SIA/sia/llm_engine/qwq_llm_engine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unsloth should be imported before transformers to ensure all optimizations are applied.\n",
    "from unsloth import FastLanguageModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from threading import Thread\n",
    "from transformers import AutoTokenizer, TextIteratorStreamer, pipeline\n",
    "from xml_schema_validator import XmlLogitsProcessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature = 0.6\n",
    "model_path = \"/root/models/notebook\"\n",
    "xml_schema_text = Path(\"/root/sia/action_schema.xsd\").read_text()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    model_path,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load model\n",
    "model, _returned_tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_path,\n",
    "    load_in_4bit = True, # False for LoRA 16bit\n",
    "    fast_inference = True, # Enable vLLM fast inference\n",
    "    gpu_memory_utilization = 0.8, # Reduce if out of memory\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create inference pipeline with memory-efficient settings\n",
    "pipeline = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    return_full_text=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logits_processor = XmlLogitsProcessor(tokenizer, xml_schema_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": \"Always respond in a <write_stdout></write_stdout> xml block.\"},\n",
    "    {\"role\": \"user\", \"content\": \"Hi, how are you?\"},\n",
    "    {\"role\": \"assistant\", \"content\": \"\"},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = tokenizer.apply_chat_template(\n",
    "    messages,\n",
    "    tokenize=False,\n",
    "    add_generation_prompt=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "streamer = TextIteratorStreamer(\n",
    "    tokenizer,\n",
    "    skip_prompt=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "generation_kwargs = {\n",
    "    \"text_inputs\": text,\n",
    "    \"do_sample\": True,\n",
    "    \"temperature\": temperature,\n",
    "    \"streamer\": streamer,\n",
    "    \"use_cache\": True,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "generation_kwargs[\"logits_processor\"] = [logits_processor.copy()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "generation_thread = Thread(\n",
    "    target=pipeline,\n",
    "    kwargs=generation_kwargs\n",
    ")\n",
    "\n",
    "generation_thread.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for text in streamer:\n",
    "    print(text, end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "generation_thread.join()"
   ]
  }
 ],
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