File size: 15,924 Bytes
413e454 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 | from __future__ import annotations
import argparse
import os
import shutil
import subprocess
import sys
from pathlib import Path
MODEL_TITLE = "LumynaX Infused SmolLM2 360M Instruct GGUF"
def _build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description=f"Run a local GGUF chat for {MODEL_TITLE}.")
parser.add_argument(
"--prompt",
default=None,
help="Prompt to send to the model.",
)
parser.add_argument("--system-prompt", default="", help="Optional system prompt override.")
parser.add_argument(
"--interactive",
action="store_true",
help="Start an interactive terminal chat instead of running a single prompt.",
)
parser.add_argument("--max-new-tokens", type=int, default=192)
parser.add_argument("--ctx-size", type=int, default=4096)
parser.add_argument("--temperature", type=float, default=0.1)
parser.add_argument("--threads", type=int, default=max(1, os.cpu_count() or 1))
parser.add_argument("--llama-cli", default="", help="Optional explicit path to llama-cli.")
parser.add_argument(
"--cache-local",
action="store_true",
help="Copy the GGUF into LOCALAPPDATA before running. Useful when a runtime cannot read network paths.",
)
parser.add_argument("--reasoning", choices=("on", "off", "auto"), default="off")
parser.add_argument(
"--reasoning-format",
choices=("auto", "none", "deepseek", "deepseek-legacy"),
default="auto",
)
parser.add_argument("--reasoning-budget", type=int, default=None)
return parser
def _preferred_gguf(root: Path) -> Path:
gguf_candidates = sorted(root.glob("*.gguf"))
if not gguf_candidates:
raise SystemExit(f"No GGUF file was found in {root}")
for path in gguf_candidates:
if "-q" in path.stem.lower():
return path
return gguf_candidates[0]
def _local_model_path(model_path: Path, *, cache_local: bool = False) -> Path:
if not cache_local:
return model_path
local_app_data = Path(os.environ.get("LOCALAPPDATA", Path.home() / "AppData" / "Local"))
cache_dir = local_app_data / "tinyluminax" / "gguf-cache"
cache_dir.mkdir(parents=True, exist_ok=True)
cached_path = cache_dir / model_path.name
source_stat = model_path.stat()
if (
not cached_path.exists()
or cached_path.stat().st_size != source_stat.st_size
or cached_path.stat().st_mtime_ns < source_stat.st_mtime_ns
):
print(f"Caching GGUF locally at {cached_path}", file=sys.stderr)
shutil.copy2(model_path, cached_path)
return cached_path
def _discover_llama_cli(explicit_path: str) -> Path | None:
candidates: list[Path] = []
if explicit_path.strip():
candidates.append(Path(explicit_path.strip()))
for env_var in ("LLAMA_CPP_CLI", "LLAMA_CLI_PATH"):
raw_value = os.environ.get(env_var, "").strip()
if raw_value:
candidates.append(Path(raw_value))
for binary_name in ("llama-cli", "llama-cli.exe"):
resolved = shutil.which(binary_name)
if resolved:
candidates.append(Path(resolved))
for candidate in candidates:
if candidate.exists():
return candidate
return None
def _extract_text(response: dict[str, object]) -> str:
choices = response.get("choices", [])
if not isinstance(choices, list) or not choices:
raise RuntimeError("The runtime returned no choices.")
first_choice = choices[0]
if isinstance(first_choice, dict):
message = first_choice.get("message")
if isinstance(message, dict):
content = message.get("content")
if content not in (None, ""):
return str(content).strip()
text = first_choice.get("text")
if text not in (None, ""):
return str(text).strip()
raise RuntimeError("The runtime returned an unsupported response payload.")
