LLM Chat Interface — hf_chat • huggingfaceR

Have a conversation with an open-source language model via the Inference Providers API. Tool calling support depends on the model/provider. The default chat model is optimized for a low-friction first call; for tool-calling examples, use a tool-capable model such as "Qwen/Qwen2.5-72B-Instruct".

Usage

hf_chat(
  message,
  system = NULL,
  model = hf_default_model("chat"),
  max_tokens = 500,
  temperature = 0.7,
  token = NULL,
  tools = NULL,
  tool_choice = NULL,
  stream = FALSE,
  callback = NULL,
  image = 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.
tools: A list of tool definitions created by hf_tool(), or NULL.
tool_choice: Character string or list controlling tool use. The public Hugging Face router currently supports "auto" and "none"; a tool name, "required", or full list can be used with compatible custom endpoints. Default: NULL.
stream: Logical. If TRUE, stream response deltas and return the reassembled final response. Default: FALSE.
callback: Function called with each streamed text delta. When NULL and stream = TRUE, deltas are printed to the console.
image: Optional image input for vision-capable chat models. Can be a URL, local file path, raw vector, or list/vector of those.
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, and tool_calls.

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")

# Stream response deltas
hf_chat("Reply with exactly: OK", stream = TRUE)
} # }