Skip to main content

transformers library new generate API

Project description

synthetic-transformers

Package structure

SyntheticTransformer

Class that will substitute the HuggingFace's PreTrainedModel API. The idea is to use one HuggingFace Transformer and a Tokenizer to initialize this class together with a prompt template and some special plugins as Hooks, Components and Commands. This will create an instance that then you can call as usual with the usual .generate() method.

The idea of this plugins is to give you more control over the generation and allow the LLM to interact with tools in a more dynamic manner.

Hooks

Hooks are one of the two main ways to change how your LLM generates text, they can be triggered by many events and will have access to needed information, such as generated text and others, depending on the activation. There are the following hook types:

  • on-token: this hook runs when a token is generated by the model, it has access to the generated text and can modify it freely.
  • on-eos: this hook runs when the end of generation exception has been raised.

Commands

Commands are executable functions that can be called during generation.

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

synthetic_transformers-0.2.4.tar.gz (5.8 kB view details)

Uploaded Source

Built Distribution

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

synthetic_transformers-0.2.4-py3-none-any.whl (8.8 kB view details)

Uploaded Python 3

File details

Details for the file synthetic_transformers-0.2.4.tar.gz.

File metadata

  • Download URL: synthetic_transformers-0.2.4.tar.gz
  • Upload date:
  • Size: 5.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.4 CPython/3.13.1 Linux/6.11.0-1018-azure

File hashes

Hashes for synthetic_transformers-0.2.4.tar.gz
Algorithm Hash digest
SHA256 21b6f00198224ce6a7de00f938423a843115e90e827bc6a7b81ea070bb0d57dc
MD5 a2adbad184da7920bf5be35c3eab9bb0
BLAKE2b-256 d53bd83d13f4b4f25eaa1e3de1e16b7e293a77f37b4ffeeb9950b46f4373815c

See more details on using hashes here.

File details

Details for the file synthetic_transformers-0.2.4-py3-none-any.whl.

File metadata

File hashes

Hashes for synthetic_transformers-0.2.4-py3-none-any.whl
Algorithm Hash digest
SHA256 2babb9158a66df071e86fd53a2221c7a2456ae2f9b22e19a928dd1ee6971f76a
MD5 523d5f8958a312ad319bafea0f1f8e2b
BLAKE2b-256 327b8c18dcbf34c723f73507ec11b6b9c4f9fd0e044931a9d5bd15b79926a7e6

See more details on using hashes here.

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