def _run_llama_cpp_python(
*,
model_path: Path,
system_prompt: str,
user_prompt: str,
max_new_tokens: int,
ctx_size: int,
temperature: float,
threads: int,
) -> str:
from llama_cpp import Llama
llm = Llama(
model_path=str(model_path),
n_ctx=ctx_size,
n_threads=threads,
n_gpu_layers=0,
chat_format="chat_template.default",
verbose=False,
)
response = llm.create_chat_completion(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
max_tokens=max_new_tokens,
temperature=temperature,
)
return _extract_text(response)
def _run_llama_cli(
*,
llama_cli_path: Path,
model_path: Path,
system_prompt: str,
user_prompt: str,
max_new_tokens: int,
ctx_size: int,
temperature: float,
threads: int,
reasoning: str,
reasoning_format: str,
reasoning_budget: int | None,
) -> None:
command = [
str(llama_cli_path),
"-m",
str(model_path),
"-sys",
system_prompt,
"-p",
user_prompt,
"-cnv",
"-st",
"-n",
str(max_new_tokens),
"-c",
str(ctx_size),
"--reasoning",
reasoning,
"--temp",
str(temperature),
"--threads",
str(threads),
"--no-display-prompt",
]
if reasoning_format != "auto":
command.extend(["--reasoning-format", reasoning_format])
if reasoning_budget is not None:
command.extend(["--reasoning-budget", str(reasoning_budget)])
completed = subprocess.run(
command,
check=False,
capture_output=True,
text=True,
encoding="utf-8",
)
if completed.returncode != 0:
detail = completed.stderr.strip() or completed.stdout.strip() or "llama-cli failed"
raise SystemExit(detail)
stdout = completed.stdout.strip()
if stdout:
print(stdout)
def _print_interactive_banner() -> None:
print("LumynaX interactive terminal chat")
print("Type /reset to clear the conversation, or /quit to exit.")
def _run_interactive_llama_cpp_python(
*,
model_path: Path,
system_prompt: str,
max_new_tokens: int,
ctx_size: int,
temperature: float,
threads: int,
opening_prompt: str | None = None,
reasoning: str = "off",
reasoning_format: str = "auto",
reasoning_budget: int | None = None,
) -> None:
from llama_cpp import Llama
llm = Llama(
model_path=str(model_path),
n_ctx=ctx_size,
n_threads=threads,
n_gpu_layers=0,
chat_format="chat_template.default",
verbose=False,
)
transcript: list[tuple[str, str]] = []
_print_interactive_banner()
pending_prompt = opening_prompt.strip() if opening_prompt and opening_prompt.strip() else None
while True:
try:
if pending_prompt is None:
user_prompt = input("You> ").strip()
else:
user_prompt = pending_prompt
print(f"You> {user_prompt}")
pending_prompt = None
except (EOFError, KeyboardInterrupt):
print("\nExiting LumynaX chat.")
return
if not user_prompt:
continue
lowered_prompt = user_prompt.lower()
if lowered_prompt in ('/quit', '/exit'):
print("Exiting LumynaX chat.")
return
if lowered_prompt == "/reset":
transcript.clear()
print("Conversation reset.")
continue
messages: list[dict[str, str]] = [{"role": "system", "content": system_prompt}]
for transcript_user_prompt, transcript_assistant_response in transcript:
messages.append({"role": "user", "content": transcript_user_prompt})
messages.append({"role": "assistant", "content": transcript_assistant_response})
messages.append({"role": "user", "content": user_prompt})
response = llm.create_chat_completion(
messages=messages,
max_tokens=max_new_tokens,
temperature=temperature,
)
assistant_text = _extract_text(response)
print(f"LumynaX> {assistant_text}")
transcript.append((user_prompt, assistant_text))
def _run_interactive_llama_cli(
*,
llama_cli_path: Path,
model_path: Path,
system_prompt: str,
max_new_tokens: int,
ctx_size: int,
temperature: float,
threads: int,
opening_prompt: str | None = None,
reasoning: str = "off",
reasoning_format: str = "auto",
reasoning_budget: int | None = None,
) -> None:
print("LumynaX interactive terminal chat")
print("Interactive mode already uses llama-cli directly. Use Ctrl+C to exit.")
command = [
str(llama_cli_path),
"-m",
str(model_path),
"-sys",
system_prompt,
"-cnv",
"-n",
str(max_new_tokens),
"-c",
str(ctx_size),
"--reasoning",
reasoning,
"--temp",
str(temperature),
"--threads",
str(threads),
"--simple-io",
]
if reasoning_format != "auto":
command.extend(["--reasoning-format", reasoning_format])
if reasoning_budget is not None:
command.extend(["--reasoning-budget", str(reasoning_budget)])
if opening_prompt and opening_prompt.strip():
command.extend(["-p", opening_prompt.strip()])
completed = subprocess.run(command, check=False)
if completed.returncode != 0:
raise SystemExit(completed.returncode)
def main() -> None:
args = _build_parser().parse_args()
root = Path(__file__).resolve().parent
source_model_path = _preferred_gguf(root)
if hasattr(sys.stdout, "reconfigure"):
sys.stdout.reconfigure(encoding="utf-8")
single_prompt = (args.prompt or "Say hello in one short sentence.").strip()
system_prompt = args.system_prompt.strip() or (
f"You are LumynaX operating from the {MODEL_TITLE} package identity. "
"Be helpful, clear, and honest about provenance."
