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9a1014e | 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 | """Inferencia local con el GGUF Qwen3-4B-Instruct mediante llama.cpp."""
from __future__ import annotations
import os
import threading
import time
from pathlib import Path
from typing import Iterator
from dotenv import load_dotenv
from llama_cpp import Llama
load_dotenv()
DEFAULT_MODEL = "AlbertiTechnology/qwen3-4b-instruct-gguf"
DEFAULT_MODEL_PATH = (
Path(__file__).resolve().parent
/ "MODELS"
/ "qwen3-4b-instruct-gguf"
/ "model.gguf"
)
MODEL_ALIASES = {
"Qwen/Qwen3-4B": DEFAULT_MODEL,
"Qwen/Qwen3-4B-Instruct-2507": DEFAULT_MODEL,
"Qwen/Qwen2.5-7B-Instruct": DEFAULT_MODEL,
}
_load_lock = threading.Lock()
_generation_lock = threading.Lock()
_model: Llama | None = None
def local_model_path() -> Path:
return Path(os.getenv("LOCAL_MODEL_PATH", str(DEFAULT_MODEL_PATH))).resolve()
def _load_local_model() -> Llama:
global _model
if _model is not None:
return _model
with _load_lock:
if _model is not None:
return _model
model_path = local_model_path()
if not model_path.is_file():
raise RuntimeError(f"Local GGUF model was not found at {model_path}")
print(f"Loading local GGUF model from {model_path}...", flush=True)
_model = Llama(
model_path=str(model_path),
n_ctx=int(os.getenv("LOCAL_MODEL_CONTEXT_SIZE", "2048")),
n_threads=int(os.getenv("LOCAL_MODEL_THREADS", "2")),
n_threads_batch=int(os.getenv("LOCAL_MODEL_BATCH_THREADS", "2")),
n_batch=int(os.getenv("LOCAL_MODEL_BATCH_SIZE", "256")),
n_gpu_layers=int(os.getenv("LOCAL_MODEL_GPU_LAYERS", "0")),
verbose=os.getenv("LLAMA_CPP_VERBOSE", "false").lower() == "true",
)
print("Local GGUF model loaded.", flush=True)
return _model
def generate_response(
text: str,
model: str = DEFAULT_MODEL,
max_new_tokens: int | None = None,
) -> str:
"""Genera una respuesta usando exclusivamente el GGUF local."""
return "".join(generate_response_stream(text, model, max_new_tokens)).strip()
def generate_response_stream(
text: str,
model: str = DEFAULT_MODEL,
max_new_tokens: int | None = None,
) -> Iterator[str]:
"""Genera la respuesta local y entrega cada fragmento apenas esta disponible."""
del model
local_model = _load_local_model()
token_limit = max_new_tokens or int(
os.getenv("LOCAL_MODEL_MAX_NEW_TOKENS", "256")
)
context_size = int(os.getenv("LOCAL_MODEL_CONTEXT_SIZE", "2048"))
prompt_budget = max(256, context_size - token_limit - 192)
prompt_tokens = local_model.tokenize(
text.encode("utf-8"),
add_bos=False,
special=True,
)
if len(prompt_tokens) > prompt_budget:
marker = "\n\n[Contexto intermedio recortado por límite de tokens]\n\n"
marker_tokens = local_model.tokenize(
marker.encode("utf-8"),
add_bos=False,
special=True,
)
available = max(1, prompt_budget - len(marker_tokens))
start_count = int(available * 0.6)
end_count = available - start_count
kept_tokens = (
prompt_tokens[:start_count]
+ marker_tokens
+ prompt_tokens[-end_count:]
)
text = local_model.detokenize(kept_tokens).decode(
"utf-8", errors="ignore"
)
print(
"Local prompt truncated | "
f"original_tokens={len(prompt_tokens)} | kept_tokens={len(kept_tokens)} "
f"| budget={prompt_budget}",
flush=True,
)
messages = [
{
"role": "system",
"content": (
"Responde en español de forma directa y concisa. Usa el contexto "
"recuperado de Chroma como fuente principal. Si el contexto no "
"alcanza, dilo claramente. No muestres razonamiento interno."
),
},
{"role": "user", "content": f"/no_think\n{text}"},
]
with _generation_lock:
started_at = time.monotonic()
first_chunk = True
print(
f"Local generation prompt submitted | max_tokens={token_limit}",
flush=True,
)
response = local_model.create_chat_completion(
messages=messages,
max_tokens=token_limit,
temperature=0.7,
top_p=0.8,
top_k=20,
repeat_penalty=1.05,
stream=True,
)
for chunk in response:
delta = chunk["choices"][0].get("delta", {})
content = delta.get("content") or delta.get("reasoning_content")
if content:
if first_chunk:
print(
"Local model produced a chunk | "
f"elapsed={time.monotonic() - started_at:.1f}s",
flush=True,
)
first_chunk = False
yield str(content)
if __name__ == "__main__":
print(generate_response("Responde solamente: Qwen GGUF local OK"))
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