IBM BAM

The bam provider integrates with IBM's BAM API, allowing access to various models like meta-llama/llama-2-70b-chat and ibm/granite-13b-chat-v2.

Setup

This provider requires you to install the IBM SDK:

npm install @ibm-generative-ai/node-sdk

Configuration

Configure the BAM provider by specifying the model and various generation parameters. Here is an example of how to configure the BAM provider in your configuration file:

providers:
  - id: bam:chat:meta-llama/llama-2-70b-chat
    config:
      temperature: 0.01
      max_new_tokens: 1024
      prompt:
        prefix: '[INST] '
        suffix: '[/INST] '
  - id: bam:chat:ibm/granite-13b-chat-v2
    config:
      temperature: 0.01
      max_new_tokens: 1024
      prompt:
        prefix: '[INST] '
        suffix: '[/INST] '

Authentication

To use the BAM provider, you need to set the BAM_API_KEY environment variable or specify the apiKey directly in the provider configuration. The API key can also be dynamically fetched from an environment variable specified in the apiKeyEnvar field in the configuration.

export BAM_API_KEY='your-bam-api-key'

API Client Initialization

The BAM provider initializes an API client using the IBM Generative AI Node SDK. The endpoint for the BAM API is configured to https://bam-api.res.ibm.com/.

Configuration

Parameter	Type	Description
top_k	number	Controls diversity via random sampling: lower values make sampling more deterministic.
top_p	number	Nucleus sampling: higher values cause the model to consider more candidates.
typical_p	number	Controls the "typicality" during sampling, balancing between top_k and top_p.
beam_width	number	Sets the beam width for beam search decoding, controlling the breadth of the search.
time_limit	number	Maximum time in milliseconds the model should take to generate a response.
random_seed	number	Seed for random number generator, ensuring reproducibility of the output.
temperature	number	Controls randomness. Lower values make the model more deterministic.
length_penalty	object	Adjusts the length of the generated output. Includes start_index and decay_factor.
max_new_tokens	number	Maximum number of new tokens to generate.
min_new_tokens	number	Minimum number of new tokens to generate.
return_options	object	Options for additional information to return with the output, such as token probabilities.
stop_sequences	string[]	Array of strings that, if generated, will stop the generation.
decoding_method	string	Specifies the decoding method, e.g., 'greedy' or 'sample'.
repetition_penalty	number	Penalty applied to discourage repetition in the output.
include_stop_sequence	boolean	Whether to include stop sequences in the output.
truncate_input_tokens	number	Maximum number of tokens to consider from the input text.

Moderation Parameters

Moderation settings can also be specified to manage content safety and compliance:

Parameter	Type	Description
hap	object	Settings for handling hate speech. Can be enabled/disabled and configured with thresholds.
stigma	object	Settings for handling stigmatizing content. Includes similar configurations as hap.
implicit_hate	object	Settings for managing implicitly hateful content.

Each moderation parameter can include the following sub-parameters: input, output, threshold, and send_tokens to customize the moderation behavior.

Here's an example:

providers:
  - id: bam:chat:ibm/granite-13b-chat-v2
    config:
      moderations:
        hap:
          input: true
          output: true
          threshold: 0.9