LLM Chat Interface — hf_chat • huggingfaceR

Have a conversation with an open-source language model via the Inference Providers API.

Usage

hf_chat(
  message,
  system = NULL,
  model = "meta-llama/Llama-3.1-8B-Instruct",
  max_tokens = 500,
  temperature = 0.7,
  token = NULL,
  endpoint_url = NULL,
  ...
)

Arguments

message: Character string. The user message to send to the model.
system: Character string or NULL. Optional system prompt to set behavior.
model: Character string. Model ID from Hugging Face Hub. Default: "meta-llama/Llama-3.1-8B-Instruct". Use `:provider` suffix to select a specific provider (e.g., "meta-llama/Llama-3.1-8B-Instruct:together").
max_tokens: Integer. Maximum tokens to generate. Default: 500.
temperature: Numeric. Sampling temperature (0-2). Default: 0.7.
token: Character string or NULL. API token for authentication.
endpoint_url: Character string or NULL. A custom Inference Endpoint URL. When provided, requests are sent to this URL instead of the public Inference API. The endpoint must support the chat completions format.
...: Additional parameters passed to the model.

Value

A tibble with columns: role, content, model, tokens_used

Examples

if (FALSE) { # \dontrun{
# Simple question
hf_chat("What is the capital of France?")

# With system prompt
hf_chat(
  "Explain gradient descent",
  system = "You are a statistics professor. Use simple analogies."
)

# Use a specific provider
hf_chat("Hello!", model = "meta-llama/Llama-3-8B-Instruct:together")
} # }