yiyuliu/voice-command-intent-qwen0.5b

TINYLM Voice Command Intent Classifier (Qwen 0.5B, Q3_K_M)

Fine-tuned Qwen2.5-0.5B-Instruct for routing student speech during live assignment defense oral assessments. Runs on CPU via llama-cpp-python — no GPU required.

Intents

Intent	Platform action
answer	Do nothing — student is answering (default)
navigate_back	Jump to target_question_id (e.g. q2)
continue	Resume forward after failed recall

Model details

Base model	Qwen/Qwen2.5-0.5B-Instruct
Fine-tuning	LoRA SFT (rank 8, ~4.4M trainable params)
Quantization	Q3_K_M
File size	~339 MB
RAM at inference	~410–470 MB (n_ctx=1024)
Eval token accuracy	99.2%

Files in this repository

File	Description
voice-command-intent-qwen0.5b-Q3_K_M.gguf	Production weights (ship this)
inference/	Python wrapper (VoiceCommandClassifier, prompts, schema, guard)

Quick start

Download GGUF only

from huggingface_hub import hf_hub_download

gguf = hf_hub_download(
    repo_id="yiyuliu/voice-command-intent-qwen0.5b",
    filename="voice-command-intent-qwen0.5b-Q3_K_M.gguf",
)

Full integration (GGUF + inference code)

1. Clone this repo or copy voice-command-intent-qwen0.5b-Q3_K_M.gguf + inference/ into your project.
2. Install dependencies:

cd inference
pip install -e .

3. Classify an utterance:

from tinylm.inference import VoiceCommandClassifier
from tinylm.prompts import QuestionRecord

clf = VoiceCommandClassifier("voice-command-intent-qwen0.5b-Q3_K_M.gguf")

result = clf.classify(
    questions=[
        QuestionRecord("q1", "remember", "answered"),
        QuestionRecord("q2", "understand", "answered", focus="technical debt"),
        QuestionRecord("q3", "apply", "current"),
    ],
    current_question_id="q3",
    utterance="could I correct my previous question",
    subject="Computer Science",
)

print(result.to_action())
# {"action": "navigate", "target_question_id": "q2"}

Verify install

cd inference
python smoke_test.py

Runtime inputs

Required: student utterance (STT text), Bloom session position (q1–q6, status, optional focus term).

Not required: full lecturer question text or assignment content.

Questions follow Bloom's taxonomy: remember → understand → apply → analyze → evaluate → create.

Environment variables (optional)

Variable	Default
TINYLM_GGUF_PATH	auto-resolve GGUF in repo root
TINYLM_N_CTX	1024
TINYLM_MAX_TOKENS	64

License

• Fine-tuned weights: Apache 2.0 (same as base Qwen2.5)
• Inference code: MIT

Base model license: Qwen/Qwen2.5-0.5B-Instruct