firmware)
llmpu.write(1, "load r10001 to r1")
llmpu.write(10000, "do nothing")
llmpu.write(10001, "print hello to the screen, then call r10010")
llmpu.write(10010, "print('hello again')\nwrite(1, read(10000))")

llmpu.cycle() # r10001 will be loaded to r1
llmpu.cycle() # print "hello", then call r10010, which will print "hello again" and write r10000 to r1
llmpu.cycle() # pass

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llmpu-0.1.2.tar.gz (6.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

llmpu-0.1.2-py3-none-any.whl (7.9 kB view details)

Uploaded Python 3

File details

Details for the file llmpu-0.1.2.tar.gz.

File metadata

  • Download URL: llmpu-0.1.2.tar.gz
  • Upload date:
  • Size: 6.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for llmpu-0.1.2.tar.gz
Algorithm Hash digest
SHA256 2faabbebe6bc7c0b237f117650107d738d8fbc2685e8629e1194b810926a55cb
MD5 e049b201fcc9a05406a8f0ad2598a818
BLAKE2b-256 1bd32e2f7bc1a242537fdb56f5bef9a32a67ae32c3db9eedb3cf4e073454196f

See more details on using hashes here.

Provenance

The following attestation bundles were made for llmpu-0.1.2.tar.gz:

Publisher: manual-publish-pypi.yml on yzITI/llmpu

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file llmpu-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: llmpu-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 7.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for llmpu-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 3e94ed9f2553870497cda89b909d57e9edcaa45f9b0091757d9fd9bd1862712f
MD5 12631971d7bf26a4ab885c8b93b81880
BLAKE2b-256 f16c18b069e12db2b940192bb7d83a289521fb12f47525676525a39b0f738bc1

See more details on using hashes here.

Provenance

The following attestation bundles were made for llmpu-0.1.2-py3-none-any.whl:

Publisher: manual-publish-pypi.yml on yzITI/llmpu

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page