)
explicit_cli_requested = bool(
args.llama_cli.strip()
or os.environ.get("LLAMA_CPP_CLI", "").strip()
or os.environ.get("LLAMA_CLI_PATH", "").strip()
)
if args.interactive:
llama_cli_path = _discover_llama_cli(args.llama_cli)
if explicit_cli_requested:
if llama_cli_path is None:
raise SystemExit(
"A llama-cli override was requested, but no usable llama-cli binary was found.",
)
_run_interactive_llama_cli(
llama_cli_path=llama_cli_path,
model_path=_local_model_path(source_model_path, cache_local=args.cache_local),
system_prompt=system_prompt,
opening_prompt=args.prompt,
max_new_tokens=args.max_new_tokens,
ctx_size=args.ctx_size,
temperature=args.temperature,
threads=args.threads,
reasoning=args.reasoning,
reasoning_format=args.reasoning_format,
reasoning_budget=args.reasoning_budget,
)
return
model_path = _local_model_path(source_model_path, cache_local=args.cache_local)
try:
_run_interactive_llama_cpp_python(
model_path=model_path,
system_prompt=system_prompt,
opening_prompt=args.prompt,
max_new_tokens=args.max_new_tokens,
ctx_size=args.ctx_size,
temperature=args.temperature,
threads=args.threads,
reasoning=args.reasoning,
reasoning_format=args.reasoning_format,
reasoning_budget=args.reasoning_budget,
)
return
except Exception as exc: # noqa: BLE001
if llama_cli_path is None:
raise SystemExit(
"llama-cpp-python could not load this GGUF package. "
"Install or point LLAMA_CPP_CLI at llama-cli to use the built-in fallback. "
f"Original error: {exc}",
) from exc
print(
f"llama-cpp-python failed; falling back to llama-cli at {llama_cli_path}",
file=sys.stderr,
)
_run_interactive_llama_cli(
llama_cli_path=llama_cli_path,
model_path=model_path,
system_prompt=system_prompt,
opening_prompt=args.prompt,
max_new_tokens=args.max_new_tokens,
ctx_size=args.ctx_size,
temperature=args.temperature,
threads=args.threads,
reasoning=args.reasoning,
reasoning_format=args.reasoning_format,
reasoning_budget=args.reasoning_budget,
)
return
if explicit_cli_requested:
llama_cli_path = _discover_llama_cli(args.llama_cli)
if llama_cli_path is None:
raise SystemExit(
"A llama-cli override was requested, but no usable llama-cli binary was found.",
)
_run_llama_cli(
llama_cli_path=llama_cli_path,
model_path=_local_model_path(source_model_path, cache_local=args.cache_local),
system_prompt=system_prompt,
user_prompt=single_prompt,
max_new_tokens=args.max_new_tokens,
ctx_size=args.ctx_size,
temperature=args.temperature,
threads=args.threads,
reasoning=args.reasoning,
reasoning_format=args.reasoning_format,
reasoning_budget=args.reasoning_budget,
)
return
model_path = _local_model_path(source_model_path, cache_local=args.cache_local)
try:
print(
_run_llama_cpp_python(
model_path=model_path,
system_prompt=system_prompt,
user_prompt=single_prompt,
max_new_tokens=args.max_new_tokens,
ctx_size=args.ctx_size,
temperature=args.temperature,
threads=args.threads,
),
)
return
except Exception as exc: # noqa: BLE001
llama_cli_path = _discover_llama_cli(args.llama_cli)
if llama_cli_path is None:
raise SystemExit(
"llama-cpp-python could not load this GGUF package. "
"Install or point LLAMA_CPP_CLI at llama-cli to use the built-in fallback. "
f"Original error: {exc}",
) from exc
print(
f"llama-cpp-python failed; falling back to llama-cli at {llama_cli_path}",
file=sys.stderr,
)
_run_llama_cli(
llama_cli_path=llama_cli_path,
model_path=model_path,
system_prompt=system_prompt,
user_prompt=single_prompt,
max_new_tokens=args.max_new_tokens,
ctx_size=args.ctx_size,
temperature=args.temperature,
threads=args.threads,
reasoning=args.reasoning,
reasoning_format=args.reasoning_format,
reasoning_budget=args.reasoning_budget,
)
if __name__ == "__main__":
main()
|