Llama v1.3.8 (LTS)

Model name: llama_instruct_local

About Llama

LLaMA is a decoder-only transformer model designed for text generation tasks. It uses the standard Transformer architecture without an encoder, processing input tokens autoregressively to predict the next token in sequence. Pre-trained on a large-scale corpus of publicly available text, LLaMA is capable of handling various natural language tasks, including chat, code generation, summarization, and question answering.

Read more about Llama on Wikipedia.

Supported aidb operations

• decode_text
• decode_text_batch

Supported models

See more in the Support matrix.

Creating the default model

SELECT aidb.create_model('my_llama_model', 'llama_instruct_local');

Creating a specific model

SELECT aidb.create_model(
  'another_llama_model',
  'llama_instruct_local',
  '{"model": "HuggingFaceTB/SmolLM2-135M-Instruct", "revision": "main"}'::JSONB
)

Running the model

SELECT aidb.decode_text('llama_instruct', 'Why is the sky blue?');

Model configuration settings

The following configuration settings are available for Llama models:

• model — The Llama model to use. The default is TinyLlama/TinyLlama-1.1B-Chat-v1.0.
• revision — The revision of the model to use. The default is main.
• system_prompt — Optional. Foundational instructions to guide general LLM responses.
• use_flash_attention — Indicate if the model uses flash attention. The default is false.
• seed — The random seed to use for sampling. The default is 1599222198345926291.
• temperature — The temperature to use for sampling. The default is 0.2.
• sample_len — The maximum number of tokens to generate. The default is 64.
• repeat_last_n — The number of tokens to consider for the repetition penalty. The default is 64.
• repeat_penalty — The repetition penalty to use. The default is 1.1.
• top_p — Cumulative probability threshold for filtering the token distribution. The default is 0.9.
• use_kv_cache — Enables reuse of attention key/value pairs during generation for faster decoding. The default is true.

Model credentials

No credentials are required for local Llama models.