support-json-qwen3.5-4b-lora

Support-JSON · Qwen3.5-4B LoRA

A local Russian SaaS support adapter: customer request, company policies and sourced facts → JSON decision and draft reply. It assists an operator; it does not execute refunds, modify accounts or send messages to customers.

Локальный помощник поддержки: разбирает обращение по переданным правилам, предлагает действие и готовит черновик ответа на русском.

Dataset · Animated demo · Evaluation · Capstone package

The video is an animated presentation of real local application captures. Transitions and waiting are shortened. A screenshot is not a quality benchmark; all draft replies require review.

What the adapter predicts

Input: message, history, policies with id / text, facts with typed values and origin, and available capabilities. Unknown facts are null. Customer reports are distinct from system observations.

Nine output fields: category, priority, sentiment, action, recommended_action, reply, missing_info, supported_rule_ids, human_escalation. Company rules are supplied in context for each request. Preserve the provided prompt and schema rather than sending only a naked customer message.

The included code strictly checks JSON, schema and cross-field invariants. It does not repair invalid output or verify every claim in the reply. Correct JSON does not guarantee a correct policy decision.

Run locally

Tested on Windows, Python 3.12, RTX 5070 Ti with 16 GB VRAM, CUDA 12.8. Other hardware is not certified; CPU deployment is not implemented in this example. Base weights are about 9.3 GB on disk; adapter weights are about 130 MB. Allow additional space for downloads and GPU buffers.

python -m venv .venv
.\.venv\Scripts\python.exe -m pip install torch==2.11.0+cu128 --index-url https://download.pytorch.org/whl/cu128
.\.venv\Scripts\python.exe -m pip install huggingface_hub==1.32.0
.\.venv\Scripts\python.exe -c "from huggingface_hub import snapshot_download; snapshot_download('A11Sunday/support-json-qwen3.5-4b-lora', local_dir='support-json', allow_patterns=['*.json','*.txt','*.py','*.safetensors','app/**','LICENSE','NOTICE'])"
.\.venv\Scripts\python.exe -m pip install -r support-json/app/requirements.inference.txt
.\.venv\Scripts\python.exe -m pip install -e support-json/app
.\.venv\Scripts\python.exe -X utf8 -m support_json.demo --model Qwen/Qwen3.5-4B --adapter support-json

Open http://127.0.0.1:7860. The first request downloads/loads the pinned base model; subsequent requests use the loaded model. The server is loopback-only. GPU training or a game on the same card competes for memory and computation. Stop the server when finished to release GPU memory.

For a standalone JSON request using the included synthetic sample:

.\.venv\Scripts\python.exe -X utf8 support-json/predict.py
# Or supply a context file using --input path/to/context.json.

The base revision is pinned to 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a; no custom remote model code is required. The model is a PEFT adapter, not a merged standalone model or GGUF. predict.py forces suggestion-only capabilities. Preserve operator review.

Training

Qwen3.5-4B, text-only, BF16 LoRA r=16 / alpha=32, all-linear, dropout 0, learning rate 1e-4, seed 42, response-only loss, context 2048. Selected run: 512 synthetic train rows × 2 epochs, micro-batch 2, accumulation 4, 128 updates. Soup 0.75.0 / Transformers 5.17.0 / PEFT 0.21.0 / TRL 0.29.1 / Liger on native Windows.

Three adapters were evaluated on 96 validation examples. The third was selected before opening the 108-example test, with a critical-recall safety constraint and a predeclared ordering favoring action accuracy. The 10,000 published candidates were not all trained. The initial train/validation/test corpora and selected training snapshot remain frozen.

Evaluation: same prompt, same test

108 held-out synthetic SaaS cases; base and LoRA use the same prompt/settings, greedy non-thinking generation, max 700 new tokens, batch 8, both native stop tokens. No JSON repair or constrained decoding.

When invalid output fails all task fields, strict action accuracy is 24.1% → 86.1%. Field-only scores and strict scores answer different questions. A separate base + four frozen train-example control reaches 40.7% action accuracy with a different prompt; it is not a comparison to a stronger large model.

Content review is separate: a blinded AI-assisted audit of 36 paired test replies rates quality 2.78 → 4.31 out of 5 and flags unsupported fact/policy claims in 13/36 → 5/36 replies. The evaluator was an AI assistant, not independent human reviewers. Raw predictions, rubric ratings and limitations are included in the evaluation folder.

Additional prototype checks match expected fields plus contract in 8/10 model replies. Known failures include an incorrect denial at an inclusive refund boundary, a failed changed-threshold pair and an invented external refund status. These are validation-derived checks, not a fresh unseen benchmark.

Limitations

• Small synthetic suite, mostly templated Russian. Training histories are empty; the application accepts history, but robust multi-turn behavior is not established.
• Test company identifiers and authored families differ from train, but semantic templates can overlap. Arbitrary-company generalization is not proven.
• Only three emotional variants of one critical test scenario. Critical recall 3/3 is not a broad safety guarantee.
• No independent human annotation or evaluation; no production customer data, strong-large-model comparison or measured operator-time savings.
• Base and LoRA were benchmarked in a shared desktop session. Packet/amortized times are not request latency or isolated speed measurements.
• 5/36 reviewed LoRA replies still contain false claims. Use as an operator assistant; do not enable autonomous financial or account actions.
• No RAG, live company connectors, verified confidence score or full prompt-injection defense is included.

License and provenance

Adapter and authored code: Apache-2.0, author A11Sunday, 2026. Base Qwen license and font notices are retained. Synthetic dataset: CC BY 4.0, separately published. The repository contains the adapter and application source; base weights are downloaded from the pinned upstream revision.

Source app · Capstone requirement audit · Content error analysis · Presentation