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Context: the project archive (KXKM_Batterie_Parallelator-main) had no git history locally; a fresh repository is needed to host it on git.saillant.cc (electron/KXKM_Batterie_Parallelator). Approach: initialize a new repo on branch main, stage the archive content, and harden .gitignore before the first commit. Changes: - Import the full project tree: firmware/, firmware-idf/, firmware-rs/, iosApp/, kxkm-bmu-app/, kxkm-api/, hardware/, docs/, specs/, scripts/, models/, tests/ - Keep project dotfiles tracked despite the trailing '.*' ignore rule: .github/, .claude/, .superpowers/, .gitattributes, .markdownlint.json - Extend .gitignore: firmware/src/credentials.h (local secrets, template kept), kxkm-bmu-app/**/build/ (66 MB compiled iOS framework), .remember/ (session data) Impact: the project can now be maintained on the self-hosted Gitea forge with a clean, secret-free initial history.
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
"""
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inference_server.py — Qwen2.5-7B inference with LoRA adapter for battery diagnostics.
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Loads the base model + LoRA adapter once, serves generation requests.
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Designed to run as a long-lived process inside the soh-llm Docker container.
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"""
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from __future__ import annotations
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import logging
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import time
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from typing import Any
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from config import config
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from prompt_template import build_messages, extract_severity, clean_diagnostic
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log = logging.getLogger("inference")
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# Global model/tokenizer (loaded once at startup)
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_model = None
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_tokenizer = None
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def load_model() -> None:
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"""Load base model + LoRA adapter into GPU memory."""
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global _model, _tokenizer
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log.info("Loading model: %s + %s", config.base_model, config.lora_adapter)
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from unsloth import FastLanguageModel
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_model, _tokenizer = FastLanguageModel.from_pretrained(
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model_name=config.lora_adapter,
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max_seq_length=config.max_seq_length,
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dtype=None,
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load_in_4bit=True,
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)
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FastLanguageModel.for_inference(_model)
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log.info("Model loaded successfully")
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def generate_diagnostic(scores: dict[str, Any]) -> dict[str, Any]:
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"""Generate a diagnostic for a single battery from its ML scores.
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Args:
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scores: Battery scoring data from Phase 2 API.
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Returns:
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Dict with keys: battery, diagnostic, severity, generation_time_ms
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"""
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if _model is None or _tokenizer is None:
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raise RuntimeError("Model not loaded. Call load_model() first.")
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messages = build_messages(scores)
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text = _tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = _tokenizer(text, return_tensors="pt").to(_model.device)
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t0 = time.monotonic()
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outputs = _model.generate(
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**inputs,
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max_new_tokens=config.max_new_tokens,
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temperature=config.temperature,
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do_sample=True,
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top_p=config.top_p,
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)
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generation_ms = int((time.monotonic() - t0) * 1000)
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generated = _tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True,
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)
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diagnostic_text = clean_diagnostic(generated)
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severity = extract_severity(generated)
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return {
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"battery": scores.get("battery", -1),
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"diagnostic": diagnostic_text,
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"severity": severity,
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"generation_time_ms": generation_ms,
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}
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