unsloth/cli/commands/inference.py
Roland Tannous 47654cb91c Final cleanup
2026-03-12 18:28:04 +00:00

69 lines
2.4 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
import sys
from typing import Optional
import typer
def inference(
model: str = typer.Argument(..., help = "HF model id or local path."),
prompt: str = typer.Argument(..., help = "Prompt to send to the model."),
hf_token: Optional[str] = typer.Option(
None, "--hf-token", envvar = "HF_TOKEN", help = "Hugging Face token if needed."
),
temperature: float = typer.Option(0.7, "--temperature"),
top_p: float = typer.Option(0.9, "--top-p"),
top_k: int = typer.Option(40, "--top-k"),
max_new_tokens: int = typer.Option(256, "--max-new-tokens"),
repetition_penalty: float = typer.Option(1.1, "--repetition-penalty"),
system_prompt: str = typer.Option(
"",
"--system-prompt",
help = "Optional system prompt to prepend.",
),
max_seq_length: int = typer.Option(2048, "--max-seq-length"),
load_in_4bit: bool = typer.Option(True, "--load-in-4bit/--no-load-in-4bit"),
):
"""Run a single inference using the specified model."""
from studio.backend.core import ModelConfig, get_inference_backend
inference_backend = get_inference_backend()
model_config = ModelConfig.from_ui_selection(
dropdown_value = model, search_value = None, hf_token = hf_token, is_lora = False
)
if not model_config:
typer.echo("Could not resolve model config", err = True)
raise typer.Exit(code = 1)
if not inference_backend.load_model(
config = model_config,
max_seq_length = max_seq_length,
load_in_4bit = load_in_4bit,
hf_token = hf_token,
):
typer.echo("Model load failed", err = True)
raise typer.Exit(code = 1)
messages = [{"role": "user", "content": prompt}]
stream = inference_backend.generate_chat_response(
messages = messages,
system_prompt = system_prompt,
temperature = temperature,
top_p = top_p,
top_k = top_k,
max_new_tokens = max_new_tokens,
repetition_penalty = repetition_penalty,
)
typer.echo("Assistant:", nl = True)
previous = ""
for chunk in stream:
delta = chunk[len(previous) :]
if delta:
sys.stdout.write(delta)
sys.stdout.flush()
previous = chunk
sys.stdout.write("\n")
sys.stdout.flush()